使用复杂独立分量分析的弱同频道干扰通信信号的提取
Independent Component Analysis (ICA) has largely been applied to the biomedical field over the past two decades and only recently extended to the processing of complex non-circular sources. The feasibility and performance of complex ICA to extract a weak co-channel interfering communications signal from a television broadcast signal is investigated in this thesis. The performance of three algorithms, complex maximization of non-Gaussianity (CMN) by Novey et al., RobustICA by Zarzoso et al., and complex fixed-point algorithm (CFPA) by Douglas, over varied interference-to-noise ratios (INR) for a fixed signal-to- interference ratio (SIR) is obtained by simulation. The communication signals examined for the weak interferer are binary phase-shift keying (BPSK), four- level rectangular quadrature amplitude modulation (4-QAM), and 16-level rectangular quadrature amplitude modulation (16-QAM), and the television broadcast signals are North American standard, Advanced Television Systems Committee (ATSC) and European standard, Digital Video Broadcasting - Terrestrial (DVB-T). Improved performance and sensitivity to the prewhitening step present in the ICA implementations are shown as the number of sensors increases.
使用循环功能的通用通信和脉冲压缩雷达波形的调制自动分类
This research develops a feature-based MAP classification system and applies it to classify several common pulse compression radar and communication modulations. All signal parameters are treated as unknown to the classifier system except SNR and the signal carrier frequency. The features are derived from estimated duty cycle, cyclic spectral correlation, and cyclic cumulants. The modulations considered in this research are BPSK, QPSK, 16-QAM, 64-QAM, 8- PSK, and 16-PSK communication modulations, as well as Barker coded, Barker coded, Barker coded, Frank coded, Px49 coded, and LFM pulse compression modulations. Simulations show that average correct signal modulation type classificationC > 90is achieved for SNR > 9dB, average signal modulation family classificationC > 90is achieved for SNR > 1dB, and an average communication versus pulse compression radar modulation classificationC > 90is achieved for SNR > -4dB. Also, it is shown that the classification cation performance using selected input features is sensitive to signal bandwidth but not to carrier frequency. Mismatched bandwidth between training and testing signals caused degraded classification cation ofC 10- 14over the simulated SNR range.