A Study Of Non-cooperative DS Communication Signal Detection

Direct-Sequence Spread Spectrum (DSSS) communication has been widely applied in military communications and public mobile communications. Before capturing the information carried by cooperative direct-sequence (DS) signals, some important parameters, such as carrier frequency, data rate, and the code pattern of the pseudo-noise (PN) code have to be known. It is very difficult to intercept a non-cooperative DS signal because of its low power spectral density, unknown code pattern of the PN code, and the complex background noise. Therefore, detection and interception of DS signals are always research hotspots. It is of great significance in military communications and public mobile communications to find an effective algorithm for detecting DS signals and recognizing their parameters.According to different background noises, in the thesis we propose several approaches for DS signal detection. Computer Simulations have been made to verify the effectivity of these approaches.To detect the DS signals that are affected only by single-frequency interference or narrow band interference (NBI), a sideband correlation replacement (SCR) algorithm has been introduced in Chapter 2. The SCR algorithm is used on the premise that the frequency spectra of most signals have a symmetrical structure, especially DS signals. If single-frequency interference or NBI is within one sideband of a DS signal, the spectrum of the DS signal can then be captured after replacing the interference with another symmetrical sideband. The Simulation results have verified that the SCR algorithm is effective on the suppression of NBI and single-frequency interference, and has a better performance than the transform-domain interference excise algorithm.However, SCR algorithm cannot suppress Gaussian noise. In Chapter 3 cyclic sideband correlation replacement (CSCR) algorithm is proposed to solve this problem. Gaussian noise and NBI can be suppressed together by using CSCR. The key idea of CSCR algorithm is applying SCR algorithm in the analysis of the cyclic spectrum. To be specific, first Gaussian noise can be reduced through analysis of the cyclic spectrum, because it has no cyclostationary. Then, The NBI is suppressed by using SCR algorithm.SCR or CSCR algorithms will fail to capture the DS signal if there are kinds of interference in the background noise, e.g., the interference has the same carrier frequency as DS signal, or the bandwidth of the interference closed to that of DS signal. A detection scheme that extracts DS signal from the complex background noises is investigated in chapter 4. Different from conventional detection schemes, this scheme has made use of independent component analyze (ICA) algorithm, which can separate the DS signal from kinds of interference, and recognize the type of the modulation and the parameters. In Chapter 4, FastICA and JADE algorithms are also introduced to the detection schemes. These schemes are Real-Time and the signals can be well reconstructed.In practice, signals arriving at the receiver, which could be super-Gaussian, sub-Gaussian and near Gaussian, may have different distribution. Therefore, the ICA algorithm is required to be robust. A DS signal detection scheme based on kernel independent component analyze (KICA) has been presented in Chapter 5. The Simulation results verified that this scheme has the better robustness than those based on FastICA and JADE.