A Study On Inteception And Analysis Of Direct-Sequence Spread-Spectrum Signals

Direct-Sequence Spread-Spectrum (DSSS) signal has low probability of interception, anti-jamming capability and other advantages. It has widely been used in military and civil communications and many other applications. With unknown pseudo-noise (PN) sequence and other parameters, i.e., under non-cooperative context, the interception and analysis of DSSS signals is challengeable, especially under low signal-to-noise (SNR) scenarios.Although in the past thirty years the study of non-cooperative DSSS signals has witnessed many developments in interception and analysis techniques of the DSSS signals, not so much in theoretic investigation and with new measures. However, some important problems have not been solved satisfactorily. For an example, with conventional spectral correlation theory, the detection and estimation of pseudo-noise (PN) code period, which is very important to analyze the DSSS signals, has not yet been studied rigorously. Another example is that performance analysis of the methods developed in blind channel identification has seldom been evaluated comprehensively under the non-cooperative context.These problems are comprehensively studied from theoretical viewpoint. Other spread-spectrum signals such as long-code DSSS and the Tamed ones are considered as well. The main contributions of this dissertation include some aspects as follows:1. For the first time, optimal detection of the DSSS signals with unknown parameters is rigorously discussed to detect and estimate the PN code period of short-code DSSS signals. With the Gaussian mixture model, the uniform most powerful invariant test and some sub-optimal invariant tests are derived. The results show the connection between the invariant tests and the multi-cycle detectors. Proposed nocoherent weighted multi-cycle detectors can be used as the performance upper-bound of all detectors based on second-order cyclostationary statistics. In the same time, proposed asymptotic locally most powerful invariant (ALMPI) test outperforms the multi-cycle detectors in the situation of finite samples and without obviously increasing computational complexity. Based on the ALMPI test, a new PN code period estimator is proposed, which needs no artificial interpretation compared with conventional approaches.2. Simutaneously from two viewpoints, signal interception and blind channel identification, estimation of the DSSS signals is reviewed. The connection and difference between two viewpoints are spotted. When used for the estimation of short-code DSSS signals, the algorithm of blind channel identification shows to be inherently not robust even knowing the correct channel filter order (or the effective order). The principle to balance the channel matrix is proposed, and a novel MPP (maximize the product of eigenvalues) algorithm is used to solve the problem.3. Estimation of information-code-width of long-code DSSS signals is analyzed. The results show that suppressing the interference by the PN code period is crucial and existing approach is PN code-dependent and has poor performance at low SNR. Therefore, a PN code-independent information-code-width estimator based on difference PN code despreading is proposed and demonstrates good performance at low SNR.4. Complete study is made for the estimation of long-code DSSS signals. Using the stochastical and the deterministic models for aperiodic long-code DSSS signals, an iterative low-SNR unconditional maximum likelihood estimator and an eigen-composition algorithm for the missing-data model are proposed, respectively. Both estimators exploit the weighted low-rank approximation optimization and the latter algorithm unifies the aperiodic long-code DSSS signals and the short-code ones, which is almost optimal in the sense of the second-order statistics of received samples. The deteministic complex exponential basis expansion method for time-varying single-input-multi-output (SIMO) blind channel identification is used to estimate periodic long-code DSSS signals under multi-path scenarios and the performance is compared with conventional eigen-composition algorithms often used in the interception and analysis context.5. Estimation of the Tamed DSSS signals is also investigated in the interception and analysis context. The identificability of M-ary orthogonal spreading spectrum (MO-SS) signals is investigated and a special phenomenon of delay ambiguity is observed. Then a blind synchronization algorithm is proposed and the Expectation Maximization (EM) algorithm is used to estimate the PN code of MO-SS signals. Moreover, an estimator based on the autocorrelation-like matrix is proposed for CCSK (Cyclic Code Shift Keying) signal, which shows obvious perfromance improvement at low SNR cases.