基于压缩感知的DSSS信号双阶段捕获方法

Two-stage acquisition algorithm for DSSS signal based on compressive sensing

针对直接序列扩频(DSSS,Direct Sequence Spread Spectrum)信号的捕获性能与硬件消耗或计算复杂度的相互制约问题,基于压缩感知理论,提出了一种双阶段压缩捕获方法,第1阶段进行快速粗捕获,第2阶段在第1阶段基础上实现精确捕获.首先研究DSSS信号的相关域稀疏性,构造了稀疏变换矩阵;然后利用确定性沃尔什-阿达马矩阵,分别构造了2个阶段压缩测量矩阵及其检测算法;最后从检测概率和平均捕获时间两方面对提出算法的捕获性能进行了理论分析,并用蒙特卡罗法进行了验证.理论分析和仿真实验表明,该方法能够在显著降低相关次数的前提下,达到传统基于并行相关方法的捕获性能水平.

According to the restriction between the acquisition performance and the hardware consumption, a two-stage compressive acquisition method was proposed based on compressive sensing, specifically some coarse acquisition values can be got by the first stage, following by accurately detecting the signal in the values by the second stage. Firstly, the sparsity of DSSS signal in the correlation domain was explored, so as to build the sparsity transformation matrix. Secondly, the measurement matrixes and detection algorithms in both stages were produced according to the deterministic Walsh-Hadamard matrix. Finally, the acquisition performance of the provided method was analyzed theoretically in terms of the detection probability and the mean acquisition time, which was also verified by Monte Carle method. The theoretical analysis and the simulation results show that the novel method can use much less correlations to achieve the same performance as conventional parallel correlation-based methods.