Cyclic Code Shift Keying A Low Probability of Intercept Communication Technique

Fig. 1. Typical CCSK base function b.These equations are then applied to the CCSK andTCCSK systems to evaluate their vulnerability todetection.II.CYCLIC CODE SHIFT KEYINGCCSK uses discrete cyclic shifts of a base functionf(t) to modulate a carrier, with each shift representingB bits of information. The receiver determines theshift by computing the cyclic correlation of the basefunction f(t) with the received signal plus noise. Thebase function we consider here is a binary sequenceb =(b 0 ,b 1 ,:::,b M1 ) T with b m = 1, resulting inbiphase modulation of the carrier. A typical sequenceb isshowninFig.1.Theelementsofb indicated inFig. 1 are called “chips.” Each chip is of duration T Cand the “symbol” duration T S is MT C .Wedefinethechip bandwidth as W C =1=T C ,sothatT CW C =1.We consider three different methods for generatingb. The first uses an MLS of length M =2 k 1, whichhas the property that its cyclic autocorrelation has apeak of M and sidelobes of 1. Unfortunately, inthis case the number of bits B is less than k. Also,M is not a power of two, thus processing by usingthe FFT algorithm may be precluded. To alleviatethese problems, an MMLS (also called an “extendedm-sequence”) [3] is used. In this case an MLS isgenerated and a 1 or+1isinsertedtoextendthelength to M =2 k . A result of this modification is anincrease in the level of the autocorrelation sidelobes,compared with the true MLS. We show later that thisincrease has little effect on error probabilities becausethe level of the input signals of interest is so lowthat the error process is controlled by the noise. Thisfact leads us to consider a third option in which b isobtained by generating a random binary sequence of1 s.A. Noncoherent CCSK Using Binary SequencesThe cyclic shifts of the code b are designatedb 0 ,b 1 ,:::,b M1 ,whereb 0 = b, andb n is the nth shift.We consider the case where it is reasonable to assumetime synchronization so that the receiver knows thetime of arrival of each “symbol” to a small fractionof one chip interval. We also assume that the carrierfrequency is known. However, the relative phase ofthe received signal is not known, and noncoherentprocessing is required. We assume the received signalis a sum of signals of the formr(t)=A cos(! 0t + ' mn + µ)+g(t) (1)where A is the amplitude of the received signal,' mn =0or¼ is determined by the mth element ofthe transmitted code b n , µ is the unknown phase, andg(t) is a Gaussian noise process with mean zero and(one-sided) power spectral density N 0=2. A quadrature(or baseband) detector is used and its sampled outputis a column vectorr = Ab n exp[jµ]+g: (2)In (2), b n is a column vector of the shifted codeelements, g is a column vector of independent andidentically (IID) circular Gaussian noise with variance¾ 2 ,andA is the signal amplitude. The receivercomputesS m = b T m r = AbT m b n + bT mg (3)for m =0,1,:::,M 1. If maxS m = S p , the bitscorresponding to the pth symbol are output. A symbolerror occurs if p = n.B. Comparisons with M-ary Orthogonal SignalingIn conventional MOS, one of a set of Morthogonal, equal-energy signals is used to representB =log 2M bits of information. One form of thereceiver correlates the received signal plus noise witheach of the M reference signals and determines whichhas the maximum correlation. Although a single cycliccorrelation is performed in implementing CCSK,the process can be viewed as the performance of Mseparate correlations, as implied in (3). If the cyclicshifts of the code were all uncorrelated, then CCSKwould be a special case of MOS. However, for codesthat provide LPI, the cyclic shifts are correlated; thatis, the cyclic autocorrelation function has non-zero“sidelobes.” We show that the bit error rate (BER) forCCSK can be approximated by using the equations forMOS, even though the cyclic shifts are correlated.C. Performance EstimatesThe performance of noncoherent MOS in terms ofBER is well documented in the literature [5, p. 489].For M orthogonal signals, the probability of a symbol(or word) error is given bywhereP S =1 0exp[(x+q)]I 0 ( 4qx)H(x)dxH(x)=[1 e x ] M1(4a)(4b)q = E S =N 0 is the ratio of symbol energy to noisepower spectral density, and I 0 (x) is the modifiedBessel function of the first kind and order zero. Byusing the binomial expansion of H(x) and integrating(4a) term-by-term, a finite series for P S is obtained[5, p. 489]. However, the series is alternating in signDILLARD ET AL.: CYCLIC CODE SHIFT KEYING 787

Fig. 2. Comparison of CCSK performance with MOS, M = 1024.Fig. 3. Comparison of CCSK performance with MOS, M = 4096.and presents numerical difficulties for large M or q.Fortunately, (4a) lends itself to numerical integration,and that is the technique we use for its evaluation.With M =2 k , the probability P B of a bit error isobtained directly from P S as [5, p. 198]2 k1P B = P S2 k 1 : (5)Monte Carlo simulations were performed tocompare CCSK performance with MOS. Someof the results are shown in Figs. 2–4. From theseresults we conclude that (4) can be used as a closeapproximation to the symbol error probabilityfor CCSK when P S is larger than about 10 4 andwhen M 1024. This conclusion is based on thesimulations performed; however, there appears to beno reason to assume that the approximation is notvalid for smaller P S , especially for large values ofM. To corroborate the simulation results, we showthat the cross-correlation between cyclic shifts aresmall and thus are “approximately” orthogonal.Table I shows the maximum cross-correlation fork = 9,10,:::,16 for MLS, MMLS, and randomsequences. The MMLS results were obtained by usinga single set of shift-register taps for each k to generatethe sequence. Some slight variations are expectedif other sets of taps are used. Note that for k 12,the maximum cross-correlation is less than 0.03 forMMLS and decreases with increasing k. The tabulatedvalue for each random sequence was obtained byaveraging the results from 100 different randomsequences. A comparison shows that the randomsequence has larger maximum cross-correlation than788 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 39, NO. 3 JULY 2003

amplitude of the signal must be increased by a factorof (M=M T ) 1=2 . This is considered later when detectionby a radiometer is discussed.byPMR = M 2 A 2 + M¾ 2QA 2 + M¾ 2 (10)III.CODE SELECTION TRADEOFFSFor CCSK, the information transmitted iscontained in the location of the maximum of thecorrelation function defined by (3) and (6). Thus,a measure of performance that can be used in codeselection is the peak-power to mean-sidelobe-powerratio (PMR). If code b has a significantly higherPMR than code b , then it is intuitive that b willprovide better performance. Another considerationin code selection is their noise-like property. MLSs aresometimes referred to as “pseudonoise” sequences,but they possess certain structure not found in atruly random sequence. In an MLS (with elements1 and 0) of length M =2 k 1, all k-bit binarynumbers except zero appear as k successive elements.For example, one MLS of length seven is b =[1 0 1 0 0 1 1]. Each three successive bits arebinary representations of [5 2 4 1 3 7 6], withthe last two [7 6] determined cyclically. Some ofthis same structure still is retained if an MMLSis used.A. PMRTo simplify the analysis in defining and evaluatingthe PMR of a sequence b, we assume that the phaseof the received signal is known. In this case, thereceived signal plus noise is r = Ab n + g, wheregis a vector of IID Gaussian noise with variance ¾ 2 .Without loss of generality, we may assume that n =0;i.e., assume that b n = b. The correlation of r withb produces M terms. The first term, defined as the“peak,” iss 0 = MA+ h 0 (7)where h 0 is a Gaussian random variable with meanzero and variance M¾ 2 . The remaining M 1terms(the sidelobes) are of the forms m = q mA + h m (8)where q m is the mth signal sidelobe and h m is aGaussian random variable with mean zero andvariance M¾ 2 . We define PMR asE[s0 2 PMR =]AVE[sm 2 ] (9)where AV denotes the average over the M 1sidelobes. Note that we define the peak to be locatedat the position of the shifted code (in this case,zero); however, for low SNR, the actual maximummay occur at some other position (i.e., an erroroccurs). By using (7), (8), and (9), the PMR is givenwhere Q is the average sidelobe power of the code b.Equation (10) can be written asPMR =1+MSNR IN1+QSNR IN=M(11)where SNR IN = A 2 =¾ 2 is the input SNR.Fig. 5 shows PMR versus SNR IN for an MLS,MMLS, and a random binary (RANDOM) sequence.Note that the MLS curve is basically a unity-slope lineup to about 20 dB input SNR. What is most striking,however, is the fact that the three curves are nearlycoincident for negative input SNR, which is the usualcase for use in CCSK. Also, it is obvious from thefigure that a unity-slope line provides an excellentestimate of PMR for the region of interest (negativeinput SNR). Further results in the next figure illustratethese points.The PMR versus SNR IN for M =2 10 ,2 13 and 2 16is shown in Fig. 6 for a random sequence and anMMLS. Note that the three pairs of curves followthe unity-slope line up to SNR IN of about 5 dB.Anexception is the curve for 2 10 when SNR IN is lessthan about 20 dB, the region where the positionof the peak correlation is likely to be determined bynoise. That is, the peak does not necessarily occur atn =0.B. PMR for TCCSKWhen TCCSK is used it is necessary to modify(11) to compute the PMR. Because only M T chips ofthe code are transmitted, the peak correlation is givenbys 0T = M TA + h 0T (12)where h 0T is a Gaussian random variable with meanzero and variance M T¾ 2 . Similarly, the sidelobes are ofthe forms mT = q mT A + h mT (13)where q mT is the mth signal sidelobe and h mTis Gaussian with mean zero and variance M T¾ 2 .Therefore, the PMR of the truncated sequence isPMR T =1+M T SNR IN1+Q T SNR IN =M T(14)where Q T is the average sidelobe power of thetruncated code.Fig. 7 shows PMR T as a function of the inputSNR for M =2 13 and M T =2 13 ,2 12 ,and2 11 .(WhenM T =2 13 , there is no truncation.) This shows thatPMR T decreases by about 3 dB when M T is decreasedby a factor of two. However, this fact is misleading790 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 39, NO. 3 JULY 2003

Fig. 5.PMR versus input SNR for CCSK.Fig. 6.PMR versus input SNR for CCSK.when error performance is considered. To maintainthe same error performance, E B=N 0 must be keptconstant when M T is decreased; that is, the input SNRmust increase. Fig. 8 shows PMR T as a function ofE B=N 0 over the range of 0 to 20 dB. Note that forsmall E B=N 0 ,PMR T is essentially the same for bothtruncated and nontruncated sequences. In fact, asshown in the next section, the range of interest forE B=N 0 is about 0 to 5 dB, and over this range PMRand PMR T differ by only a fraction of a dB.C. Random versus Pseudorandom CodesBy observing graphs of the probability of a biterror P B versus E B=N 0 (e.g., [5, Fig. 10-6]) we see thatfor 10 k 20 and 0:00001 P B 0:1, the requiredE B=N 0 is less than about 4.5 dB. Also, E B=N 0 (in dB)is given byE B=N 0 =SNR IN +10log 10M 10log 10k (15)which means that SNR IN is less than about 15:6 dB.(For TCCSK, E B =N 0 is given by (15) with M replacedby M T .) The data in Fig. 6 show that the PMR forMMLS and a random sequence is essentially thesame over the range of SNR IN of interest. Therefore,random sequences can be used as the code in CCSKwith little effect on performance compared withMMLS.Some caution must be used in generating a randomsequence for the CCSK code. For example, althoughDILLARD ET AL.: CYCLIC CODE SHIFT KEYING 791

Fig. 7.PMR Tversus input SNR for TCCSK.Fig. 8.PMR Tversus E B=N 0for TCCSK.the sequence is called “random,” it is likely to beobtained by using a random number generator thatis actually “pseudorandom.” Thus the quality of thegenerator used must be ensured [6]. (The randomcodes used in Figs. 5 and 6 were generated by usingthe Matlab R routine “rand.”) Additionally, somerandom codes may have unacceptable properties.For example, it may be necessary to ensure that thenumbers of 1 s and 1 s are approximately equal toavoid having a dc offset.IV.VULNERABILITY OF CCSK TO RADIOMETERDETECTIONA radiometer (or energy detector) is often the mosteffective device to detect spread-spectrum signals [7].It is conceptually a simple device, and requires onlya few assumptions to be made about the structure ofthe signals being detected. Invulnerability to detectionby a radiometer is required if a communication systemis to be considered LPI. Fig. 9 is a simplified blockdiagram of a radiometer.A. Equations for Evaluating PerformanceEquations for evaluating radiometer performanceare given in [7] and are based on derivations byUrkowitz [8]. We assume that the noise at theinput to the radiometer is a zero-mean, stationary,Gaussian random process that has a flat, bandlimited(one-sided) power spectral density N 0=2 over thebandwidth W of the bandpass filter. For convenience,792 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 39, NO. 3 JULY 2003

Because E S=N 0 = E B=N 0 +10log 10k,E R=N 0 = E S=N 0 +10log 10TW 10log 10M: (20)Fig. 9.Radiometer block diagram.we consider a normalized detection statisticV =2V=N 0 . Urkowitz shows that the probabilitydistribution of V is closely approximated by thechi-square distribution with 2TW deg of freedomin the noise-only case and by the noncentralchi-square distribution with 2TW deg of freedomand noncentrality parameter ¸ =2E R =N 0 =2S in thesignal-plus-noise case. The parameter E R is the totalsignal energy integrated by the radiometer and N 0 isthe noise power spectral density. For simplicity, weassume that TW is an integer.Detection is accomplished by comparing thenormalized radiometer output V with a threshold K,and a signal is claimed present if V K. IfV Kwhen no signal is present, a false alarm occurs. Bymaking appropriate changes of variables in the centraland noncentral chi-square distributions, and definingK 0 = K=2, the equations for the probability of a falsealarm PFA and the probability of detection P D aregiven by [7, pp. 56–57] u TW1 e uPFA =du (16)K 0(TW 1)!and u (TW1)=2eP D =(u+S) IK 0STW1 (2 uS)du(17)where I TW1 (x) is the modified Bessel function of thefirst kind and order TW 1. These equations wereused to evaluate the detectability of the CCSK biphasemodulated waveforms considered here.B. Radiometer Detection of CCSKTo simplify the analysis, we assume that theradiometer has the same gains and losses as theintended communications receiver, and is located atthe same range. As a result of this assumption bothhave the same SNR IN ,sothatE R = E S when T = T S .We also assume that the radiometer integration timevaries in units of the chip time T C . Then, becauseT CW C = 1, the radiometer TW product is just thenumber of chips integrated. Also, E R =N 0 (in dB) canbe expressed asE R=N 0 =SNR IN +10log 10TW: (18)By using (15) and (18), we have the following relationbetween E R=N 0 and E B=N 0 :E R=N 0 = E B=N 0 +10log 10TW 10log 10M +10log 10k:(19)If q symbols are transmitted contiguously, then(18)–(20) apply when TW qM. IfTW > qM, thenthe radiometer parameters are TW and E R=N 0 =E S=N 0 +10log 10q.When the symbol error probability P S and thenumber of bits k are specified, the required E S=N 0for CCSK is determined by solving (4). Equation(20) is then solved for S = E R=N 0 and (16) and (17)are used to determine the detection probability P Dachieved by the radiometer for a given PFA. Resultspresented later take into account the effect of holdingthe false-alarm rate (FAR) a constant.We first assume that the radiometer integrates overT S seconds, the length of one CCSK symbol, withthe integration interval matched to the symbol. Thisresults in E S = E R and radiometer time-bandwidthproduct TW = M. Table II shows the detectionprobability P D for the indicated number of bits k,when P S =0:001 and PFA = 0:0001.The data in Fig. 10 exhibit further the vulnerabilityof CCSK to detection by a radiometer when TW = M.Three sets of curves are shown for three choices ofPFA. Within each set, the symbol error probability P Svaries from 0.00001 to 0.1. From these sets of curvesit is obvious that vulnerability to detection decreaseswith M, but increases with increasing radiometer PFAor with decreasing symbol error probability P S .Alsoincluded is the spectral efficiency R=W (in bits persecond per hertz of bandwidth) [8, pp. 282–284],which is a measure of performance of modulationmethods. For this case, the spectral efficiency is givenbyR=W = log 2 MM : (21)Note that the vulnerability to detection increases withincreasing spectral efficiency. Also, the values of PFAused in Fig. 10 are likely to be larger than would beused in practice. However, the trend is obvious: afurther decrease in PFA will lead to a decrease in P Dwhen other parameters remain the same as used in thefigure.We now assume that symbols are transmittedcontinuously (and are contiguous). An assumptionmore realistic than the one above (TW = M) is that theradiometer integrates over multiple symbols. In thiscase, its probability of detection increases comparedwith the single symbol case. Conversely, if theradiometer integration time is less than the length ofone symbol, the probability of detection is decreased.This is illustrated by the data in Fig. 11, which showsthe probability of detection by a radiometer as itstime-bandwidth product (i.e., integration time) varies.For each k (k = 10, 12, 14, and 16) results are shownfor a range of values of P S , and the circles representthe results given in Table II. Note that P D increasesDILLARD ET AL.: CYCLIC CODE SHIFT KEYING 793

Fig. 10.Spectral efficiency and radiometer detection of CCSK.Fig. 11.P Dversus TW for radiometer detection of CCSK.TABLE IIProbability of Detection by Radiometer with Integration Time T SNumber of Requiredbits, k E B=N 0(dB) E S=N 0(dB) P D10 3.97 13.97 0.0017012 3.56 14.35 0.0005114 3.24 14.70 0.0002416 2.97 15.01 0.00016considerably with increasing TW and decreasingP S for k =10andk = 12. However, for k =14andk = 16, the variation of P D with TW and P S is small.Thus, to simultaneously achieve LPI performance (lowP D ) for small values of P S requires the use of longsequences with lowered spectral efficiency R=W.C. Radiometer Detection of TCCSKWhen TCCSK is used, the equation for E R=N 0 isgiven by (19) with M replaced by M T :E R=N 0= E B=N 0+10log 10TW 10log 10M T+10log 10k:(22)We first assume that the radiometer integrates over thetruncated symbol length; i.e., TW = M T . Results areonly given for k =16(M = 65536), and illustrate theeffect of truncation on vulnerability to detection by aradiometer.Fig. 12 shows P D versus the truncated symbollength M T for the case where TW = M T . Three sets ofcurves are shown for three values of PFA. Within eachset the symbol error probability varies from 0.00001to 0.1. Also shown is the spectral efficiency. The794 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 39, NO. 3 JULY 2003

Fig. 12. P Dand spectral efficiency versus truncated symbol length M T.Fig. 13.P Dversus TW for radiometer detection of TCCSK.results in Fig. 9 and Fig. 11 show that the detectabilityof TCCSK increases slightly compared with CCSKwhen the transmitted symbol lengths are the same.Also, TCCSK slightly increases the spectral efficiencyfor small symbol lengths. This occurs because thenumber of bits per symbol is a constant for TCCSK(16 in the case shown), but varies for CCSK. Again,the values of PFA used are likely to be larger than inpractice, but the trend is obvious.Fig. 13 shows P D versus TW for M =2 16 and forM T =2 10 ,2 12 ,and2 14 and PFA = 0:0001. For eachchoice of M T , curves are shown for a range of valuesof P S . We again assume that symbols are transmittedcontinuously (and are contiguous). A comparison ofFig. 13 with Fig. 11 shows that, for the same symbollength (i.e., M T =2 k ), the detectability of TCCSKincreases considerably compared with CCSK forM T =2 10 , but only slightly for longer sequences.D. FARFor the detection results presented above, theradiometer PFA is held constant. It is usually morerealistic to hold the FAR constant, especially whencomparing systems with different integration times.We define the FAR as the average number of falsealarms per second (or some other unit of time). Itseffect on detectability is discussed in detail in [7] andis summarized here.We assume that the integrator shown in Fig. 9operates in the integrate-and-dump mode. That is,DILLARD ET AL.: CYCLIC CODE SHIFT KEYING 795

Fig. 14.P Dversus TW for CFAR radiometer detection of CCSK.the integrator output is sampled every T seconds, theintegrator is reset to zero, then integrates over thenext T seconds, etc. Thus, a decision is made everyT seconds and the false-alarm rate is FAR = PFA=T. Ifa fixed FAR is required and we compare two systemswith integration times T 1 and T 2 (T 1 = T 2 ), we mustuse a different PFA for each system. This is trueeven if the bandwidths of the two systems are equal.Conversely, if the integration times of the two systemsare equal, the FAR is independent of their bandwidths.However, detection performance does depend on theirbandwidths because (16) and (17) depend on the TWproduct.Fig. 14 illustrates the effect a constant FAR(CFAR) has on radiometer detection of CCSK andshould be compared with Fig. 11, in which a constantPFA (CPFA) is assumed. In Fig. 14, FAR = 10 7and is expressed in units of T C ; thus, PFA increaseslinearly with FAR. The results for both cases (CFARand CPFA) are identical for TW = 1024; however,for TW > 1024, P D is larger for CFAR and forTW < 1024, P D is larger for CPFA. These situationsoccur because P D increases with increasing PFA(and vice versa) when TW and E R=N 0 are constant.Also, because P D is bounded below by PFA and PFAincreases with increasing TW, P D approaches 1.0asymptotically as TW increases. This effect is evidentin the results shown in Fig. 14.Although not shown, similar results are obtainedfor detection of TCCSK by a CFAR radiometer. Thatis, if the results in Fig. 13 for CPFA were comparedwith the corresponding results for CFAR, the sameconclusions made just above would apply.V. CONCLUSIONSA low probability of intercept (LPI)communication technique known as CCSK has beendescribed and discussed. This technique uses cyclic(circular) shifts of a base function f(t) to modulatea carrier. The function f(t) has the property that itscyclic autocorrelation has a distinct peak and lowsidelobes. The receiver estimates the position of thepeak correlation of the received signal plus noise withf(t). If there are M resolvable positions the estimatedposition represents B =log 2 M bits.The base functions considered are binarysequences of +1 s and 1 s, which results in biphasemodulation of the carrier. Three different methods forgenerating these sequences were evaluated in terms oftheir PMR. The first is an MLS of length M =2 k 1,which has the property that its cyclic autocorrelationhas a peak of M 1 and sidelobes of magnitude1. Because the MLS only represents k 1bits,anMMLS was considered. The MMLS is obtained byappending a +1 or 1 toaMLS,thusresultingina sequence of length 2 k that represents k bits. Boththe MLS and MMLS have well-defined structure andthis led to the consideration of a random sequence oflength 2 k . Results showed no significant difference incommunication performance when comparing the useof an MMLS with a random sequence.Simulation results were obtained to show that thatthe performance of CCSK in terms of probability ofsymbol error P S and required E B=N 0 can be measuredby using equations that apply to MOS. Also, it wasshown that the receiver signal processing for CCSKis simpler and easier to implement than for MOSbecause only one cyclic correlation is computed forCCSK.A generic radiometer system was defined andequations for evaluating its detection performancewere given. These equations were applied to evaluatethe detection of CCSK and TCCSK. Results givenfor various combinations of parameters show that796 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 39, NO. 3 JULY 2003

to achieve LPI performance, transmitted symbols oflength greater than about 2 12 are required. The use ofTCCSK results in a data rate higher than CCSK, butwith a penalty of higher detectability. The tradeoffsbetween detectability and data rate for a particularapplication must be made on a case-by-case basis.However, we conclude that CCSK and TCCSKprovide LPI capabilities and are techniques that aresimple and easy to implement.REFERENCES[1] Endsley, J. D., and Dean, R. A. (1994)Multi-access properties of transform domain spreadspectrum systems.In Proceedings of the 1994 Tactical CommunicationsConference, Vol. 1, Digital Technology for the TacticalCommunicator, 1994, 505–506.[2] Dixon, R. C. (1994)Spread Spectrum Systems with Commercial Applications(3rd ed.).New York: Wiley, 1994.[3] Fiebig, U. C. G., and Schnell, M. (1993)Correlation properties of extended M-sequences.Electronic Letters, 29, 20 (Sept. 1993), 1753–1755.[4] Sklar, B. (1988)Digital Communications Fundamentals and Applications.Englewood Cliffs, NJ: Prentice-Hall, 1988.[5] Lindsey, W. C., and Simon, M. K. (1973)Telecommunication Systems Engineering.Englewood Cliffs, NJ: Prentice-Hall, 1973.[6] Park, S. K., and Miller, K. W. (1988)Random number generators: Good ones are hard to find.Communication of the A. C. M., 32, 10 (Oct. 1988),1192–1201.[7] Dillard, R. A., and Dillard, G. M. (1989)Detectability of Spread Spectrum Signals.Norwood, MA: Artech House, 1989.[8] Urkowitz, H. (1967)Energy detection of unknown deterministic signals.Proceedings of the IEEE, 55, 4 (Apr. 1967), 523–531.[9] Proakis, J. G. (1995)Digital Communications (3rd ed.).New York: McGraw-Hill, 1995.George M. Dillard (M’67—SM’69—LSM’00) was born in Little River, TX onNovember 12, 1931. He received the A.B. and M.S. degrees in mathematics fromSan Diego State College, San Diego, CA in 1959 and 1962 and the Ph.D. ininformation and computer science from the University of California at San Diegoin 1971.Since April 1959, he has worked as a mathematician at the Space and NavalWarfare Systems Center, San Diego (SSCSD) and it predecessor organizations.His primary research activities involved the application of distribution-freeand nonparametric statistics, sequential analysis, detection theory, and otherstatistical techniques to signal detection for radar and communication systems.He also participated in programs that included radar signal processing, ECCMtechniques for radar, inverse synthetic aperture radar, and the technical evaluationof surveillance systems developed for the Navy. His recent research has been inevaluating the detectability of spread-spectrum signals and he is the coauthorof one book and author or coauthor of several papers in that area. He retiredfrom the Federal Service in January 1991, but has continued part time work asa reemployed annuitant in the SSCSD Joint and national Systems Division.Michael Reuter (S’82—M’86) received the B.S. and M.S. degrees in electrical engineering from the Universityof Illinois, Urbana-Champaign, in 1984 and 1986, respectively, and the Ph.D. degree in electrical and computerengineering from the University of California, San Diego, in 2000.From 1987 to 2002 he was with the Space and Naval Warfare Systems Center, San Diego, CA, where hisresearch interests were in adaptive and statistical processing applied to problems in wireless communications.Since 2002, he has been with the Motorola Automotive and Electronics Systems Group, Deer Park, IL.DILLARD ET AL.: CYCLIC CODE SHIFT KEYING 797

Brandon Zeidler is currently a Ph.D. student at the University of California, SanDiego. He earned his M.S. degree in electrical and computer engineering with anemphasis in communications theory and systems from University of California,San Diego, (UCSD) in 2002. He attended the University of California, SantaBarbara (UCSB) with a four year, full-tuition Regents scholarship and completedhis B.S. degree in electrical and computer engineering in 1999.He has worked two consecutive summers at four high-tech companies in SanDiego: Alliant Techsystems, Digital Transport Systems, TRW Avionics SystemsDivision, and SPAWAR Systems Center.Mr. Zeidler’s involvement in undergraduate research was recognized witha President’s Undergraduate Research Fellowship and a scholarship from theNational Society of Professional Engineers. He graduated Cum Laude andwas selected as the student speaker for the UCSB College of Engineeringcommencement ceremony. At UCSB he served as president of the EngineeringStudent Council and the Eta Kappa Nu honor society, and as an officer for TauBeta Pi. He currently serves as an officer of the Graduate Student Council atUCSD.James R. Zeidler (M’76—SM’84—F’94) has been a scientist at the Spaceand Naval Warfare Systems Center, San Diego, CA since 1974. Since 1988,he has also been an adjunct professor of electrical and computer engineeringat the University of California, San Diego. His research interests includecommunications signal processing, adaptive signal processing and arrayprocessing applied to wireless communications systems.Dr. Zeidler was an associate editor of the IEEE Transactions on SignalProcessing from 1991 to 1994. He received the Lauritsen-Bennett award forachievement in science in 2000 and the Navy Meritorious Civilian Service Awardin 1991. He was a corecipient of the award for the best unclassified paper at theIEEE Military Communications Conference in 1995.798 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 39, NO. 3 JULY 2003