A low probability of intercept (LPI), or low probability of detection (LPD) communication technique known as cyclic code shift keying (CCSK) is described. We discuss the basic concepts of CCSK and describe a system based on the use of random or pseudorandom codes for biphase modulation. We use simulation to show that the bit error rate (BER) for CCSK can be closely estimated by using existing equations that apply to M-ary orthogonal signaling (MOS). Also, we show that significantly fewer computations are required for CCSK than for MOS when the number of bits per symbol is the same. We show that using biphase modulation results in waveforms that have a large time-bandwidth product and very low input signal-to-noise ratio (SNR) and thus inherently have an LPI by a radiometer. We evaluate detection by a radiometer and show that LPI can be achieved by using codes of lengths greater than about 2 12 (i.e., by transmitting more than about 12 bits per symbol). Results illustrate the effect that the CCSK symbol length and error probability, and the radiometer integration time and probability of false alarm (PFA), have on detection by a radiometer. We describe a variation of CCSK called truncated CCSK (TCCSK). In this system, the code of length 2 k is cyclically shifted, then truncated and transmitted. Although shortened, the truncated code still represents k bits of information, thus leading to an increased data rate. We evaluate radiometer detection of TCCSK and it is shown that the probability of detection is increased compared with the detection of CCSK.

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Analysis of the performance of a mean-level threshold in the detection of nonfluctuating signals is performed. Formulas for the probability of detection are derived and a simple recursive method that can be used for computations is described. Binary integration is discussed, and it is shown that the loss in sensitivity due to the use of an adaptive threshold followed by binary integration is only a fraction of a decibel when compared with optimum binary integration. Binary integration results are given for both fluctuating and nonfluctuating signals.The detection of signals in a background of stationary noise usually involves the comparison of a statistic, based on samples of signal plus noise, with a constant threshold that is determined from the noise-only probability distribution. The threshold is chosen so that a specified false-alarm probability is achieved, using the so-called Neyman-Pearson criterion [1].Unfortunately, in most cases the threshold depends on parameters of the noise-only distribution and, in practice, these parameters are actually unknown. If these parameters can be estimated from the available data, then the threshold can be determined (at least approximately) from the estimated parameters.In applications such as a radar operating in a land and sea clutter background, the background noise is nonstationary. Thus, the parameters of the distribution of the noise are not only unknown, but also vary with time. Distribution-free detection procedures [21 are applicable in such a situation; also, adaptive procedures can be applied. The procedure that will be discussed here is an adaptive procedure which determines the detection threshold from the data. Such a procedure is described by Steenson [31, where the detection of a fluctuating signal (Swerling case 2 [41 ) is discussed. This paper extends Steenson's results to the detection of a nonfluctuating signal. It is shown that the single-pulse detection probability for a nonfluctuating signal in stationary Gaussian noise can be written in terms of Poisson and negative-bionomial random variables, and that a simple recursive method can be used for computations. Results are given that show the degradation in performance as compared with the optimum procedure.Binary integration is discussed, and it is shown that the loss in sensitivity due to the use of an adaptive threshold followed by binary integration is only a fraction of a decibel when compared with optimum binary integration. Binary integration results are given for both fluctuating and nonfluctuating signals, with the results for fluctuating signals based on Steenson's analysis [3].Only the application to a short-pulse noncoherent radar will be discussed explicitly; however, the results are also valid for an angle-scanning CW radar and for a coherent pulseDoppler radar.Aerospace and Electronic Systems, vol. AES-1 0, no. 6, November 1974.The adaptive detection procedure determines the detection threshold adaptively by estimating the mean level of the "recent" clutter plus noise. The mean-level ci...