Research Article A Novel Variable Index and Excision CFAR Based Ship Detection...

Research ArticleA Novel Variable Index and Excision CFAR Based ShipDetection Method on SAR Imagery

Kefeng Ji Xiangwei Xing Huanxin Zou and Jixiang Sun

College of Electronic Science and Engineering National University of Defense Technology Changsha Hunan 410073 China

Correspondence should be addressed to Kefeng Ji jikefengnudteducn

Received 10 December 2014 Revised 3 March 2015 Accepted 3 March 2015

Academic Editor Ignacio R Matias

Copyright copy 2015 Kefeng Ji et alThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

When applying the constant false alarm rate (CFAR) detector to ship detection on synthetic aperture radar (SAR) imagery multipleinterferers such as upwelling breaking waves ambiguities and neighboring ships in a dense traffic area will degrade the probabilityof detection In this paper we propose a novel variable index and excision CFAR (VIE-CFAR) based ship detection method toalleviate the masking effect of multiple interferers Firstly we improve the variable index (VI) CFAR with an excision procedurewhich censors the multiple interferers from the reference cells And then the paper integrates the novel CFAR concept into a shipdetection scheme on SAR imagery which adopts the VIE-CFAR to screen reference cells and the distribution to derive detectionthreshold Finally we analyze the performances of the VIE-CFAR under different environments and validate the proposed methodon both ENVISAT and TerraSAR-X SAR data The results demonstrate that the proposed method outperforms other existingdetectors especially in the presence of multiple interferers

1 Introduction

Ship detection filters the peaks among sea clutter to indicatethe location of potential ships With the development ofremote sensing technology ship detection on remote sensingimagery becomes more and more attractive Among oth-ers synthetic aperture radar (SAR) sensors are particularlyappealing due to the fact that they are independent ofatmospheric and meteorological conditions [1 2] Thereforeship detection on SAR imagery has received a lot of interestsin the past decades [3ndash6] To the present date the EuropeanCommission and European Space Agency have jointly initi-ated series of projects [7] such as IMPAST DECLIMS andLIMES to develop efficient and reliablemaritime surveillancesystems in which ship detection on SAR imagery is one of themost critical modules

Besides the methods based on wavelet analysis [8]and subaperture correlation [9] constant false alarm rate(CFAR) method is the most popular technique for shipdetection on SAR imagery [3] The CFAR scheme sets thedetection threshold adaptively based on the local statisticwhich is estimated by processing a group of reference cellssurrounding the cell under test (CUT) In a homogeneous

environment when the reference cells contain independentand identically distributed samples governed by an expo-nential distribution it has been shown in [10] that the cellaveraging (CA) CFAR can achieve the near optimum perfor-mance However the CA-CFAR detector will be deterioratedunder nonhomogeneous environments for example clutteredges and multiple interferers [11ndash13] The nonhomogeneousenvironments result in either an excessive increase in falsealarms or degradation of detection probability To overcomethese problems alternative CFAR processors are proposedThe great-of (GO) smallest-of (SO) order statistic (OS)and trimmed mean (TM) CFARs are the most representativeones These CFAR detectors produce diverse performancesunder different clutter environments [11] To exploit theadvantages of different CFAR detectors for accommodatingthe variety of environments an intelligent CFAR conceptbased on data variability index (VI) and mean ratio (MR)of the leading and lagging windows named VI-CFAR isproposed in [14] which assesses the current environmentand switches among the CA-CFAR GO-CFAR and SO-CFAR dynamically The VI-CFAR provides low CFAR lossin homogeneous environments and performs robustly inthe presence of clutter edges and properly located multiple

Hindawi Publishing CorporationJournal of SensorsVolume 2015 Article ID 437083 10 pageshttpdxdoiorg1011552015437083

2 Journal of Sensors

VIAA BCUT

VIBWindow selection Excision COM

Target

No target

T

ΣA ΣB

120582 and ΣAB

ΣE

C = CN2 CN CE Σ = ΣA ΣB ΣAB ΣE

Figure 1 Illustration of the VIE-CFAR scheme

interferers However if the interferers are located in bothsides of the reference window the performance of VI-CFARdegrades with the increasing number of interferers

Practical ship detection on SAR imagery often encountersinterferers such as upwelling breaking waves ambiguitiesand neighboring ships in a dense traffic area On theseoccasions when applying the CFAR scheme to ship detectionon SAR imagery the interferers are likely to produce a higherdetection threshold corresponding to the masking effect inthe detection issue To overcome the aforementioned prob-lems we propose a novel variability index and excision CFAR(VIE-CFAR) based ship detection method on SAR imageryThe proposed method firstly incorporates the VI-CFAR withan excision procedure to filter the reference cells And thenwe derive the detection threshold based on the survivedreference cells and1198660 distribution [15]The proposedmethodaddresses the ship detection on SAR imagery in the presenceof multiple interferers and inherits the robust performance ofVI-CFAR in various environments We analyze the proposedmethod under different environments and validate its perfor-mances on both ENVISAT and TerraSAR-X SAR data

The rest of this paper is organized as follows Section 2presents our proposed VIE-CFAR scheme in detail InSection 3 we integrate the VIE-CFAR into a ship detectionflowchart In Section 4 simulation experiments are per-formed in various environments Section 5 conducts thevalidation of the VIE-CFAR for ship detection on ENVISATand TerraSAR-X SAR imagery Finally Section 6 concludesthis paper

2 Description of the VIE-CFAR Scheme

In this paper we present the VIE-CFAR scheme in Figure 1which incorporates the VI-CFARwith an excision procedureThroughout this paper we use the similar notations as in [14]In the VIE-CFAR scheme we check whether a target existsfor each CUT by comparing the CUT 119884 with a threshold119879 If 119884 gt 119879 then a target can be declared to be presentHere the threshold 119879 is decided by the estimated statistic ofthe reference cells 119883

119894| 119894 = 1 2 119873 and a multiplier

constant 119862 In most cases the statistic is estimated as thesumof the reference cells Furthermore in the proposedVIE-CFAR scheme the reference window can be selected as thewholewindow119860119861 the leadingwindow119860 the laggingwindow119861 or the excision window 119864 (results from the whole window119860119861 and an excision parameter 120582) based on the data VI andMR of the leading and lagging windows Correspondingly

the CFAR processor switches among CA-CFAR GO-CFARSO-CFAR and excision-CFAR (E-CFAR) dynamically

21 Definition of VI and MR We adopt the definition of theVI and MR detailed in [14] VI is a second-order statistic andits value is a function of the estimated populationmean andvariance 2 Specifically the VIE-CFAR calculates the VI ofthe leading and lagging window respectively as

VI = 1 + 2

2= 1 +

1

119899 minus 1

119899

sum

119894=1

(119883119894minus 119883)2

(119883)2

(1)

where119883 is the arithmetic mean of the 119899 = 1198732 cells in a halfreference window

In a homogeneous environment the probability densityfunction (PDF) of VI is independent of the noise powerHowever the value of VI changes considerably when clutteredges and interferers are presentTherefore we need to decideif the environment of the half reference window is variable ornonvariable This is done by comparing VI with a threshold119870VI via applying the following hypothesis [14]

VI le 119870VI 997904rArr Non-variable

VI ge 119870VI 997904rArr Variable(2)

TheMR is further defined to determine whether the cellsin the leading and lagging windows come from the sameenvironment or not It is defined as the ratio of the meanvalues of the leading and the lagging window cells denotedby [14]

MR =119883119860

119883119861

=sum119894isin119860

119883119894

sum119894isin119861

119883119894

(3)

in which 119883119860and 119883

119861are the leading and lagging window

means Similar to VI the PDF of MR is independent of thenoise power in a homogeneous environment However thevalue of MR increases when interfering targets and higherpower of clutters are present in the leading window while itdecreases when they are present in the lagging window

To determine whether the means of the leading andlagging windows are the same or not we use the followinghypothesis test by comparing the MR with a threshold 119870MRand its reciprocal [14]

119870minus1

MR le MR le 119870MR 997904rArr Same Means

MR lt 119870minus1

MR or MR gt 119870MR 997904rArr Different Means(4)

22 Window Selection Strategy and Excision Procedure Inthis section we present the window selection strategy of theVIE-CFAR scheme In contrast to the VI-CFAR that selectsdifferent reference windows to estimate the backgroundstatistic based on the VI and MR [14] we improve it byintroducing an excision procedure as shown in Table 1In the case of multiple interfering targets especially whenthe interferers are present in both the leading and lagging

Journal of Sensors 3

Table 1 Multiplier and window selection

Leading windowvariable

Lagging windowvariable Different means VIE-CFAR adaptive

thresholdsEquivalent CFAR

detectorNo No No 119862

119873Σ119860119861

CA-CFARNo No Yes 119862

1198732max (Σ

119860 Σ119861) GO-CFAR

Yes No mdash 1198621198732Σ119861

CA-CFARNo Yes mdash 119862

1198732Σ119860

CA-CFARYes Yes mdash 119862

119864Σ119864

E-CFAR

SAR

imag

es

Prep

roce

ssin

g

Slid

ing

win

dow

VIE-CFAR schemeLeading

Whole window

Excision window

Win

dow

sele

ctio

n

CFAR threshold

Para

met

er es

timat

ion

Thre

shol

d de

rivat

ion

G0

All thepixels are

processedYes

No

Mor

phol

ogic

alpr

oces

sing

Det

ectio

n re

sults

data

window

Laggingwindow

Pfa

Figure 2 Block diagram of the proposed method

windows the VI-CFAR chooses the half window with thesmallest mean and thus results in a loss in detection proba-bility To avoid this drawback we propose to use an excisionprocedure [16] for suppressing the masking effect caused bythe interfering targets The introduced excision procedure isreferred to as E-CFARThis method compares the samples inthe whole window119860119861 with an excision threshold and groupsthe samples that do not exceed the excision threshold into anexcision window denoted by 119864 with the size of 119873

119864 We can

explicitly define the excision window as

119864 = 119883119894| 119883119894le 120582 sum

119894isin119860119861

119883119894 (5)

where 120582 is the excision coefficientHere the value of 120582 is set based on the sumof the reference

cells and an excision probability to ensure the survivedsamples in window 119864 are not variable More specifically weselect the value of 120582 based on the following steps

Firstly we set a relative low excision probability forexample 1119890 minus 6 and accordingly calculate an initial excisioncoefficient 120582(0) (similar to (7) below)

120582(119894)= (119875fa)

minus1119873(119894)

119864 minus 1 119894 = 0 1 2 119872 (6)

in which 119894 denotes the 119894th iteration and 119873(119894)

119864denotes the

number of the survived cells of the 119894th iteration and119873(0)119864

= 119873Secondly we screen the reference cells based on (5) to

obtain the excisionwindow119864 and calculate VI of the survivedreference cells

Thirdly we compare the value ofVIwith119870VI to determinethe variability of 119864 If the window 119864 is variable we increasethe excision probability with a step of 5 times of the initial

value (eg if the initial value is 1119890 minus 6 the step is 5119890 minus 6) andrecalculate a lower 120582

And the second and third steps repeat until the referencecells are not variable With the iteratively calculated 120582 theinterferers can be removed and accordingly the survivedreference cells are in nonvariable environment

Besides the selected subset of reference cells to estimatethe background statistic the VIE-CFAR also determines amultiplier constant to form the adaptive detection thresholdUnder the assumption that the background cells are governedby an exponential distribution we determine the multiplierconstant 119862

119873based on CA-CFAR with reference cells [14]

119862119873= (119875fa)

minus1119873minus 1 (7)

where 119875fa is the predetermined probability of false alarm(PFA) It is worth noting that the number of reference cellsin window 119864 is 119873

119864and the multiplier 119862

119864is calculated from

(6) by replacing119873 with119873119864

3 VIE-CFAR for Ship Detection in SAR Images

Based on theVIE-CFAR scheme described above we proposea VIE-CFAR based ship detection method on SAR imageryFigure 2 illustrates the block diagram of the proposedmethod When applying the VIE-CFAR to ship detectionon SAR imagery we further improve the procedures in thefollowing aspects Firstly we extend the 1D reference windowto a 2D hollow stencil and obtain the reference cells basedon the proposed VIE-CFAR scheme Secondly we adopt the1198660 distribution to describe the statistical characteristic of sea

clutter Thirdly the method estimates the parameters of 1198660distribution and calculates the decision threshold to filterships from sea clutter Finally the CFAR detection results are


Reference cells

Guard cells

CUT

(a)

Lead

ing

win

dow

Lagg

ing

win

dow

(b) (c) (d)

Figure 3 Illustration of the 2D stencil (a) the three-layer sliding window (b) leading and lagging window and (c) and (d) variations of thesliding window

postprocessed with morphological operations to cluster andfilter isolated pixels

As the SAR sensor captures the observed area fromboth range and azimuth directions the VIE-CFAR referencewindow for ship detection on SAR image is extended toa two-dimensional hollow stencil as shown in Figure 3(a)The stencil consists of three layers [3] where the centralone concentrates on the pixels under test and the guardwindow protects the target from leaking to the backgroundwindowThe stencil size depends on the image resolution andship target dimensionsThe leading and lagging windows areillustrated in Figure 3(b) Notice that the leading and laggingwindow can be defined as other forms to be more robust toclutter edges as shown in Figures 3(c) and 3(d) Due to thefact that the wave fields present various directions on SARimages (not only limited to horizontal or vertical) the stencilwindow should be designed flexibly to remain consistent withthe wave field direction

The adopted statistical model is another important factorthat impacts ship detection performance on SAR imageryConducted from themultiplicativemodel and the underlyingphysical scattering mechanisms the 119866

0 distribution hasarisen to represent clutters on SAR imagery [15] The PDF ofa random variable119883 that has an amplitude 1198660 distribution isdenoted as

119891119883 (119909) =

2119871119871Γ (119871 minus 120572) 119909

2119871minus1

120574120572Γ (119871) Γ (minus120572) (120574 + 1198711199092)119871minus120572

minus120572 120574 119871 119909 gt 0

(8)

where 119871 denotes the equivalent number of looks (ENL) ofthe amplitude image 120572 is a shape parameter that describesthe spikiness of the clutter 120574 is a scale parameter and Γ(sdot)is the gamma function The 119866

0 distribution is compati-ble with several special cases when the shape parameteradopts different values The exponential distribution is oneof the special cases of 1198660 distribution [17] The parametersare estimated from sea clutters by Mellin kind statistics[18]

Different from the one-dimensional CFAR detection the2D stencil ensures enough reference cells for the estimationof the background statisticsTherefore we estimate the shapeand scale parameters with different reference cells basedon the switch logic in Table 1 And then we directly derive

the detection threshold119879119889for ship detection on SAR imagery

by (8)

119875fa = intinfin

119879119889

119891119883 (119909) 119889119909 = int

infin

119879119889

2119871119871Γ (119871 minus 120572) 119909

2119871minus1

120574120572Γ (119871) Γ (minus120572) (120574 + 1198711199092)119871minus120572

119889119909

(9)

When 119871 = 1 close form of (8) can be derived

119875fa =120572 minus 1

120572120574120572(120574 + 119879

2

119889)120572

(10)

therefore 119879119889is presented as

119879119889= (

120572120574120572

120572 minus 11198751120572

fa minus 120574)

12

(11)

However analytical expression of (8) is difficult to besolved formost cases Once given the PFAwe derive the deci-sion threshold 119879

119889based on (8) with numerical calculations

Ship target is declared to be present if the image pixel valueexceeds the decision threshold

4 Simulation Results and Analysis

In this section we illustrate the performances of VIE-CFARfor the environments of homogeneous clutter edges andmultiple interferers We compare the probability of detection(PD) and the PFA performances of the VIE-CFAR with theexisting CFAR detectors including the CA GO SO OS (119896 =21 119896 is the order of the cell being taken as the estimation oflocal statistic) and VI-CFAR

Due to the lack of analytical expression for the PDF ofVI and the dependence between the VI and MR hypothesistest decisions [14] we apply the Monte-Carlo simulation(119872 = 1 000 000) In these simulations we mainly expect todemonstrate the ability of selecting homogeneous clutter cellsby the VIE-CFAR scheme Therefore we generally assumethe exponential distribution instead of the 1198660 distributionfor background cells and Swerling I fluctuation for the targetand interferers The interferer to noise ratio (INR) equalsthe signal to noise ratio (SNR) The interferers are generatedrandomly and inserted to the appointed cells The clutteredge sweeps from left to right (Window 119860 to Window 119861)with clutter to noise ratio (CNR) as 10 dB The multiplierconstants are calculated from (6) given that the number of


reference cells is 119873 = 24 and the designed 119875fa is 1119890 minus 4 Asdescribed in [14] the values of VI and MR are related to theCNR and INR of the variable environments as well as thenumber of reference cellsWe here set them as 476 and 1806respectively The excision coefficient 120582 is calculated throughthe iteration procedure in Section 22 with an initial excisionprobability of 1119890 minus 6

Figure 4 illustrates the various environments of thesimulation including the homogeneous clutter edges andmultiple interferers Note that the clutter and interferer cellsare set to be stable values in Figure 4 It only illustrates therelative position of the clutter edge and interferers

41 Homogeneous Environment Figure 5 shows the PD per-formance in a homogeneous environment for the VIE-CFARalong with results for an optimum detector (with a fixeddetection threshold when the clutter power is known) and forthe CA-CFAR GO-CFAR SO-CFAR OS-CFAR (119896 = 21)and VI-CFAR All of the CFAR detectors perform similarlyand exhibit some CFAR loss relative to the optimumdetectorThe VIE-CFAR performs the same as VI-CFAR and behavessomewhat better than the SO- and OS-CFAR

42 Variable Environment with Clutter Edge In Figure 6 weevaluate the PFA performance of the proposed VIE-CFARunder the condition of clutter edges (as shown in Figures4(b) and 4(c)) Due to the adaptive selection procedureboth the VI-CFAR and VIE-CFAR behave steadily with thedesired PFA Particularly when the number of clutter cellsis more than 1198732 and the cell under test is from the clutterbackground the PFA achieved by the VIE-CFAR and VI-CFAR is superior to the other CFAR processors

43 Variable Environment with Multiple Interferers InFigure 7 we assess the performance of the VIE-CFAR in thepresence of a single interfering target that locates in one halfof the reference windowThe interferer is inserted in the 18thcell (as shown in Figure 4(d)) In this case the performancesof OS-CFAR SO-CFAR VI-CFAR and VIE-CFAR arerelatively unaffected while the performances of CA-CFARandGO-CFAR degrade heavilyThe degradation results fromthe fact that the interferer increases the estimated statisticsand the decision thresholds of the CA-CFAR and GO-CFARWe also observe that the VIE-CFAR and VI-CFAR performnearly the same This result is expected since they switch tothe same reference window in this situationThe small CFARloss in VI-CFAR and VIE-CFAR for low SNR is a resultof errors in the window selection hypothesis As the SNRincreases the VIE-CFAR selects the right reference cells andthe performance approaches to the SO-CFAR and OS-CFAR

In Figure 8 we present multiple interferers that locatein both halves of the reference window In the case oftwo interfering targets (inserted in the 5th and 20th cellsas shown in Figure 4(e)) the VIE-CFAR and OS-CFARretain good performances while the other detectors showperformance degradation The performance degradation ofVI-CFAR validates the necessity of introducing the excisionprocedure when the interfering targets are present in bothhalves of the reference window When the SNR is low

the VIE-CFAR switches not always to the E-CFAR andthe performance is a bit lower than the OS-CFAR As theSNR increases the VIE-CFAR behaves similarly to the OS-CFAR As the number of interfering targets increases to 4 andlocates in both halves of the reference window (the additionaltwo interferers locate at the 7th and 18th cells as shown inFigure 4(f)) the performance of the OS-CFAR shows largedegradation The reason lies in that the value of 119896 in theOS-CFAR is not appropriate to tolerate enough interferingtargets The performance of VIE-CFAR maintains a stablelevel and is robust to the number of interferers

Finally we evaluate the PFA performance of the proposedVIE-CFAR in the presence of multiple interferers FromFigure 9(a) the proposed method controls the PFA withinthe same order of the designed PFA This is because theVIE-CFAR scheme removes the multiple interferers by anexcision procedure and the resulting reference cells are underhomogeneous environments Figure 9(b) illustrates the vari-able index of the resulting reference cells The variable indexis lower than 119870VI and accordingly the detector performs asCA-CFAR with the resulting homogeneous reference cells

5 Validation for Ship Detection onSAR Imagery

Most of the operating ship detection systems such as thesearch for unidentified maritime objects (SUMO) and oceanmonitoring workstation (OMW) are developed from theCA-CFAR detector [3]Therefore we mainly conduct perfor-mance comparisons of the proposed method with the CA-CFAR and VI-CFAR detectors

The proposed VIE-CFAR based ship detection methodis validated on two frames of SAR imagery The first oneis acquired by ENVISAT on wide swath mode centered at3783∘N and 11991∘E (Yellow Sea China) with a resolution

of 125m times 125m in the range and azimuth directionsrespectively The image size is 193 times 228 pixels and contains39 visually inspected ships as shown in Figure 10(a) The seaclutter environment presents clutter edges The other one isacquired by TerraSAR-X over the area of Hong Kong with aresolution of 19mtimes33min the range and azimuth directionrespectively The image size is 739 times 887 pixels and contains41 visually inspected ships as shown in Figure 11(a)The shipsin this image are larger in pixels due to the higher resolutionand some of the ships are located closely Both the ENVISATand TerraSAR-X SAR images are level 2 products

The test guard and background windows of the CFARsliding stencil are set as 1 by 1 7 by 7 and 13 by 13 for theENVISAT image and 1 by 1 15 by 15 and 21 by 21 for theTerraSAR-X image respectively The designed 119875fa is set as00001 In the real SAR imagery the INR and number of refer-ence cells are larger than those in the simulations Thereforewe set the 119870VI and 119870MR as 25 and 1806 empirically Theexcision factor 120582 is determined by the iteration procedure

For the ENVISAT image detection results are illustratedin Figure 10 From Figures 10(b) 10(c) and 10(d) false alarmsare observed for the CA-CFAR VI-CFAR and VIE-CFARand the number of false alarms is 3 2 and 3 respectively Notethat the CFAR detection results are refined by morphological


ClutterTarget

(a)

ClutterTarget

(b)

ClutterTarget

(c)

Interferer

ClutterTarget

(d)

Interferer

ClutterTarget

(e)

Interferer

ClutterTarget

(f)

Figure 4 Illustration of the various simulated environments (a) Homogeneous environment (b) clutter edge on the leading window (c)clutter edge on the lagging window (d) one interferer on the lagging window (the 18th cell) (d) two interferers on both halves of the window(the 5th and 20th cells) and (f) four interferers on both halves of the window (the 5th 7th 18th and 20th cells)


5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility

OptimumCA-CFARGO-CFARSO-CFAR

OS(21)-CFARVI-CFARVIE-CFAR

Figure 5 PD comparison of VIE-CFAR optimum detector CA-CFAR GO-CFAR SO-CFAR OS-CFAR and VI-CFAR in homo-geneous environment

0 5 10 15 20Number of clutter cells

CA-CFARGO-CFARSO-CFAROS-CFAR

VI-CFARVIE-CFARDesired FAR

log(

pfa)

minus9

minus8

minus7

minus6

minus5

minus4

minus3

minus2

minus1

Figure 6 PFA comparison of VIE-CFAR CA-CFAR GO-CFARSO-CFAR OS-CFAR andVI-CFAR in the presence of clutter edges

proceduresThe number of false alarms produced by the pro-posed VIE-CFAR is consistent with the other two detectorsin the final detection results

In the TerraSAR-X SAR image the closely located shipsdegrade the performances of CA-CFAR and VI-CFAR asshown in Figures 11(b) and 11(c) Similar to the resultsof the ENVISAT image the CFAR detection results ofthe TerraSAR-X image are also refined by morphologicalprocedures For the CA-CFAR the ships number 6 7 8 26and 29 are misdetected For the VI-CFAR the ships number

5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility

CA-CFARGO-CFARSO-CFAR


Figure 7 PD comparison of VIE-CFAR CA- GO- SO- OS- andVI-CFAR for a single interfering target on one half of the referencewindows

5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility


OS-CFARVI-CFARVIE-CFAR

2 interferers

4 interferers

Figure 8 PD comparison of VIE-CFAR CA- GO- SO- OS-and VI-CFAR for multiple interfering targets on both halves of thereference windows

15 26 and 29 are misdetected and there are 6 additionalfalse alarms The VIE-CFAR also produces 5 false alarmshowever it alleviates the masking effect of the neighboringinterferers and achieves highest PD among the detectors asshown in Figure 11(d) In fact the false alarms in the VI-CFAR and VIE-CFAR detection results are produced by highlevel intensity of sea clutters These impulsive clutters causefalse alarms intrinsically Although the CA-CFAR producesfewer false alarms there are 5 ships misdetected which isunacceptable in practical marine surveillance applications


5 10 15 20 25 30SNR (dB)

Prob

abili

ty o

f fal

se al

arm

Designed PFA2 interferers4 interferers

10minus3

10minus4

10minus5

(a)

5 10 15 20 25 3015

2

25

3

35

4

45

5

SNR (dB)

Varia

ble i

ndex

2 interferers4 interferers

KVI

(b)

Figure 9 PFA performance of the proposed method with multiple interferers (a) PFA and (b) threshold of the variable index

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Figure 10 Detection results on ENVISAT image (a) SAR image of the study area with visually inspected ships marked by red square (b)CA-CFAR detection results (c) VI-CFAR detection results and (d) VIE-CFAR detection results (red square detected ships green ellipsefalse alarms Cyan Ellipse missing detection)


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3839

40

41

5

(d)Figure 11 Detection results on TerraSAR-X SAR image with visually inspected ships marked by red square (a) SAR image of the study area(b) CA-CFAR detection results (c)VI-CFAR detection results and (d) VIE-CFAR detection results (red square detected ships green ellipsefalse alarms Cyan Ellipse missing detection)

Table 2 Overview of the detection results obtained by different detectors

Detectors Number of ships Number of detected ships Number of false alarms Number of missed shipsCA-CFAR 80 75 3 5VI-CFAR 80 77 8 3VIE-CFAR 80 80 8 0

Finally we summarize the detection results on theENVISAT and TerraSAR-X SAR imagery in Table 2 Theresults confirm that the VIE-CFAR is superior to VI-CFARand CA-CFAR in terms of PDThe VIE-CFAR is more robustthan the other detectors in the environment of multipleinterferers

6 Conclusions

In this paper we have proposed a VIE-CFAR based shipdetection method on SAR imageryThe VIE-CFAR improvesthe concept of VI-CFAR detection by introducing an exci-sion procedure to alleviate the masking effect of multipleinterferers We have analyzed the performances of the VIE-CFAR detector under various environments and validatedthe proposed ship detection method on ENVISAT andTerraSAR-X SAR imagery We conclude from the validation

that the proposed VIE-CFAR is robust in the presence ofmultiple interferers compared to the VI-CFAR and CA-CFAR

With the validation on real SAR imagery the proposedVIE-CFAR behaves robustly in nonhomogeneous environ-ments especially in the presence ofmultiple interferers Someadditional remarks and discussions are given as follows

(1) The VIE-CFAR accommodates the variety of envi-ronments and switches among several basic CFARdetectors dynamically In paerticular we introducethe excision procedure to address the multiple inter-ferers that come from spiky sea clutters ambiguitiesand neighboring ships for ship detection on SARimagery Validation on ENVISAT and TerraSAR-Ximages illustrates the effectiveness of the proposedmethod


(2) The three-layer stencil is usually employed for shipdetection on SAR imagery to avoid the ship pixelsleaking to the clutter window If the size of the stencilis set properly it alleviates the masking effect of theship itself However it is not able to account forthe multiple interferers that we considered in thispaper The proposed VIE-CFAR based ship detectionmethod alleviates the masking effect of the ship itselfby employing the stencil and further addresses thechallenges that come from multiple interferers by theexcision procedure

(3) Due to the window switching strategy and the addi-tional excision procedure the proposed VIE-CFARconsumes more computation than the CA-CFARHowever the proposedmethod really produces betterresults in difficult scenarios Once the method isembedded in operating ship detection system fastalgorithm and strategy can be designed to compen-sate the additional computation complexity

(4) In the detection results several kinds of false alarmssuch as spiky sea clutters islands and other man-made objects are facilitating to appear They behavesimilarly in terms of image intensity with ships andare difficult to be removed by the CFAR method Infact polarimetric information has been introduced toanalyze the different scattering mechanisms of shipsand false alarms However it is out of the scope of thispaper and can be investigated in future work

(5) Ship surveillance with SAR images consists of sev-eral modules such as land masking ship detectionparameter derivation ship recognition and crossvalidation with auxiliary data We here focus onthe detection stage and evaluate the performance ofthe proposed VIE-CFAR scheme Future work willfocus on embedding the proposed VIE-CFAR into acomplete processing chain for ship surveillance withSAR imagery

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work is partially supported by the National NaturalScience Foundation of China under Grants 61372163 and61240058

References

[1] G Margarit J J Mallorqui J M Rius and J Sanz-MarcosldquoOn the usage of GRECOSAR an orbital polarimetric SARsimulator of complex targets to vessel classification studiesrdquoIEEE Transactions on Geoscience and Remote Sensing vol 44no 12 pp 3517ndash3525 2006

[2] A Moreira P Prats-Iraola M Younis G Krieger I Hajnsekand K P Papathanassiou ldquoA tutorial on synthetic aperture

radarrdquo IEEEGeoscience and Remote SensingMagazine vol 1 no1 pp 6ndash43 2013

[3] D Crisp ldquoThe State-of-the-Art in ship detection in syntheticaperture radar imageryrdquo Tech Rep 013-053 Austrilian DefenceScience and Technology Organisation 2004

[4] S Brusch S Lehner T Fritz M Soccorsi A Soloviev and BVan Schie ldquoShip surveillance with TerraSAR-Xrdquo IEEE Transac-tions on Geoscience and Remote Sensing vol 49 no 3 pp 1092ndash1103 2011

[5] JWei P Li J Yang J Zhang and F Lang ldquoA new automatic shipdetection method using L-band polarimetric SAR imageryrdquoIEEE Journal of Selected Topics in Applied Earth Observationsand Remote Sensing vol 7 no 4 pp 1383ndash1393 2014

[6] A Marino ldquoA notch filter for ship detection with polarimetricSAR datardquo IEEE Journal of Selected Topics in Applied EarthObservations and Remote Sensing vol 6 no 3 pp 1219ndash12322013

[7] G Margarit J A B Milanes and A Tabasco ldquoOperationalship monitoring system based on Synthetic Aperture Radarprocessingrdquo Remote Sensing vol 1 no 3 pp 375ndash392 2009

[8] M Tello C Lopez-Martinez J Mallorqui T Tares and HGreidanus ldquoAdvances in unsupervised ship detection withmultiscale techniquesrdquo IEEE Geoscience and Remote SensingLetters vol 2 no 2 pp IVndash979ndashIVndash982 2009

[9] S-I Hwang and K Ouchi ldquoOn a novel approach using MLCCand CFAR for the improvement of ship detection by syntheticaperture radarrdquo IEEE Geoscience and Remote Sensing Lettersvol 7 no 2 pp 391ndash395 2010

[10] H M Finn and R S Johnson ldquoAdaptive detection mode withthreshold control as a function of spatially sampled clutter levelestimatesrdquo RCA Review vol 29 pp 414ndash468 1968

[11] P P Gandhi and S A Kassam ldquoAnalysis of CFAR processorsin homogeneous backgroundrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 24 no 4 pp 427ndash445 1988

[12] G Gao L Liu L Zhao G Shi and G Kuang ldquoAn adaptive andfast CFAR algorithm based on automatic censoring for targetdetection in high-resolution SAR imagesrdquo IEEE Transactionson Geoscience and Remote Sensing vol 47 no 6 pp 1685ndash16972009

[13] R Zhang Y ZouW Sheng XMa andHWang ldquoAn improvedCFAR detector for non-homogeneous clutter environmentrdquo inProceedings of the International Symposium on Signals Systemsand Electronics (ISSSE rsquo10) vol 2 pp 1ndash4 September 2010

[14] M E Smith and P K Varshney ldquoIntelligent CFAR processorbased on data variabilityrdquo IEEE Transactions on Aerospace andElectronic Systems vol 36 no 3 pp 837ndash847 2000

[15] A C Frery H-J Muller C D C F Yanasse and S J SSantrsquoAnna ldquoAmodel for extremely heterogeneous clutterrdquo IEEETransactions on Geoscience and Remote Sensing vol 35 no 3pp 648ndash659 1997

[16] H Goldman and I Bar-David ldquoAnalysis and application of theexcision CFAR detectorrdquo IEE Proceedings F CommunicationsRadar and Signal Processing vol 135 no 6 pp 563ndash575 1988

[17] G Gao ldquoStatistical modeling of SAR images a surveyrdquo Sensorsvol 10 no 1 pp 775ndash795 2010

[18] R Abdelfattah and J-M Nicolas ldquoInterferometric SAR coher-ence magnitude estimation using second kind statisticsrdquo IEEETransactions on Geoscience and Remote Sensing vol 44 no 7pp 1942ndash1953 2006

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



VIAA BCUT

VIBWindow selection Excision COM

Target

No target

T

ΣA ΣB

120582 and ΣAB

ΣE

C = CN2 CN CE Σ = ΣA ΣB ΣAB ΣE

Figure 1 Illustration of the VIE-CFAR scheme

interferers However if the interferers are located in bothsides of the reference window the performance of VI-CFARdegrades with the increasing number of interferers

Practical ship detection on SAR imagery often encountersinterferers such as upwelling breaking waves ambiguitiesand neighboring ships in a dense traffic area On theseoccasions when applying the CFAR scheme to ship detectionon SAR imagery the interferers are likely to produce a higherdetection threshold corresponding to the masking effect inthe detection issue To overcome the aforementioned prob-lems we propose a novel variability index and excision CFAR(VIE-CFAR) based ship detection method on SAR imageryThe proposed method firstly incorporates the VI-CFAR withan excision procedure to filter the reference cells And thenwe derive the detection threshold based on the survivedreference cells and1198660 distribution [15]The proposedmethodaddresses the ship detection on SAR imagery in the presenceof multiple interferers and inherits the robust performance ofVI-CFAR in various environments We analyze the proposedmethod under different environments and validate its perfor-mances on both ENVISAT and TerraSAR-X SAR data

The rest of this paper is organized as follows Section 2presents our proposed VIE-CFAR scheme in detail InSection 3 we integrate the VIE-CFAR into a ship detectionflowchart In Section 4 simulation experiments are per-formed in various environments Section 5 conducts thevalidation of the VIE-CFAR for ship detection on ENVISATand TerraSAR-X SAR imagery Finally Section 6 concludesthis paper

2 Description of the VIE-CFAR Scheme

In this paper we present the VIE-CFAR scheme in Figure 1which incorporates the VI-CFARwith an excision procedureThroughout this paper we use the similar notations as in [14]In the VIE-CFAR scheme we check whether a target existsfor each CUT by comparing the CUT 119884 with a threshold119879 If 119884 gt 119879 then a target can be declared to be presentHere the threshold 119879 is decided by the estimated statistic ofthe reference cells 119883

119894| 119894 = 1 2 119873 and a multiplier

constant 119862 In most cases the statistic is estimated as thesumof the reference cells Furthermore in the proposedVIE-CFAR scheme the reference window can be selected as thewholewindow119860119861 the leadingwindow119860 the laggingwindow119861 or the excision window 119864 (results from the whole window119860119861 and an excision parameter 120582) based on the data VI andMR of the leading and lagging windows Correspondingly

the CFAR processor switches among CA-CFAR GO-CFARSO-CFAR and excision-CFAR (E-CFAR) dynamically

21 Definition of VI and MR We adopt the definition of theVI and MR detailed in [14] VI is a second-order statistic andits value is a function of the estimated populationmean andvariance 2 Specifically the VIE-CFAR calculates the VI ofthe leading and lagging window respectively as

VI = 1 + 2

2= 1 +

1

119899 minus 1

119899

sum

119894=1

(119883119894minus 119883)2

(119883)2

(1)

where119883 is the arithmetic mean of the 119899 = 1198732 cells in a halfreference window

In a homogeneous environment the probability densityfunction (PDF) of VI is independent of the noise powerHowever the value of VI changes considerably when clutteredges and interferers are presentTherefore we need to decideif the environment of the half reference window is variable ornonvariable This is done by comparing VI with a threshold119870VI via applying the following hypothesis [14]

VI le 119870VI 997904rArr Non-variable

VI ge 119870VI 997904rArr Variable(2)

TheMR is further defined to determine whether the cellsin the leading and lagging windows come from the sameenvironment or not It is defined as the ratio of the meanvalues of the leading and the lagging window cells denotedby [14]

MR =119883119860

119883119861

=sum119894isin119860

119883119894

sum119894isin119861

119883119894

(3)

in which 119883119860and 119883

119861are the leading and lagging window

means Similar to VI the PDF of MR is independent of thenoise power in a homogeneous environment However thevalue of MR increases when interfering targets and higherpower of clutters are present in the leading window while itdecreases when they are present in the lagging window

To determine whether the means of the leading andlagging windows are the same or not we use the followinghypothesis test by comparing the MR with a threshold 119870MRand its reciprocal [14]

119870minus1

MR le MR le 119870MR 997904rArr Same Means

MR lt 119870minus1

MR or MR gt 119870MR 997904rArr Different Means(4)

22 Window Selection Strategy and Excision Procedure Inthis section we present the window selection strategy of theVIE-CFAR scheme In contrast to the VI-CFAR that selectsdifferent reference windows to estimate the backgroundstatistic based on the VI and MR [14] we improve it byintroducing an excision procedure as shown in Table 1In the case of multiple interfering targets especially whenthe interferers are present in both the leading and lagging







119873Σ119860119861


1198732max (Σ

119860 Σ119861) GO-CFAR

Yes No mdash 1198621198732Σ119861


1198732Σ119860


119864Σ119864

E-CFAR

SAR

imag

es

Prep

roce

ssin

g

Slid

ing

win

dow


Whole window

Excision window

Win

dow

sele

ctio

n

CFAR threshold

Para

met

er es

timat

ion

Thre

shol

d de

rivat

ion

G0

All thepixels are

processedYes

No

Mor

phol

ogic

alpr

oces

sing

Det

ectio

n re

sults

data

window

Laggingwindow

Pfa



119864 We can


119864 = 119883119894| 119883119894le 120582 sum

119894isin119860119861

119883119894 (5)




120582(119894)= (119875fa)

minus1119873(119894)

119864 minus 1 119894 = 0 1 2 119872 (6)


119864denotes the









119862119873= (119875fa)










Reference cells

Guard cells

CUT

(a)

Lead

ing

win

dow

Lagg

ing

win

dow

(b) (c) (d)






119891119883 (119909) =

2119871119871Γ (119871 minus 120572) 119909

2119871minus1

120574120572Γ (119871) Γ (minus120572) (120574 + 1198711199092)119871minus120572

minus120572 120574 119871 119909 gt 0

(8)





by (8)

119875fa = intinfin

119879119889

119891119883 (119909) 119889119909 = int

infin

119879119889

2119871119871Γ (119871 minus 120572) 119909

2119871minus1

120574120572Γ (119871) Γ (minus120572) (120574 + 1198711199092)119871minus120572

119889119909

(9)


119875fa =120572 minus 1

120572120574120572(120574 + 119879

2

119889)120572

(10)


119879119889= (

120572120574120572

120572 minus 11198751120572

fa minus 120574)

12

(11)























ClutterTarget

(a)

ClutterTarget

(b)

ClutterTarget

(c)

Interferer

ClutterTarget

(d)

Interferer

ClutterTarget

(e)

Interferer

ClutterTarget

(f)



5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility







log(

pfa)

minus9

minus8

minus7

minus6

minus5

minus4

minus3

minus2

minus1




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility



2 interferers

4 interferers




5 10 15 20 25 30SNR (dB)

Prob

abili

ty o

f fal

se al

arm


10minus3

10minus4

10minus5

(a)

5 10 15 20 25 3015

2

25

3

35

4

45

5

SNR (dB)

Varia

ble i

ndex


KVI

(b)


1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

(a)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

12

3

(b)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

2122

23

2425

2627

28

29

30

3132

33

34

353637

3839

12

(c)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

2425

2627

28

29

30

3132

33

34

353637

3839

12 3

(d)



1

2

3

4

5

67

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

3031

32

33

34

35

36

37 3839

40

41

(a)

1

2

3

4

5

67

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

30

31

32 33

34

35

36

12

3

45

(b)

1

2

3

1

4

2

5

67

389

4

10

11

12

13

14

5

15

16

17

18

19

20

21

22

23

24

25

26

2728

29

30

31

3233

34 3536

37

38

6

1

2 3

(c)

1

2

3

1

4

5

267

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

3

29

3031

32

33

34

35

36

374

3839

40

41

5





6 Conclusions












Acknowledgment


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















119873Σ119860119861


1198732max (Σ

119860 Σ119861) GO-CFAR

Yes No mdash 1198621198732Σ119861


1198732Σ119860


119864Σ119864

E-CFAR

SAR

imag

es

Prep

roce

ssin

g

Slid

ing

win

dow


Whole window

Excision window

Win

dow

sele

ctio

n

CFAR threshold

Para

met

er es

timat

ion

Thre

shol

d de

rivat

ion

G0

All thepixels are

processedYes

No

Mor

phol

ogic

alpr

oces

sing

Det

ectio

n re

sults

data

window

Laggingwindow

Pfa



119864 We can


119864 = 119883119894| 119883119894le 120582 sum

119894isin119860119861

119883119894 (5)




120582(119894)= (119875fa)

minus1119873(119894)

119864 minus 1 119894 = 0 1 2 119872 (6)


119864denotes the









119862119873= (119875fa)










Reference cells

Guard cells

CUT

(a)

Lead

ing

win

dow

Lagg

ing

win

dow

(b) (c) (d)






119891119883 (119909) =

2119871119871Γ (119871 minus 120572) 119909

2119871minus1

120574120572Γ (119871) Γ (minus120572) (120574 + 1198711199092)119871minus120572

minus120572 120574 119871 119909 gt 0

(8)





by (8)

119875fa = intinfin

119879119889

119891119883 (119909) 119889119909 = int

infin

119879119889

2119871119871Γ (119871 minus 120572) 119909

2119871minus1

120574120572Γ (119871) Γ (minus120572) (120574 + 1198711199092)119871minus120572

119889119909

(9)


119875fa =120572 minus 1

120572120574120572(120574 + 119879

2

119889)120572

(10)


119879119889= (

120572120574120572

120572 minus 11198751120572

fa minus 120574)

12

(11)























ClutterTarget

(a)

ClutterTarget

(b)

ClutterTarget

(c)

Interferer

ClutterTarget

(d)

Interferer

ClutterTarget

(e)

Interferer

ClutterTarget

(f)



5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility







log(

pfa)

minus9

minus8

minus7

minus6

minus5

minus4

minus3

minus2

minus1




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility



2 interferers

4 interferers




5 10 15 20 25 30SNR (dB)

Prob

abili

ty o

f fal

se al

arm


10minus3

10minus4

10minus5

(a)

5 10 15 20 25 3015

2

25

3

35

4

45

5

SNR (dB)

Varia

ble i

ndex


KVI

(b)


1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

(a)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

12

3

(b)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

2122

23

2425

2627

28

29

30

3132

33

34

353637

3839

12

(c)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

2425

2627

28

29

30

3132

33

34

353637

3839

12 3

(d)



1

2

3

4

5

67

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

3031

32

33

34

35

36

37 3839

40

41

(a)

1

2

3

4

5

67

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

30

31

32 33

34

35

36

12

3

45

(b)

1

2

3

1

4

2

5

67

389

4

10

11

12

13

14

5

15

16

17

18

19

20

21

22

23

24

25

26

2728

29

30

31

3233

34 3536

37

38

6

1

2 3

(c)

1

2

3

1

4

5

267

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

3

29

3031

32

33

34

35

36

374

3839

40

41

5





6 Conclusions












Acknowledgment


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Reference cells

Guard cells

CUT

(a)

Lead

ing

win

dow

Lagg

ing

win

dow

(b) (c) (d)






119891119883 (119909) =

2119871119871Γ (119871 minus 120572) 119909

2119871minus1

120574120572Γ (119871) Γ (minus120572) (120574 + 1198711199092)119871minus120572

minus120572 120574 119871 119909 gt 0

(8)





by (8)

119875fa = intinfin

119879119889

119891119883 (119909) 119889119909 = int

infin

119879119889

2119871119871Γ (119871 minus 120572) 119909

2119871minus1

120574120572Γ (119871) Γ (minus120572) (120574 + 1198711199092)119871minus120572

119889119909

(9)


119875fa =120572 minus 1

120572120574120572(120574 + 119879

2

119889)120572

(10)


119879119889= (

120572120574120572

120572 minus 11198751120572

fa minus 120574)

12

(11)























ClutterTarget

(a)

ClutterTarget

(b)

ClutterTarget

(c)

Interferer

ClutterTarget

(d)

Interferer

ClutterTarget

(e)

Interferer

ClutterTarget

(f)



5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility







log(

pfa)

minus9

minus8

minus7

minus6

minus5

minus4

minus3

minus2

minus1




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility



2 interferers

4 interferers




5 10 15 20 25 30SNR (dB)

Prob

abili

ty o

f fal

se al

arm


10minus3

10minus4

10minus5

(a)

5 10 15 20 25 3015

2

25

3

35

4

45

5

SNR (dB)

Varia

ble i

ndex


KVI

(b)


1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

(a)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

12

3

(b)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

2122

23

2425

2627

28

29

30

3132

33

34

353637

3839

12

(c)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

2425

2627

28

29

30

3132

33

34

353637

3839

12 3

(d)



1

2

3

4

5

67

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

3031

32

33

34

35

36

37 3839

40

41

(a)

1

2

3

4

5

67

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

30

31

32 33

34

35

36

12

3

45

(b)

1

2

3

1

4

2

5

67

389

4

10

11

12

13

14

5

15

16

17

18

19

20

21

22

23

24

25

26

2728

29

30

31

3233

34 3536

37

38

6

1

2 3

(c)

1

2

3

1

4

5

267

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

3

29

3031

32

33

34

35

36

374

3839

40

41

5





6 Conclusions












Acknowledgment


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation

























ClutterTarget

(a)

ClutterTarget

(b)

ClutterTarget

(c)

Interferer

ClutterTarget

(d)

Interferer

ClutterTarget

(e)

Interferer

ClutterTarget

(f)



5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility







log(

pfa)

minus9

minus8

minus7

minus6

minus5

minus4

minus3

minus2

minus1




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility



2 interferers

4 interferers




5 10 15 20 25 30SNR (dB)

Prob

abili

ty o

f fal

se al

arm


10minus3

10minus4

10minus5

(a)

5 10 15 20 25 3015

2

25

3

35

4

45

5

SNR (dB)

Varia

ble i

ndex


KVI

(b)


1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

(a)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

12

3

(b)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

2122

23

2425

2627

28

29

30

3132

33

34

353637

3839

12

(c)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

2425

2627

28

29

30

3132

33

34

353637

3839

12 3

(d)



1

2

3

4

5

67

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

3031

32

33

34

35

36

37 3839

40

41

(a)

1

2

3

4

5

67

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

30

31

32 33

34

35

36

12

3

45

(b)

1

2

3

1

4

2

5

67

389

4

10

11

12

13

14

5

15

16

17

18

19

20

21

22

23

24

25

26

2728

29

30

31

3233

34 3536

37

38

6

1

2 3

(c)

1

2

3

1

4

5

267

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

3

29

3031

32

33

34

35

36

374

3839

40

41

5





6 Conclusions












Acknowledgment


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility







log(

pfa)

minus9

minus8

minus7

minus6

minus5

minus4

minus3

minus2

minus1




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility




5 10 15 20 25 300

01

02

03

04

05

06

07

08

09

1

SNR (dB)

Det

ectio

n pr

obab

ility



2 interferers

4 interferers




5 10 15 20 25 30SNR (dB)

Prob

abili

ty o

f fal

se al

arm


10minus3

10minus4

10minus5

(a)

5 10 15 20 25 3015

2

25

3

35

4

45

5

SNR (dB)

Varia

ble i

ndex


KVI

(b)


1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

(a)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

12

3

(b)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

2122

23

2425

2627

28

29

30

3132

33

34

353637

3839

12

(c)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

2425

2627

28

29

30

3132

33

34

353637

3839

12 3

(d)



1

2

3

4

5

67

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

3031

32

33

34

35

36

37 3839

40

41

(a)

1

2

3

4

5

67

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

30

31

32 33

34

35

36

12

3

45

(b)

1

2

3

1

4

2

5

67

389

4

10

11

12

13

14

5

15

16

17

18

19

20

21

22

23

24

25

26

2728

29

30

31

3233

34 3536

37

38

6

1

2 3

(c)

1

2

3

1

4

5

267

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

3

29

3031

32

33

34

35

36

374

3839

40

41

5





6 Conclusions












Acknowledgment


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










5 10 15 20 25 30SNR (dB)

Prob

abili

ty o

f fal

se al

arm


10minus3

10minus4

10minus5

(a)

5 10 15 20 25 3015

2

25

3

35

4

45

5

SNR (dB)

Varia

ble i

ndex


KVI

(b)


1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

(a)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3132

33

34

353637

3839

12

3

(b)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

2122

23

2425

2627

28

29

30

3132

33

34

353637

3839

12

(c)

1

2

3

4

5

67

8

9

10

1112

13

14

15

16

17

18

19

20

21

22

23

2425

2627

28

29

30

3132

33

34

353637

3839

12 3

(d)



1

2

3

4

5

67

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

3031

32

33

34

35

36

37 3839

40

41

(a)

1

2

3

4

5

67

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

30

31

32 33

34

35

36

12

3

45

(b)

1

2

3

1

4

2

5

67

389

4

10

11

12

13

14

5

15

16

17

18

19

20

21

22

23

24

25

26

2728

29

30

31

3233

34 3536

37

38

6

1

2 3

(c)

1

2

3

1

4

5

267

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

3

29

3031

32

33

34

35

36

374

3839

40

41

5





6 Conclusions












Acknowledgment


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










1

2

3

4

5

67

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

3031

32

33

34

35

36

37 3839

40

41

(a)

1

2

3

4

5

67

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

29

30

31

32 33

34

35

36

12

3

45

(b)

1

2

3

1

4

2

5

67

389

4

10

11

12

13

14

5

15

16

17

18

19

20

21

22

23

24

25

26

2728

29

30

31

3233

34 3536

37

38

6

1

2 3

(c)

1

2

3

1

4

5

267

89

10

11

1213

14

15

16

17

18

19

20

21

22

23

24

2526

27

28

3

29

3031

32

33

34

35

36

374

3839

40

41

5





6 Conclusions












Acknowledgment


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation
















Acknowledgment


References






















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Research Article A Novel Variable Index and Excision CFAR Based Ship Detection...

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Transcript of Research Article A Novel Variable Index and Excision CFAR Based Ship Detection...