Automatic Identification Method of Link4A

ZHU Bin-gang1, PENG Shi-rui2, SU Yuan-wei1,TANG Tang1 1. Department of Graduation Department, AFRA, Wuhan 430019, China;

2. Department of Information Countermeasures, AFRA, Wuhan 430019, China

Abstract

Accurate identification of Link4A is the premise of performing effective jamming. Based on instantaneous information identification algorithm, three key features, including two new ones, are extracted which are used to identify 2FSK modulation signal: the maximum value of the spectral power density of the normalized-centered instantaneous amplitude max� ,the mean of the absolute value of instantaneous frequency offset f� and the standard deviation of the absolute value of instantaneous frequency offset af� .Simulation result indicates that the success rate reach 91.8% at SNR=-10dB about 2FSK modulation signal. Then estimating the code-width of the 2FSK modulation signal, and comparing it with the real Link4A parameter, simulation result also indicates that the success rate reach 100% at SNR=5dB. So, this mean has a high success rate under low SNR condition, and it would have a favorable using foreground.

Key words: Link4A; automatic identification key feature

1. Introduction

In the information war, Link4A is used to communicate between early warning aircraft and operational aircraft, and perform AIC, ATC, ACLS, and so on [1]. It is an effective method to jam Link4A link for debasing the operational effectiveness of early warning aircraft. And Link4A must be effectively surveilled and identified before effectively jamming.

It has many features for Link4A [2], for example, its frequency ranges 225MHz 399.975MHz 2FSK modulation, ±20kHz frequency offset, 200�s or 100�s code-width. According to those features, there are three steps to identify it: 2FSK modulation identification, frequency offset estimation and code-width estimation.

Until now, there has been so few paper referring to

automatic identification technology, but communication signal automatic identification technology based on instantaneous information has been in-depth studied [3~7]. In [3], the maximum value of the spectral power density of the normalized-centered instantaneous amplitude max� is chosen to identify FSK signal, but it discusses the case only when the SNR is 10dB and 20dB, and it does not study the identification between 2FSK and 4FSK. In [4], the ratio of envelop variance to expectation of envelop’ square is chosen to identify 2ASK signal, and differential envelop is chosen to identify 2FSK, but it is effective only at SNR�6dB. In [5], max� is chosen, but it does not discuss the case at SNR�5dB, and the limits difference is too small to judge. In [6], the FSK signal could be identified out only at SNR�8dB, the standard deviation of the absolute value of the normalized-centered instantaneous amplitude in the no weak segments is chosen to identify 2FSK and 4FSK, but it is effective only at SNR�5dB. In [7], key feature

max� could identify out 2FSK and 2PSK, and it needs another step to identify 2FSK.

Based on classed digital modulation signals, parameter estimate technology and key feature automatic identification technology, this paper puts forward a method which could realize automatic identification Link4A. Referring to the related key feature, putting forward two new key features: the mean of the absolute value of instantaneous frequency offset f� and the standard deviation of the absolute value of instantaneous frequency offset af� . The Link4A signal can be identified with the following steps: recognizing modulation according to the three key features; instantaneous frequency offset estimation of 2FSK modulation signal with wavelet denoising; code-width estimation. The simulation indicates that success rate 90 at SNR=-10dB, and Link4A signal can be identified effectively at SNR 0dB.

2009 International Conference on Networks Security, Wireless Communications and Trusted Computing

978-0-7695-3610-1/09 $25.00 © 2009 IEEE

DOI 10.1109/NSWCTC.2009.393

239

2. Digital modulation identification Extracting the key features which are used for

identify modulation is the first and most important step. Based on signal’s instantaneous information, extracting the following three key features of ASK FSK PSKQAM the maximum value of the spectral power density of the normalized-centered instantaneous amplitude max� , the mean of the absolute value of instantaneous frequency offset f� , the standard deviation of the absolute value of instantaneous frequency offset af� . By simulation, the judging limit can be decided to realize the exact identification for 2FSK modulation.

(a) The maximum value of the spectral power density of the normalized-centered instantaneous amplitude max�

max� is defined by[3]:

� �

2

max

max cnFFT a i

N�

� � � 1

where N is the sample-point number,

is the normalized-centered

instantaneous amplitude, and � � � � 1cn na i a i� �

� � � �na i a i m� a ,

� �1

1 N

ai

m aN �

� i . For eliminating the effect of channel

gain, we use mean to normalize the instantaneous amplitude. max� expresses the maximum value of the spectral power density of the normalized-centered instantaneous amplitude which is related to signal’s instantaneous amplitude. Theoretically, FSK and PSK modulation signals’ instantaneous amplitude are constant, so max 0� � ; For ASK and QAM modulation signals, max 0� � because their amplitude envelops differ much. So max� can be used to distinguish FSK PSK and ASK QAM modulation signals. In fact,

max� of FSK and PSK modulation signals are not equal to 0 because the amplitude would have a break when the phase or frequency has a change respectively.

Simulation parameters: frequencies of 2FSK modulation signal respectively are 380kHz and 420kHz; frequencies of 4FSK modulation signal respectively are 380kHz, 390kHz, 410kHz and 420kHz; frequency of 2ASK modulation signal is 400kHz, and the amplitude is 0.3; frequency of 16QAM modulation signal is 400kHz, and the amplitude respectively are 0.3 and 1; the code-width is 200�s.

Each modulation signal’s max� with different SNR

is shown in Figure 1. Assuming a limit � �maxt � at SNR -10dB, it would be FSK or PSK modulation signal if max� is larger than , otherwise it would be ASK or QAM modulation signal.

� maxt � �

-20 -15 -10 -5 0 5 10 15 20 25 300

50

100

150

200

250

300

350

400

2FSK

2ASK

2PSK4FSK

16QAM

r max

SNR dB Figure 1 Change of max� with SNR

(b) The mean of the absolute value of instantaneous frequency offset f�

f� is defined by:

1

1 ( )N

fi

cf i fN

��

� � 2

where N is the sample-point number, ( )f i is the instantaneous frequency after wavelet denoising, detailed estimation is shown in 3.2, cf is the central frequency of signal. Instantaneous frequency of PSK is a constant, but there is a difference between instantaneous frequency and central frequency because MFSK has M frequency-points. So f� can be used to distinguish FSK. With the same simulation parameters, frequency offsets of 2PSK 2FSK and 4FSK are respectively shown in Figure 2~Figure 4.

0 0.5 1 1.5 2 2.5

x 104

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5x 10

5

(Hz)

Sample-point number

Inst

anta

neou

s fr

eque

ncy

offs

et (H

z)

Figure 2 Instantaneous frequency offset of 2PSK

From Figure 2~Figure 4, we could know that PSK has a big frequency offset only when it has a phase change, and 2FSK and 4FSK has a big frequency offset all the time. So the mean of the absolute value of instantaneous frequency offset of 2FSK and 4FSK is far bigger than 2PSK. The mean of the absolute value of instantaneous frequency offset with different SNR is shown in Table 1. After analyzing Table 1, we could

240

know that 2PSK could be distinguished with an appropriate limit � �ft � . If � �f t � �2

1

1 ( )N

af m fi

f i mN

��

� � 3 f� �� , it is 2FSK or

4FSK, and otherwise it is 2PSK. where N is the sample-point number, is the instantaneous frequency after wavelet denoising,

is the central frequency,

( )f i

cf

1

1 N

f mi

m fN �

0 0.5 1 1.5 2 2.5

x 104

-3

-2

-1

0

1

2

3x 10

4(H

z)

Sample-point number

Inst

anta

neou

s fr

eque

ncy

offs

et (H

z)

( )� i ,

( ) ( )m cf i f i f� � . af� is used to distinguish 2FSK from 4FSK. Theoretically, the absolute value of the instantaneous frequency offset of 2FSK is constant, so

0af� � ; For 4FSK, it has two values about its absolute value of instantaneous frequency offset, so 0af� � . In fact, af� of 2FSK is bigger than 0 because of noise and filter band-width, and so on. af� of 2FSK and 4FSK with different SNR are shown in Table 2. By analyzing Table 2, we could know that 2FSK could be distinguished with an appropriate limit � �ft � .

Figure 3 Instantaneous frequency offset of 2FSK

0 0.5 1 1.5 2 2.5

x 104

-3

-2

-1

0

1

2

3x 10

4

(Hz)

Sample-point number

Inst

anta

neou

s fr

eque

ncy

offs

et (H

z)

(d) Principle of choosing limit From Figure 1, Table 1, Table 2, the 2FSK

modulation signal can be distinguished with appropriate limits. In this paper, the following limits could be chosen: � �max 45t � � , , � � 10000ft � �

� � 2500aft � � . With the same simulation parameters, success rates with different SNR are shown in Table 3. Figure 4 Instantaneous frequency offset of 4FSK

(c) The standard deviation of the absolute value of instantaneous frequency offset af�

af� is defined by:

Table 1 The mean of the absolute value of instantaneous frequency offset

2FSK 19807 19813 19814 19800 19820 19819 19812 19815 19829 4FSK 13259 13365 13237 13195 13355 13218 13293 12946 13324 2PSK 2108 1669 1619 1514 1456 1561 1405 1447 1472

Table2 Standard deviation of 2FSK and 4FSK

2FSK 1801.4 1564.7 1499.7 1453.8 1421.4 1428 1479.9 1503.4 1508.3 4FSK 4778.4 4741.2 4744 4679.8 4709.4 4737 4707 4713.6 4661.3

Table 3 Identification rate of 2FSK

SNR dB -10 -5 0 5

Identification rate 91.8% 93.6% 98.6% 100%

af� SNR dB

Modulation

SNR dB

Modulation

af�

As we know, each modulation mode’s signal � �S n can be given by:

3. Frequency offset estimate of 2FSK

241

4 � � � � � �cos cS n a n n n� �� �� �where and is amplitude and phase

respectively, � �a n

c

� �n�� is the carry frequency or central

frequency. The equation above also can be expressed as:

5 � � � � � �cos( ) sin( )c cS n I n n Q n n� �� �Where � � � � � �cosI n a n n�� 6

7 � � � � � �sinQ n a n n��which are called as in-phase component and

orthogonal component, respectively. In-phase and orthogonal component can illustrate the feature of the given signal because c� does not have any information, and the two components just are the parameters which are used to estimate the frequency offset. Instantaneous frequency offset � �f n� is given by:

� � � � � � � � � �

� � � �� � � � � � � �

� � � �

' '

2 2

2 2

2

1 12

s

s

I n Q n I n Q n ff n

I n Q n

I n Q n I n Q n fI n Q n

�

�

�� �

�

� � ��

�

8

Instantaneous frequency offset of 2FSK signal at SNR=-10dB is shown in Figure 5. By analyzing the Figure 5, we know that instantaneous frequency offset can be estimated using this method at SNR=-10dB, but it has a big undulation. In this paper, wavelet denoising is used to deal with the instantaneous frequency offset which would reduce the estimate error, and instantaneous frequency offset of 2FSK after wavelet denoising is shown in Figure 3.

The estimate value of frequency offset can be gained by calculating the mean of the absolute value of instantaneous frequecny.

1

1 ( )N

if f i

N �

� � � 9

where ( )f i� is the instantaneous frequency offset after wavelet denoising.

0 0.5 1 1.5 2 2.5

x 104

-1.5

-1

-0.5

0

0.5

1x 10

5

(Hz)

Sample-point number

Inst

anta

neou

s fr

eque

ncy

offs

et (H

z)

Figure 5 Instantaneous frequency offset of 2FSK

4. Code-width estimation

Delay-multiply test is an estimate algorithm of code-width based on the maximum value in the delay-multiply power spectrum of signal, and it can estimate the code-width exactly with low SNR. Its structure is shown in Figure 6.

LPF

Signal x(t)

Delay Td

Estimate of power spectrum

S(f)

Figure 6 Structure of the delay-multiply algorithm

With the same parameters and SNR=5dB, success rate has a big fluctuation with different delay times, as shown in Figure 7.

0 20 40 60 80 100 120 140 160 180 2000

10

20

30

40

50

60

70

80

90

100

Delay time us

Exac

t rat

e%

Figure 7 The relationship between success rate

and delay time

By analyzing the Figure 7, success rate can reach 98.5% when delay time is equal to half of the code-width, 100μs. And it can reach 94% when delay time is 75μs and 125μs. But this algorithm can not operate well with other delay times. In practical application, the delay time must be set as the half of the code-width for gaining well effect.

Theoretically, SNR has an important effect on the estimate of the code-width. With the same parameters, and delay time is 100μs, the relationship between success rate of estimate and the SNR is shown in Figure 8.

-5 -4 -3 -2 -1 0 1 2 3 4 50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Exac

t rat

e

Figure 8 The relationship between exact rate and SNR

By analyzing the Figure 8, success rate of code-width estimate has a quick decline with the

242

decline of SNR. It can exactly estimate the code-width at SNR=5dB, but the success rate only reaches 80% at SNR=0dB.

[7] Jiang Yuan, Zhang Zhao-Yang, Qiu Pei-Liang.Modulation Classification of Communication Signals[J]. IEEE Military Communication Conference, 2004.

Based on the discussion above, the delay time must be set as the half of the code-width and the SNR should be high enough if we want to have an exact estimate.

5. Conclusion

After putting forward two feature parameters, based on the instantaneous information of the modulated signal, this paper extrcats three key features: the maximum value of the spectral power density of the normalized-centered instantaneous amplitude max� , the mean of the absolute value of instantaneous frequency offset f� , the standard deviation of the absolute value of instantaneous frequency offset af� . From simulations and analyses, this algorithm can identify 2FSK modulation signal at SNR -10dB. And the paper estimates the code-width of the identified 2FSK modulation signal, and compared it with the real Link4A parameter.

By analyzing the Figure 1, max� can be used to identify the 2FSK directly at SNR 15dB, but its precondition is too high to be used widely. Identification process in the paper is fussy, but the differences of feature parameters with different modulation are obvious, and it will have a piratical application widely. References [1] YU Xiaogang, WANG Hua, GONG Cheng. Introduction of American Tactical Link[J]. Aviation Electronic Technology, 2002, 33, 3. [2] WANG Guoming, Cai Xiao, HAO Erwei. Study of Link4A and Its Contermeasures[J]. Ship Electronic Engineering,2007. [3] DU Xiaoguang. Software radio: Simulation on Modulation Mode Recognition and Demodulation Algorithms[D]. Jilin University, 2006. [4] LV Xinzheng. Study of Communication Modulation Rcgonition based on Several Feature Parameters[D]. UESTC, 2004. [5] XUE Mingwen. Automatic Modulation Recognition in The Software Defined Radio Technology[D]. Harbin Institute of Techonology, 2006. [6] E.E. Azzouz, A.K. Nandi. Automatic identification of digital modulation types[J]. IEEE Signal Processing, 1995.

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