Research On Blind Algorithm Based On Signals' Cyclostationary

Many man-made signals in communications, radar, sonar and other fields' exhibit cyclostationarity. Especially in many kinds of communication signal, most of them have the property. Recently, by using the cyclostationarity of signals, the research on improvement of signal processing performance attracts much attention in array signal processing. Because these methods do not need not only the DOA of the signal's and the array manifold but also signal waveform, the blind beamforming algorithms using cyclostationarity signal's properties are the genuine blind algorithms.In this dissertation, the blind beamforming algorithms based on signal's cyclostationarity are studied. Based on several typical blind beamforming algorithms, some improved algorithm are presented. The main contributions of the dissertation are as follows:1. An algorithm for matching the ESPRIT's estimation of two-dimensional angle by using twice SVD and once SCHUR is proposed. Using two row uniformity sensor-arrays and adding one sensor, the algorithm decomposes the array twice and exploits rotational invariance properties of signal space, so that both angles may be computed via matrix pencil method, and these estimates are automatically paired. Simulation results are presented to verify the efficacy of the proposed algorithm.2. Several typical blind beamforming algorithms are presented, such as CAB, C-CAB and ECAB and so on. These algorithms are compared on the aspects of convergence speed, signals' magnitude, and DOA of the interference. The computation and robustness of these algorithms are analyzed in theory.3. Cycle frequency-based blind beamforming shows the performance degradation due to CFE (cyclic frequency error). An improved algorithm is presented in literature 6, which adopting forgetting factor in estimation cyclic correlation matrix would largely depress the sensitivity of CAB to cyclic frequency error. By using this method, the improved algorithms of the C-CAB and ECAB algorithms are presented in this dissertation. Especially forgetting factor ECAB has excellent performance under large cyclic frequency error. At the same time, some problems of performance analysis in literature 6 are rectified.4. It is analyzed that the algorithms of direction finding using signals' cyclostationarity have improved the property of direction finding on the aspects of signal selectivity, resolution and overload ability. Simultaneously, it is discussed that N-"false snapshot" has effect on the direction finding' accuracy of SC-SSF algorithm.5. The conjugate cyclic ESPRIT with minimum-redundancy linear-arrays extendsthe aperture, having higher resolution, and estimating more sources with fewer sensors. It is analyzed that the independence and mean of the signals affect the performance of this algorithm. Computer simulations verify the effect of the theoretical analysis.