基于压缩感知的信源个数与信号参数估计算法

信源个数与信号参数估计是盲信号处理的关键环节，对后续信号的侦察处理意义重大。针对当前盲信号信源个数与信源参数估计研究割裂的问题，提出了一种联合估计算法。通过分析信号的稀疏系数在不同测量矩阵相同稀疏字典下位置相同的特点，提高了信源个数和信号参数的估计精度，实现算法的自适应控制；通过数理分析确定了多级搜索策略的最优级次，大大降低了稀疏字典的原子数目。仿真结果表明：算法在一定信噪比下能够实现信源个数和信号参数的有效估计；信源个数和信号参数的估计精度随着压缩比的降低而逐渐提高，随着信噪比的提高而逐步增强；噪声对信源个数和信号参数估计精度的影响很大，尤其是低信噪比下；第2个信源载波频率和调频斜率的估计误差明显高于第1个信源参数的估计误差。

Estimation of the Number of Signal Sources and Signal Parameters Based on Compressed Sensing

Estimation of the number of signal sources and signal parameters is a key procedure in processing of blind signals and it is very important for subsequent signal capture analysis. Aiming at the problem of blind number-of-signal-sources estimation and source parameter estimation, a joint estimation algorithm is proposed. Following analysis that found the locations of signal sparse coefficients in different measurement matrices are identical under the same sparse dictionary, the accuracy of estimation was improved, and the algorithm achieved adaptive control. Optimal multi-stage search strategy was determined through mathematical analysis, and it greatly reduced the number of sparse dictionary atoms. Simulation results show that the algorithm is capable of effective estimation of the number of signal sources and signal parameters under certain signal noise ratio; the estimation accuracy of the number of signal sources and signal parameters increased with the decrease of the compression ratio and increased with the increase of the signal noise ratio; noise has a great influence on the estimation, especially under low signal noise ratio; estimation error of carrier frequency and chirp frequency of second source is significantly higher than the first source.