WITE SANDS NISSILL DETECTION IN THE RANGE NM … · 2014. 9. 27. · TECHNICAL REPORT 00...
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AD-AI71 620 SPIN FREQUENCY DETECTION IN THE SPECTRAL DONIN(U) 1/2 WITE SANDS NISSILL RANGE NM INSTRUMENTATION DIRECTORATE D S JIMAREZ MAR 86 STEMS-ID-S6-1 UNCLAm FE 71 N
Transcript of WITE SANDS NISSILL DETECTION IN THE RANGE NM … · 2014. 9. 27. · TECHNICAL REPORT 00...
AD-AI71 620 SPIN FREQUENCY DETECTION IN THE SPECTRAL DONIN(U) 1/2WITE SANDS NISSILL RANGE NM INSTRUMENTATIONDIRECTORATE D S JIMAREZ MAR 86 STEMS-ID-S6-1
UNCLAm FE 71 N
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TECHNICAL REPORT
00 STEWS-ID-86-1
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SPIN FREQUENCY DETECTION IN THE SPECTRAL DOMAIN
DAVID JIMAREZ
Electronics Engineer
March 1986
,'-
Approved for public release; distribution is unlimited.
DTICU.' ELECTE:
u..SEP 1 61986w
INSTRUMENTATION DIRECTORATE -
U.S. ARMY WHITE SANDS MISSILE RANGEWHITE SANDS MISSILE RANGE, NEW MEXICO 88002
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fxp Dlare ,un30, 1986
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SPIN FREQUENCY DETECTION IN THE SPECTRAL DOMAIN
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Jimarez. David S.13a TYPE OF REPORT 13b TIME COVERED 14 DATE OF REPORT (Year, Month, Day) 15 PAGE COUNT
Final FROM TO March 19861 145'6 SUPPLEMENTARY NOTATION
17 COSATI CODES 18 SUBJECT TERMS (Continue on reverse if necessary and identify by block number)
FIELD GROUP SUB-GROUP Instrumentation radar Radar data processingI Doppler processing Heuristics
19 ABSTRACT (Continue on reverse if necessary and identify by block number)This research involves the development, implementation and optimization of algorithms fortracking the spectral spin frequency representations of a revolving cylindrical target havingfour protruding scatterers. The investigation is limited to coherent phase and amplitudedata that are constant to within a few millimeters per second with respect to the base of thecylinder, spin frequencies between five and fifteen Hz, and an absolute spin frequency rateof change less than 1.25 Hz per second. The research was conducted such that the algorithmsand procedures that were developed could be performed by analysts who are relatively unskille,in this analysis. The heuristic methodology utilized in this effort is one wherein a workingmodel of the expert analyst's problem-solving approach is obtained by observing him performthe manual procedure, generating the associated protocols, and then programming this intelli-gence into the machine. The manual procedure was easy to understand and to implement in themachine.
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TABLE OF CONTENTS
Page No.
LIST OF ILLUSTRATIONS .......... ..................... v
INTRODUCTION .......... .......................... 1
THEORY ............ ............................. 1
TRACKING PROBLEMS AND THE MANUAL TRACKING PROCEDURE ... ...... 4
INITIAL TRACKING PROCEDURES ......... .................. 6
AUTOMATIC TRACKING ALGORITHM DEVELOPMENT ...... ............ 9
KNOWLEDGE BASED SYSTEM DEVELOPMENT .... ............... . 11
CONCLUSION ........... .......................... 12
REFERENCES .......... ........................... 70
APPENDIX A. PROGRAMS ....... .................... 71
APPENDIX B. DATA SETS ........ .................... 109
DISTRIBUTION LIST ........ ....................... 135
Accession For
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LIST OF ILLUSTRATIONS
Page No.
Figure 1. Typical doppler history plot .. ........ ... 13
Figure 2. Target scattering center orientation ..... .. 14
Figure 3. Target aspect angle ... ............. ... 15
Figure 4(a). Single scattering center .... ......... 16
Figure 4(b). Relationship of spectral content toFourier transform size .. ......... .. 17
Figures 5(a)-(t). Doppler history plot withFourier transform windowequal to 1/5 cycle of spin ....... .. 18 - 37
Figures 6(a)-(J). Doppler history plot withFourier transform windowequal to 2/5 cycle of spin ....... .. 38 - 47
Figures 7(a)-(e). Doppler history plot with
Fourier transform windowequal to 4/5 cycle of spin ...... .. 48 - 52
Figures 8(a)-(c). Doppler history plot withFourier transform windowequal to 8/5 cycle of spin ...... .. 53 - 55
Figures 9(a)-(b). Doppler history plot withFourier transform windowequal to 16/5 cycle of spin . . . . 56 - 57
Figure 10. Doppler history plot with Fourier trans-form window equal to 32/5 cycle of spin . 58
Figures 1l(a)-(b). Doppler history plot with Fouriertransform window equal to32/5 cycle of spin and lagequal to 16/5 cycles of spin . . 59 - 60
Figures 12(a)-(b). Doppler history plot representa-tive of noise .... .......... ... 61 - 62
Figure 13. Doppler history plot illustrative offading of spin traces ..... ........... 63
Figure 14. Doppler history plot illustrative ofaliasing and crossover .. .......... ... 64
Figure 15. Manual analysis of a doppler history plot . 65
Figure 16. Selection of candidate spin returns ..... .. 66
Figure 17. Knowledge based system ..... ........... 67
Figure 18. Interface and knowledge base .. ........ .. 68
Figure 19. Cognitive engine ..... .............. .. 69
Figure B-1. Data set I ...... ................. ... 109
Figure B-2. Data set 2 ...... ................. ... 123
Figure B-3. Data set 3 ...... ................. ... 128
v
INTRODUCTION
This research involves the development, implementation and optimization ofalgorithms for tracking the spectral spin frequency representations of arevolving cylindrical target having four protruding scatterers. Theinvestigation is limited to coherent phase and amplitude data that are constantto within a few millimeters per second with respect to the base of thecylinder, spin frequencies between five and fifteen Hertz (Hz), and an absolutespin frequency rate of change less than 1.25 Hz per second. The research wasconducted such that the algorithms and procedures that were developed could beperformed by analysts who are relatively unskilled in this analysis. Theheuristic methodology utilized in this effort is one wherein a working model ofthe expert analyst's problem-solving approach is obtained by observing himperform the manual procedure, generating the associated protocols, and thenprogramming this intelligence into the machine. The manual procedure was easyto understand and to implement in the machine.
THEORY
In the coherent doppler processing of radar data from targets with multiplescattering centers, a frequently used method for information display is thedoppler history plot. In this plot, the doppler content of the signal, i.e.,the velocities of the various scattering centers relative to the radar aredisplayed as a function of time. The plot is generated by moving a window ofpredetermined size through the amplitude and phase data, at an appropriate lag,and mapping the contents of each window into the spectral domain. Next thespectra are sequentially plotted, equispaced, one behind the other, usinghidden line plotting techniques. Figure 1 shows an example of a typicaldoppler history plot.[]
In this plot, each peak's location in the frequency spectrum is directlyproportional to the average relative velocity of the scattering center itrepresents. A peak in the positive portion of the spectrum represents ascattering center moving towards the radar, while a peak in the negativeportion represents a scattering center moving away from the radar. Radialvelocities corresponding to the spectral frequencies are shown in meters persecond in the bottom scale. The relationship between radial velocity anddoppler frequency is directly proportional to the wavelength of the radar, asseen in Equation 1.
i - X/2fd(1
where R - radial velocity (m/sec)
- radar wavelength (a)
fd - doppler frequency
If a scattering center has an associated velocity too large to be representedon one side of a spectrum a velocity ambiguity occurs and the peak
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representation appears wrapped around to the other side of the spectrum. Thisparticular effect is classically known as "aliasing," and occurs when theNyquist criterion is not met, i.e., when the sampling rate is less than twicethe highest frequency component present in the data.
The first two steps in the investigation involve associating the velocityinformation in the doppler history plot with the spin frequencies of thesubject target, and then determining the variation of doppler processing whichbest displays the spin frequency content for subsequent tracking. The firststep is achieved by exmining the scattering center orientation of the target,with respect to the radar line of sight. The second step requires examinationof long-tern Fourier transforms, those encompassing sev rr l cycles of spin,which bring up the FM sidebands of the spin modulation.r2z
Examination of target scattering center orientation began with the analysis of
Figure 2.
As shown, the four scattering centers that produce spin frequency effects inthe doppler history plot are symmetrically located with respect to the axis ofthe cylinder. Dominance of the spectral spin information is due to therelatively long distance they extend out from the cylinder, as opposed to anyother scatterers which may exist near the surface. Since this distance and thecarrier frequency are constant, the spin doppler excursion is determined by thetarget aspect angle, Q. As shown in Figure 3, Q, which varies between 0 and 90degrees, is defined to be the angle between the radar line of sight and thespin axis of the target. The mathematical relationship for the excursion ofspin doppler is expressed in Equation 2.
dfAf = 4Rf C sin Q (2)s c
where
Af = excursion of spin doppler
f = spin frequency
I = distance from spin axis to scatterers
f = carrier frequencyC
c = speed of light
S2 = angle between radar line of sight and spin axis
Equation 2 shows that spin doppler varies sinusoidally from 0 when - 0',i.e., the radar line of sight is aligned with the axis of the target, to amaximum when * 90',i.e., the radar line of sight is perpendicular to the axis
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of the target. Alternately, this information can be expressed in terms of themodulation index, iV, as shown in Equation 3.
A f= - = 41d-11 sin 2 (3)
f cCS
The modulation index also increases as R tends toward 90', thus placing more ofthe power into the FM sidebands of the spin information. However, due to thefour-fold symmetry of the scatterers, only multiples of four times the spinfrequency appear in the sidebands, as opposing doppler returns from thesymmetric scatterers usually cause cancellation of all intermediate returns.
The next step in the research involved examination of long-term Fouriertransforms, those encompassing several cycles of spin, in order to develop areasonable display of the sidebands present. This effort began by consideringthe situation depicted in Figure 4a. Here, a single scattering center is shownspinning about its tip, with the radar line of sight in the plan of rotation,i.e., - 900
Next, Figure 4b was developed, depicting representative spectral informationthat would be received by the radar in this case. The left-hand side of Figure4b shows a doppler history plot encompassing two cycles of this target's spin,where a very narrow transform window would need to have been used for nearinstantaneous frequency representation. The right-hand side of Figure 4b showsthe corresponding single spectrum contents of windows, which contain a negativehalf cycle, a full cycle, and a positive half cycle of spin, from top tobottom, respectively. This figure indicates that only a transform window,encompassing at least a full cycle of spin, can contain all the FM sidebands ofspin modulation.
As a proof of this indication, doppler histories were generated from typicalamplitude and phase data from the subject target for Fourier transform windowsencompassing 1/5, 2/5, 4/5, 8/5, 16/5, and 32/5 cycles of spin. These dopplerhistories are displayed in Figures 5 through 10, respectively. Inspection ofthese doppler histories gave evidence of the spin sideband tendency to settleinto a single spectrum as the transform window approaches a full cycle of spin.Further, as the transform window expands to encompass several cycles of spin,fewer sidebands are lost due to temporary destructive interference; and theirrepresentations sharpen, due to the increased number of points in the window.This effect allows for more precise manual frequency determination.
Two other points are worthy of note in Figures 5 through 10. First, the totalnormalized power in each of the spectra is equal, as was the case in Figure 4bbetween the 1/2 cycle and full cycle spectral displays. Therefore, increasingthe length of the Fourier transform window has no effect on the totalnormalized power in each spectrum, but rather on how the power is distributed,i.e., according to the spectral content. Second, the lag used in moving thetransform window through the data was equal to the length of the transformwindow. This implies uncorrelated spectra, i.e., no common time domain data isused in the production of previous or successive spectra. Figure 11 is
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equivalent to Figure 10, except that each adjacent pair of spectra iscorrelated. Using a lag of 1/2 the size of the transform window, each spectrumis computed with 1/2 of the tine domain data used to compute each of itsadjacent neighbors. Correlated spectra is frequently produced in dopplerhistory displays to achieve the effect of smearing the spectral peaks as afunction of time, thus leaving traces of scattering center velocities which aremore pleasing to the human eye.
For the subject target, the number of points in each transform window waschosen to be 256, so as to encompass at least four cycles of spin and to givereasonably sharp spin traces. Production of noncorrelated spectra, i.e., lagequal to 256, was chosen for subsequent tracking, as the human eye was notintended to be part of the automated process; and also in that this reduces thecomputational workload. It was noted that reduction of a given lag, by afactor of two, doubles the number of spectra that must be produced andsubsequently handled. A lag greater than the transform window would furtherreduce the computational workload; but this was determined unfeasible, asinformation would be lost in the doppler display.
As noted in the introduction, the phase and amplitude data that are used areconstant to within a few millimeters per second, or normalized with respect tothe base of the cylindrical target. Unfortunately, the relatively largeamplitude of the base return frequently causes spin returns not to be seen inthe normalized doppler history display. Therefore, for the spin dopplerhistory displays presented in this paper, the base return has been filtered outof each spectrum after normalization, in order to bring up the sidebands of thespin modulation. 13]
TRACKING PROBEMDS AND THE MANUAL TRACKING PROCEDURE
This research has identified four classes of problems in the doppler historydata which have, to date, inhibited the development of an automated process fortracking spin frequencies. These problems, which may occur in any combination,are:
" Noise, used here in a general context to describeboth random noise and undesired clutter returns.
" Periodic fading or cancellation of spin frequencyreturns.
* Wraparound or aliasing of the higher spin frequencymultiples.
" Crossover of nonwrapped and wrapped spin returns.
For testing of automated spin line tracking algorithms to be developed, threesets of data were selected which exhibit various combinations and intensitiesof all four classes of problems. Data Set 1, shown in Figure 12, exhibits
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fading and spin multiple crossover in a severe noise environment. Noise, asillustrated in this figure, may appear at any frequency, singularly or inclusters, and with amplitudes often larger than those of the spin returns.Data Set 2, shown in Figure 13, illustrates an example of severe fading andcancellation of spin frequency returns while in a relatively low noiseenvironment. Such fading is due to variation in radar orientation (target'saspect angle) and destructive interference from other returns. Data Set 3,shown in Figure 14, exhibits very prominent higher spin multiples, which aliasand make distinction difficult at points where they cross over lower spinmultiples. Another problem is an occasional strong 60 Hertz line caused byinterference; however it was considered too rare to be included as another mainclass problem.
The first step in the development of an automated spin frequency tracker was toobserve a skilled analyst while performing such a data reduction. Thefollowing steps describe the procedure obtained from these observations.
1. A doppler history plot is generated with the following characteristics:
a. A transform window, large enough to produce reasonably sharp spintraces.
b. A lag, half the size of the transform window, to produce smearing ofthe spectral peaks.
c. High pass filtering, to remove the relatively large baserepresentation.
2. Next, traces of spin frequeacy multiples in the doppler history plotare identified and marked. These multiples are denoted as mrs where fs is thespin frequency and, m = 4, ±8, t12. ..... Here again, it is noted that,generally, only multiples of 4fs are present.
3. On a spectrum-by-spectrum basis, the largest mf., which can beidentified and is not aliased, is then selected for tracking. The spin historyis then calculated by measuring the zero doppler offset of these multiples,dividing by the corresponding multiple, and recording the results as a functionof time.
In identifying the spin frequency traces, the analyst utilized the aprioriknowledge that the actual spin frequency will be between 5 and 15 Hz, with rareexception. Using this information the analyst identifies the 4f, multiple asthe lowest spin frequency trace in the interval 20 Hz to 60 Hz. The -4f,multiple is identified in a similar manner. Higher multiples are subsequentlyidentified by searching in the area of the appropriate doppler offset for thatmultiple. For example, if the 4fs trace was located at approximately 20 Hz,the 8fs multiple would be searched for around 40 Hz, the 12f. multiple ataround 60 Hz, and so forth. Since the error in frequency measurement foridentifiable multiples is approximately equal, selection of the highestmultiple minimizes error in the calculated spin frequency as the measurement isdivided by that multiple. Aliased multiples are not considered, due to their
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generally lover relative amplitude, and due to the additional constant thatmust be included in the calculations. When fading occurs, the next lower,identifiable multiple is selected for tracking. When higher multiples aliasand crossover traces currently being tracked, the analyst has the advantage offollowing the general trend of the trace under track and, thus, usually avoidsconfusion between spin representations. In cases where no spin multiples areidentifiable due to noise and/or fading the analyst usually interpolates theseareas with the values of previously and successively tracked spin frequencies.Noting that such measurements and calculations are very labor intensive on aspectrum-by-spectrum basis for long periods of track, the analyst willfrequently make measurements only on every third or fifth spectrum andinterpolate the otbers as long as the data are relatively clean and this can bedone without loss of continuity or track. Obviously this is not always thecase due to the tracking problems that are involved. Figure 15 illustrates themanual analysis where only three spin frequencies have been calculated.
INITIAL TRACKING PROCEDURES
As an adjunct to observing the skilled analyst perform the manual trackingprocess, work on a similar problem by graduate students at the Cognitive SystemLaboratory, University of California at Los Angeles (UCLA) was also reviewed.In this work spin doppler returns from a similar target were simulated in orderto develop like tracking algorithms. These algorithmns were developed on thebasis of two fundamental assumptions.
First, a given candidate spin representation in a particular positive halfspectrum should have a like representation in the negative half spectrum.Second, an algorithm should be able to locate, in previous and successivespectra, similar candidate returns which follow a particular spin frequencytrend. Therefore positive frequency trends should be correlatable with theircounter representations in the respective negative half spectrum. This processstarted with a Monte Carlo type simulation, where positive and negative spinfrequency representations were generated and then combined, constructively anddestructively, with randomly generated noise, and used as the basis to computethe doppler power spectra. Next, the tracking algorithms would, for eachspectrum, select symmetrically located peaks as candidate spin frequencyreturns. Previous and successive spectra would then be examined, to determineif the candidate returns fit within candidate spin frequency trends. Thosecandidate returns which did not fit would then be eliminated. Finally, thechosen spin frequency trends would be selected, using the apriori knowledgethat they should be multiples of four times the fundamental spin frequency andshould last, for the most part, for the duration of the data, i.e., shortspurious trends would be eliminated. The spin frequency as a function of timewould then be calculated from these trends.
This process proved difficult to implement on available computational hardware,slow in terms of total execution time, and usually failed to obtain the correctspin frequencies when applied to real data. Processing equipment used for theimplementat ion was a Digital Equipment Corporation PDP-l11/55 with floating pointhardware, 256 kilobytes of memory, a 176 megabyte storage disk, 128 kilobytes
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Z - 2. . . .
. . . . . . . .7
of fast access disk emulator storage, an array processor which had not beenimplemented in software, 7 and 9 track magnetic tape capabilities, and a
* graphics terminal with hard copy. The first problem in the hardwareimplementation of the process was the limited memory available for the dopplerhistory data which the algorithms operated upon. Since the fast access disk
* emulator storage was too limited to contain all of the data, it was necessaryto store it on the substantially slower disk, and frequently swap smallportions of it in and out of main memory as the processing algorithms required
* them. This continuous swapping, in addition to the time required forprocessing of the algorithms and the time needed to conformally map the data tothe spectral domain, further made the entire process intollerably slow. Themajor drawback of the process, however, was the frequent failure to obtain theproper spin frequencies. Intensive manual analysis of the process and resultsshoved the failures to be primarily due to the assumption that candidate spinreturns will appear symmetrically about zero doppler. In the simulationsconducted at UCLA, this was not a problem as the simulated doppler historieswere produced noncoherently, thus insuring symmetric representations. For thereal data used in this application, however, such was not the case as it isprocessed coherently. Attempts to modify the process and remove the symmetryrequirements of candidate spin representations showed insufficient improvementin proper spin frequency identification.
While the process developed at UCLA appeared inadequate for the problem athand, it did demonstrate that available memory and speed of conformal mappingwere problems to be reckoned with. Indeed, any tracking algorithms developedwould need conformally mapped data to operate on, as well as a place to store
* it. In order to speed up the mapping process, implementation of the arrayprocessor was investigated. The result of the effort was the development ofsoftware which performed Fast Fourier Transformations (FFT) through a series ofsubsequent calls to array processor subroutines. The software also used thearray processor to compute the doppler power spectrum. This software is listedin Appendix A as subroutine FFT2. While benchmark speed tests of the softwareshowed that, after initialization, the array processor performed the required
* function more than 25 times as fast as the main processor, there were stilladditional drawbacks associated with its use. The first drawback was the 16
* kilobyte main memory requirement for array processor software storage. Thesecond drawback was the fact that if the software was swapped out of mainmemory or windowed out of the 64 kilobyte execution window in main memory, the
* array processor would need to be reinitialized before its next use. This was amajor problem in that initialization, along with the performance of only one
* FFT by the array processor, took three times as long as performance of the sameoperation by the main processor. In other words, the advantage of using thearray processor lies only in the performance of many operations betweeninitializations. Thus, utilization of the array processor for increasedconformal mapping speed placed even greater restrictions on already inadequate
* executable memory. It is worthy of note that a 16 bit processor such as thePDP-11/55 has an instantaneous execution window of only 2 1r' or 65,536 bytes of
* main memory. Further, while memory mapping allows this segment to be splitinto as many as eight subelements anywhere within the 256 kilobyte totalallocation at any one time, the subelements must be multiples of 4096.
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-- % 7 * *. .. . ..
This, then, defines the upper subelement, array procassor memory requirement of16 kilobytes, where a kilobyte is defined to be 210 or 1024 bytes.
With even more stringent requirements placed upon available main memory, due toarray processor overhead, emphasis at this stage of the research was placedupon requirements for storage of doppler history data. While reviev ofprocedures used at UCLA to obtain spin frequencies shoved little promise ofsolving this problem, a look at the manual preanalysis normalization proved tobe of great value. The process first involved precise doppler alignment, towithin a few millimeters per second, of the subject target's base. Next,because of the relatively large amplitude of the base return to that of thefins, the spectra were high pass filtered, to remove base effects and bring upspin representations, for easier identification in tracking. At this point itwas noted, while observing the analyst perform the manual tracking procedure,that the spin representations often had the largest amplitude. Utilizing thisinformation, it was decided to try storing only a small number of the largestremaining returns in each spectrum as an information base of candidate spinreturns. First, each spectrum was further high pass filtered, up to theminimum '4 fs requirement in this effort, i.e., ±4 fs min - ±4 x 5 Hz - t20 Hz.This had the effect of remmving any additional undesired returns of largeamplitude in this interval from consideration. Next, thirty was chosen a thenumber of peaks with largest amplitudes to be considered as candidate spinreturns in each spectrum. Thirty vas chosen simply as a worst case guess,based on observation of data at hand. However, after analysis of candidatepeak selections for different sets of data, it was discovered that manycandidates often described the same peak. This was primarily due to the factthat the peaks were not of infinitesimal width. Consider Figure 16, selectionof candidate spin returns, which denotes (as circled) the five largestamplitudes of twenty. While these points indeed represent the largestamplitudes, in reality the dominant peaks are those indicated by verticalarrows. After only short-term manual analysis, the solution to the problemappeared obvious. The peaks represent points at which the slope changes frompositive to negative with left to right taken as a positive direction.Utilizing this fact, points of positive-to-negative slope change are firstselected as precandidate spin returns. In the case of Figure 16, element 1
numbers 3. 7, 9, 11, 14 and 18 would be chosen. The largest of these wouldthen be chosen as candidate spin returns. Again, in the case of Figure 16,this would correspond to elements 3, 7, 11, 14, and 18 for the five largestelements. Further, trials of this algorithm were then run on the dataselected, with analysis of results showing that (in general) selection of onlythe 16 largest peaks gave rise to a sufficient candidate peak base forsubsequent tracking. Storing only the amplitude and location of 16 points foreach spectrum reduced storage requirements by 97 percent and allowed theutilization of only main memory, as opposed to main memory and the slow accessmass storage media. The selection of 16 points per spectrum is based on thefollowing assumptions:
" Try to keep the data base small without losing too muchinformation on the spin frequency lines.
" Try to reduce the number of noise peaks in the data base.
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. . *S
After reviewing several sets of data it was found that, usually, the maximumunwrapped multiple is the '16 f,-,; however, not every multiple shows in eachspectrum. Therefore, 16 points per spectrum proved to be a reasonable tradeoffbetween information content and memory restrictions.
In order to further reduce the congestion of main processor memory, it wasdecided to make this portion of the process separate from the actual spinfrequency tracking and computation. This had the advantage of removing theoverhead software that would need to be stored in main memory to concatenatethe two processes, and allowed for the independent creation (from raw data) ofdata bases to which tracking a spin frequency computation algorithms could beapplied. The complete software development to perform this process is listedin Appendix A as main routine PEARS1, with associated subroutines FFT2, PICK,SORTAG. Also included in this process is subroutine COW ER, which will bedescribed later, at the point of its development in this research.
AUTOMATIC TRACKING ALGORITHM DEVJELOPMENT
With the establishment of a satisfactory data base, the next step in theresearch was to develop tracking algorithms which duplicated the expertanalyst's approach. The expert analyst, however, has the advantage of beingable to visually locate the spin frequency traces while surveying the entiredoppler history plot. Working with a considerably more limited data base, thisluxury was not available. Therefore a method had to be devised which wouldproperly provide initial frequency identification.
Initially, the first spectrum was simply searched for the return of largestamplitude and its location assigned to the 4fs spin multiple. This selectionwas based on the assumptions that, generally, only multiples of 4fs would becontained in the data base, with th~ose in the lower portion of the spectrumcontaining the most power. Trial runs on real data were then made to check thefrequency selections. Results showed that, except in cases of very clean data,undesired returns were often selected due to clutter and spin return fading inthe spectrm. In order to compensate for the problem it was decided to makethe program interactive and query the user. The selected return and itslocation are presented to the user, along with the query, if it corresponds toany spin multiple. If the user response is positive, then he is asked to enterthe corresponding multiple. If the user response is negative, then he is askedto calculate and enter the initial frequency.
Once a method of obtaining the initial spin frequency was established, theactual tracking algorithm development began. The location in the firstspectrum of the 16fs multiple is computed and the location of each of theeight candidates for that spectrum is checked for a match, to within ±2 Hz. Ifno match is found, the location of the -16fs multiple is checked for a match,and so on, successively utilizing the +12fs, -12fs, +8fs, -8fs, +4fs, -4fslocations, until a match is found. Once a match is found, the spin frequencyfor that period is computed by dividing the location of the multiple by that.multiple. Higher multiples that do not alias are searched first, since for aconstant error in actual peak location division of a spin multiple by a higher
9
multiple minimizes spin frequency error. The 4 Hz window, used for the search,was chosen as the basis of careful hand analysis of doppler history data. Thiswindow generally seemned wide enough to accommodate for rate of change of spinfrequency when used in conjunction with extrapolation techniques, and stillminimize the presence of unwanted returns in the window. The candidate searchfrequency for the second window is taken to be the same as for the first, whilethe candidate search frequency for the third spectrum is a linear extrapolationof the frequencies found for the first and second spectra. Successive spectrause a three-point linear least squares extrapolation of frequencies found forthe previous three spectra. This procedure was tested on several sets of veryclean data and was shown to produce extremely good results. However, whensubjected to data which contained much noise, fading, and/or aliasing of spinlines, the procedure frequently failed. More work was needed to overcome theseproblems.
* Analyzing results after using the tracs~er in data that faded, showed that whenunable to pick any frequencies, track was lost. To correct this problem, it
* was felt that more interactivity between the user and the program was needed.* Therefore an algorithm which checked for the absence of candidate peaks was
implemented. If no peaks are found for multiples of the calculated candidatespin frequency, the progrm then asked the user to enter the spin frequency forthat particular spectrum. This approach solved the problem but also introducedanother. When fading occurred for large periods, the user is queried much too
* frequently to supply the correct spin frequency. Since it is not necessary tocalculate a spin frequency for every spectrum (as interpolation could be usedafterward) selective processing was implemented in the algorithms. In thisapproach the user selects the portions of data he wants to process, leaving outthose where fading is severe. This new approach also has the advantage ofleaving out those portions where noise obscures the spin frequency lines.Another problem that arose when testing these algorithms occurred at thecrossover point of aliased spin frequency lines. Analyzing the data it wasfound that, when crossing occurred, the tracker found at least two peaks in thewindow; and if the rate of change was small, it sometimes lost track. Sincethis problem does not occur often in each run, the algorithm was modified toprompt the user to select between the candidate peaks. The selection is keptas simple as possible, such that just a quick glance at the doppler plot willusually suggest to the user which is the correct peak to select. Thisapproach also solved the problem of spurious noisy spikes in the search windowby the spin frequency peaks. Finally, as a check to the spin frequenciesobtained, the algorithms were further modified for selective application to thedata in reverse order. Results of forward and backward processing could then
* be checked by the user for consistency.
Testing of the sets of data selected was performed and the results obtainedagreed with the results an experienced analyst would have obtained by doing itmanually. These results are shown in Appendix B.
For Data Set 1, the two problems encountered are noise and fading. Noise,being especially severe at the beginning and at the end of the run, will beavoided by skipping processing in these areas. Fading is more severe from 55to 77 seconds; therefore this part is not processed. Once the run is completed
10
-~~. . 1.*
in the range from 10 to 55 seconds, the results shown are satisfactory and theyagree with the results obtained by manual calculation of the spin frequencies.
Also in Appendix B, runs on Data Sets 2 and 3 are shown. Data Set 2 shovs somefading, and Data Set 3 shovs crossover of spin lines. Both runs weresuccessful, and the results shown agree with manual calculations performed onthe doppler plots.
KNOWLEDGE BASED SYSTEM DEVELOPMENT
Since the problem of noise, fading, and crossover of spin multiples oftenrequire more experienced analysis, consideration was given to making thedeveloped software more 'user friendly' for easier application by a more noviceanalyst. After detailed analysis of the entire process, it was decided thatimplementation of a software superstructure, based onua generalized knowledge-based system (KBS), would normally produce better results[41, [51 The processwas, therefore, broken down into LBS's three primary elements.
* The interface.* o The cognitive engine.
* The knowledge base (as seen in Figure 17).
*The interface, as seen in Figure 18 (which breaks down into external data,user, and expert interface), primarily functions as the user's two-way
* communication link to the expert knowledge modules and fact files whichcomprise the knowledge base. The external data interface is first used tocreate the fact files, where the current information to be processed is stored.
* The user interface then utilizes statistical and expert information, stored in* the expert knowledge modules, to guide th~e user step by step through the
process. These modules contain statistical averages from previously successful* reductions, suggested defaults, and descriptions of what is happening at every
stage of the reduction. Further, each user input is parsed and analyzed for* content so that part or all of this information is available even though a
numeric input is requested. This algorithm is listed as subroutine CONVER inAppendix A. Statistics on the current reduction are also available to theuser, and are used to update the permanent statistics if tne user feels theprocess was successful. Finally, the expert user interface duplicates the userinterface, except for the capability of altering the expert knowledge module.
The cognitive engine, as seen in Figure 19, is the active processing elementcontaining the generator and evaluator functions, as well as the inference andreasoning algorithms that interact with the current problem state. Candidatespin returns are first generated as the largest returns in a given line, where
* the number generated is generally equal to the number of spin traces that do* not alias. As mentioned previously, this function is based on the heuristic
that most spin multiples present will probably constitute the larger returns,with most power contained in the lower, unaliased multiples. The evaluatorfunction then works like the analyst, using a small search window to locate aspin return. The window center is first extrapolated to the expected areas of
the largest multiples, on each side of the spectrum, and then to successivelower multiples if a peak is not found. Problems of clutter, aliasing, andfinding no peaks are then handled interactively through the use of theinference and reasoning algorithms. For example, if no peak (or more than onepeak) is found, tracking is halted and the user is made aware of the problemand the location. The user then has the option of allowing the process to makeits best guess, based on the current state of the problem, or to enter anoverriding location. Statistics are also compiled on the number and nature ofsuch interruptions, for the purpose of later advising the user in the eventresults are unsatisfactory. For instance, finding multiple peaks more oftenthan not might indicate that a smaller search window would have greatersuccess. In the event that an inexperienced analyst has exhausted theresources of the process and is still unsatisfied, presentation of results andstatistics to a trained analyst can usually gain an expeditious solution.
CONCLUSION
The resulting product of this research contains many heuristic-based features,used by trained analysts in processing spin information, including forward,
* backyard, segmented processing, and extraneous frequency rejection. It is* worth noting that, collectively, it has made a significant advance in obtaining
spin information for the subject target. The process successfully tracks thespin frequency through fading, clutter, and the alig3ing of higher spinmultiples back onto lover spin multiples with better than 90 percentreliability in routine reductions. Primarily, this was achieved by placing thetechniques of the highly trained analyst into the process and at the immediatedisposal of the inexperienced analyst. Routine processing of these parameterscan now be performed in loe than a tenth of the time previously required by atrained analyst. The analyst's capability to update the expert knowledgemodules also reduces future reduction time by making inexperienced analystseven less dependent on their presence.
12
* - .,.. -I.- ---,
IS... A ,._= . . . -
I ,-- - -.-"i°--
,-=. - -- *-.-- _ _ - - .--.. , -
w.- . ..- --.-. ,,. ___...___ -e-& -
-__a -____-so -o -too t -
______.-rHZ
4 --. 0- -
R-DET - HZS
Figure 1. lypical doppnler history plot.
13
TV TV
,, ,,p ,,R , ,,,,,,*. ,*, o.. . , "_, ., o. ,, . . ' .. "
.. ., .- ,. .. - * -. -. - -. . . .. .-.-C . -
=_ ° ': " ' ,-' ' '_ ',t '*_ ' ,* -': ,-- '{,, ", ,' ' ' 7, " : ,, _" _, , . . .. _, r'*,, -. .- . -. ;. ,. , ''" .'-,-,1P ,,1_ *_z
900 900
900 900
Figure 2. Target scattering center nrientat ion.
S%.1
=t€,. .r , ,' , .'.r , . € . . ;. ,..* .;% • . ;; ."- . . . -" . .".".".. "." '. %.• . . . • "d . .' . ."
-q-j--j ' r ~ r ~ r.--*
- - RADAR LINEOF SIGHT
Figure 3. Target aspect angle.
15
/'
/ -
//
//
/A
Figure 4(a) . Sin)gle scattt ring vnter. .
DOPPLER HISTORY PLOT FOURIER TRANSFORM SPECTRAL CONTENTOF INSTANTANEOUS SPIN WINDOW SIZESPECTRAL CONTENT
_j ±12 CYCLE _
I CYCLEw
CYCLE L
NEGATIVE POSITIVEFREQ. FREQ.
7f S- THE MAXIMUM FREQUENCY DEVIATION OFTHE SPIN MODULATION
Figure 4(b). Relationship of spectral content to Fourier transform size.
17
; ' ' - ' "'.. l .,. .. ..- . " P . . '. " . ". . . -. - . . . . . . . . . . . . ..
"S2
........
S.U
-10 -100 -50 FRG0 - so 10o ISO
R-00ir -/ A,'
Figure 5(a). Doppler history plot with Fourier transformwindow equal to 1/5 cycle of spin.
18
-w- _7 ______________W _____W; __; W
O 10 -0 5 0S 0 D
LaO H
I I II I II I IIr I II II ~
44
191-150 -100 -50 REG HZ o LO IS
-T T =--*..*-.1. 1 11 ,1 11 11 1 '111
IP
a; 0 -0 -O05 10 S
420
~ -K~K~ :*-.~*~. ~ **** . -
-I50 -100 -50 0 so LO0 L50FREQ - HZ
R-OOT - P/S
Figure )(d). Doppler history plot with Fourier transformwindow equal to 1/5 cycle of spin.
21
- , 2. . .. . .. . ..:
44
-10 -100 -50 RE 0 SO Hoo so LOso
42 0 -2 -R-OCIT - M/S
Figure 5(e). Doppler history plot with Fourier transfnrmwindow equal to 1/5 cycle o~f spin.
22
IV 77 70
M iO -a 5 05 0 5
FRQ H
-150 ~ ~ ~ -~ -10 -50s/105S
Figure 5(f). Doppler history plot with Fourier transformwindow equal to 1/5 cycle of spin.
23
4- 5
* ~~ -I'iI -. -..
IF S -1. -W. MW0 50-N
InI
-- 2 - -- ----
24P
.. . . . . . .I-1
-150 -ICI -50 FREG0 - Z 0 LCO . S
go-
15 10 -0 0 so LOD L50
150 1 -sa FRED - lIZ
P-r1Mr -/
Figure 5(h). Doppler history plot with Fourier transformwindow equal to 1/5 cycle of spin.
25
......
5,Z
-IO -0 5 RG Z 5 U S
.5.rI I I I I I I I I I I 1 1 1
r - _
R-OT- /
Fiue5i. Dplr itr ltwt Fu irtasoi
widweult /5cceo pn
J2
nzr l9 ' ' P 'WP -P --P~ -. "W fl 57 N'TP TK 791'WJI Truq-%^ FWIK -A MA ' .sTv7 w W-WT W11I]W- -W7~~
C! O -0 5 05 0 S
FRL H
W 0; - -
0;0T- /
Fiue ). ope itr ltwt ore rnfr
a idweult /5cceo pn
027
'IN
'pD
a--
.1k.
FrQ - i
* 'I 20 -COOC-OT
FLCr () (oprtitovpo I
widwvua o15 ''
* 28
M.PM PWIF 1-4 e -RTriV I M q1111107 .5.
-'5
50 100 16
.4 ..-2 .... ..
R-,OT,0M/
Fg r () ope itr ltwt ore rnfr
29
-ISO -100 -50 a soFREO HZ
ti 2-O 0- -2
Figure 5(m). Doppler history plot with Fourier transformwindow equal to 1/5 cycle of ,'pin.
30
4~~~. ............- -
-150 -100 -50 0 50 100 ISOFREG - KZ
Q-0T 0- 14/S
Figure 5(n). Doppler history plot with Fourier transform* window equal to 1/5 cycle of spin.
31
-150 -100 -s0o 50 100
42 0 -2R-DOa - MfS
* Figure 5(o). Doppler history plot with Fourier transformwindow equal to 1/5 cycle of spin.
* 32
% p ~ .. -* *
II-
-150 -100 .. S..........[.FRCD H
...i .. l ....... l i 1 1 1 1 i i I I 1 1
Go 0-2-r4M/
Fi u e 5pc D p l r h soo l t w t o r e r n f rwidweult /5cceoypn
r33
11 11 1 .- [[I--~ -- II- 1111122- K ' . Ii I 111111i 111 11 1 11itII
........
4 9-.
r. C;Q - i
kii to1 2ROt 2-- M/
Figure S q) liopp er h i.....plo..ith..................rlwidweul o15ccl t-i
.1in
1- 1 , 1 1 11.. 11 111f~ rT T T
34o LO S
-10 -100 -50 0 so 100 LSOFREG - HZ
2 0 -2 'R-OOT - M/S
Figure 5(r). Doppler history plot with Fourier transformwindow equal to 1/5 cycle of spin.
35
Uv
Si
-10 -0 5 05 0 5
-100 -50 FREG a HZ s O 5
42 0 -2 'R-00T - M/S
Figure 5(s). Doppler history plot with Fourier tranf;formwindow C(jlIal to 1/5 cycle of spin.
36
-ISO -100 -50 0 50 to0 L50FREO - K2
II 2 0-2 -R-OOT - M/S
Figure 5(t). Doppler history plot with Fourier transformwindow equal to 1/5 cycle of spin.
37
in
-18 -100 -so 0 50 LOD ISOFREG HZ
14 2 0 -2 -4-OT- N/S
Figure 6 (a). Doppler history plot with Fouirier transformwindow equal to 2/5 cycle of spin.
3b'
nrE -W-r IV I-IZ-W~v
C39
I0
-IO -0 5 RO Z s O S
440
'VW.W.,~ k04 R2 0- T, -UI IINT
-150~ -10 -5 0 so LOO 15
Fiur 6() Dope hitr ltwt.ore rnfr
II
Tlil I M fITI 1 1 . I'l fill11111 1111 11111111.1. 1
-IO 10 50 FRG0 HZ so LO S
R-DOT -M/S
Figure 6(e~). D)oppler history plot with Fourier transform
window equal to 2/5 cycl oLf sp~in.
42
- - - - - - .4 . 4~In -
In
-150 -i.........FRM- i 1 5
111111111111I.1 Ion20 ?-
I-IT /
Fiue6f. DplrhCoy ltwt ore rnfrwidweult /5cceo pn
Ln43
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445
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I I I ITlI It I I 111 I I I I li1111111111111 I-ISO 210 -?0a s O SF-RzE - pvs
FhIlrv 00i) . D~oppler history plot wit ii 1ourfur tt.inst.,11:iwindow equal to 2/5 cycle of ,pin.
46
77-77-7771 "TM 7
zI
zA
-1f-0 5 aSn0 SLRG- I
I C)-7
a :-~J*.xx*. .x *.**j* *.* *~***j*~**~tun)***
-- L -- .- b. . . . . ** ' 'I' ja .r ,
An
-150 -100 -50 0 50 t00 ISOFREQ - HIZ
II2 0 -?2-R-001* - M/S
Figure 7 (a) .Doppler hi srory plot Withi Fouri[er t ratisto~rmwindow equal to 4/5 cycle of spin.
48
U
-150 -Ica -50 0 50 L00 I50FREO - HZ
U4 2 0 -2 -4R-Oar - M/S
Figure 7(b). Doppler history plot with Fourier transformwindow equal to 4/5 cycle of spin.
49
* - ~ V x - ~ .-. .. . . ....................
-4t
......... ............. _ _ _ _
- HZ
711F111 fa [I rII (lufffIt If It r-a ---- Ia I( lI
* ~~~F igmeri 7(c). Uopp Ier IIIS~ ory p Lot wi Lli Fur iear I rans formnwindow equal1 Lo 4/5 cyclec of :;pi it
50
i II 111111 oil 1515I 1111111111 111111
-150~100 -10 -5 a fOSS
Figure 7(d). Doppler history p lot with Fourier transform
window equal to 4/5 cycle of spin.
51
,F~F~E -F -- A-
100 2 5 F 0 -2 H-soI0i5
R-0OT -M/S
Figure 7(e). Doppler history plot with Fourier transformwindow equal to 4/5 cycle i4 -spIin.
52
0Wr
INI
-ISO -10 -0 FEa HZ so LO 5
R-00T -M/'S
Figure 8(a). Doppler history plot with Fourier transformwindow equal to 8/5 cycle of spin.
53
-' S. ~ % . .-.........
III111lit11 1111 1 1 1 1 1 1 1 11 11 11 1 11 I
42 0 -2 '-I- tR-0T - M/S
* Figure 8(b). Doppler history plot with Fourier transformwindow equal to 8/5 cycle of spin.
54
-150 -100 -50 a so 1O0 ISOFREG - lIZ
II2 -OT0 -? -U
Figure 8(c). Doppler history plot with Fourier transformwindow equal to 8/5 cycle of spin.
55
* 4 ...
~U~.~ N IU~ U ~ .........................~ ~ ~ . 4 ..........
-150 -100 -50 0 s o SFREG - HiZ
I 1 11 II I 1 1 11111 1 1.1 1 1 1 1 1 1 1 1 1 1 1 I IrT- T
IL 2 0 -2 -4RO - 14/S
Figure 9(a). Doppler history plot with Fourier t~aIlSfOrMwindow equal to 16/5 cycle of spin.
56
I-T TF T I i t II I I I Ii~j I11 I-I50 -100 -50 so5 100 ISO
FREG N Z
IL2 0-2 ---IO M/S
Figure 9(b). Doppler history plot with Fourier transformwindow equal to 1615 cycle pf spin.
57
A-l
a I -- A
I I I II A
I-01 - -------- -- -
Figre 0.Dopderhitor plt iihFouie trinsoA
wifl(1OW~~~~~ eq At 325c leo p
4A58
C 7-
Aa -
C3
F RA . l
4 2 0 -20 -u 0 0 S
P-DOT - MIS
Figure 11(a). Doppler history plot with Fourier transform windowequal to 32/S cycle of spin and lag equal to
* 16/S cycles of spin.
59
* , .. .**. . . - - *, '. o, o d . . . l . . ",
ui
-ISO -100 -so oRGaM so LOD 150
1 1 1 1i 1 1 1[j~ 1 1 1 1 1 1 1 1 f l I'42 0-O /
F igure 11(b). Doppler history plot with IJourier tr'an-sform windowequal LU i~2/:) CYCIc. of :,p6i WaIU ialv cquai1 to10/5 cycles of spin.
60
R-O ---------
61
~ . ~ *-.**.*~ -. :< *.8<7
-LS 2 0 -so a-ooo LS
R-DOT - MIS
4 1:iglI~c 12(b), D~opple r history plot I'It'sc~Ijt atji ye f-104
62
A
A -
f A
11111~~~[il t11 111 11 111 11 11 I i I I I I lIII!
14 2 0 -2 -U
Figure 13. Doppler history plot illus'trative of fading of spin traces.
63
')A-
I 64
I? * -A A * '. .* * ' .
16 fs 4 fs12 ts8fS
/8 f 8fs 12 f S
4 fj-16fs
fS 9.37
AT5
II>
-15 ....- 500.....
FREO - l7
Figre 5. anul nalsisof doplr hstoy pot
1 L s 6365
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-.. to
00
41
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REFERENCES
1. Nunn, Elwin C. "The US Army White Sands Missile Range Development of TargetMotion Resolution," EASCON 1980 record, IEEE Electronics and AerospaceSystems Convention, IEEE Publication 80CH1578-4AES, 1980. ISSN: 0531-6863.
2. Taub, Herbert and Donald L. Schiling, Principles of Communication Systems,
McGraw-Hill, Inc., New York, NY, pp. 1-42, 113-155, 1971.
3. Hynes, John N., "A Spectral Peak Tracker," Technical Report STEWS-ID-81-1,
STEWS-ID, White Sands Missile Range, New Mexico, January 1981.DTIC No. B057745L.
4. Rebane, George J. "Recommendations for Application of Knowledge Based systems
Technology to Target Motion Resolution," Report No. 308-1, IntegratedSciences Corporation, Santa Monica, California, June 1979.
5. Jackson, Philip C., Introduction to Artificial Intelligence, Petrocelli Books,New York, NY, pp. 94-102, 1974.
70
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AD-RI71 20 SPIN FREQUENCY DETECTION IN THE SPECTRRL. DONAIN(U)
VUHITE SRNDS NISSILL RANGE NM INSTRUMENTATIONDIRECTORATE D S JIMAREZ MAR 66 STERS-ID-96-1
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DISTRIBUTION LISTNo. of
Organization Copies
STEWS-AG-AS-AM ..................... 1
STEWS-NR-A ........... ...................... 1
STEWS-NR-D ........ ...................... ..
STEWS-USAISC .......... ..................... 2
STEWS-PL ......... ....................... . .1
STEWS-TE-TL .............. ...................... 2
STEWS-ID-A ........... ...................... 1
STEWS-ID-P ........... ...................... 1
STEWS-ID-T ........... ...................... 1
STEWS-ID-D ........... ...................... 1
STEWS-ID-E ........... ...................... 1
STEWS-ID-O ........... ...................... 1
CommanderU.S. Army Materiel CommandATTN: AMCRD5001 Eisenhower AvenueAlexandria, Virginia 22304 ..... ................ 1
CommanderU.S. Army Materiel CommandATTN: AMCAD-P5001 Eisenhower AvenueAlexandria, Virginia 22304 ..... ................ 1
CommanderU.S. Army Test and Evaluation CommandAberdeen Proving GroundMaryland 21005 .......... .................... 2
CommanderU.S. Army Test and Evaluation CommandATTN: AMSTE-RUAberdeen Proving Ground, Maryland 21005........ 2
CommanderU.S. Army Electronics CommandATTN: AMSEL-RDFort Monmouth, New Jersey 07703.....
Director of Research and DevelopmentHeadquarters, U.S. Air ForceWashington, DC 20315 ...... .................... 1
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Distribution List (cont)
DirectorU.S. Naval Research LaboratoryDepartment of the NavyATTN: Code 463Washington, DC 20390. .. ........ .......
CommanderAir Force Cambridge Research CenterL. G. Hanscom FieldATTN: AFCSBedford, Massachusetts 01731 .. ............
CommanderU.S. Naval Ordnance Test StationATTN: Technical LibraryChina Lake, California 93555 .. ..... ........ 2
DirectorNational Aeronautics and Space AdministrationATTN: Technical LibraryGoddard Space Flight CenterGreenbelt, Maryland 20771. ...... ......... 2
CommanderAir Proving Ground CenterATTN: PGBAP-1Eglin Air Force Base, Florida 325412.......... 1
CommanderPacific Missile RangePoint Mugu, California 930411 . .. .. .. .. .. .. 1
Commanding OfficerNaval Air Missile Test CenterPoint Mugu, California 93011.. ..... ........ 2
Office of the ChiefResearch and DevelopmentDepartment of the ArmyWashington, D. C. 20310 .. ......... ...... 3
Commanding OfficerU.S. Army Electronics CommandMeterological Support ActivityATTN: Technical LibraryFort Iuachuce, Arizona 856,13 .. ..... ........ 2
Commanding OfficerU.S. Army Ballistics Research LaboratoriesAberdeen Proving Ground, Maryland 21005 ....... 1
136
Distribution List (cont)
Commanding OfficerU.S. Army Research Office - DurhamBox CM, Duke StationATTN: Internal Research DivisionDurham, North Carolina 27706 .. ..............
Command erAtlantic Missile RangePatrick Air Force Base, Florida 32925 .. ........ 1
Commanding OfficerU.S. Army Aviation Test ActivityEdwards Air Force Base, Caliornia 93523 .. ....... 1
AdministratorDefense Technical Information CenterATTN: DTIC-DDABCameron Station, Bldg 5Alexandria, Virginia 22314. .. ......... ... 12
137
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