信号捕获是全球导航卫星系统(GNSS)信号接收的关键步骤，其搜索过程运算量较大。压缩感知可以减少捕获的运算量，但对捕获性能有一定影响。为提高压缩感捕获算法的性能，在GNSS信号稀疏性的基础上，构造基于奇异值分解的高斯测量矩阵，该测量矩阵比传统高斯测量矩阵具有更好的非相关性和重构性能，利用该矩阵进行基于压缩感知的捕获算法仿真。仿真结果表明，与传统高斯压缩感知捕获算法对比，改进算法在较低信噪比情况下捕获概率有明显提升。
邓乐乐
,
周方明
,
赵璐璐
,
梁广
,
余金培
. 基于奇异值分解的压缩感知GNSS信号捕获算法[J]. 中国科学院大学学报, 2023
, 40(1)
: 128
-134
.
DOI: 10.7523/j.ucas.2021.0031
Signal acquisition is the key step of GNSS (global navigation satellite system) signal reception, and its search process is computationally expensive. Compressed sensing can reduce the amount of computation, but it has a certain impact on the acquisition performance. In order to improve the performance of compressed sensing acquisition algorithm, based on the sparsity of GNSS signal, a Gaussian measurement matrix based on singular value decomposition is constructed. Compared with the traditional Gaussian measurement matrix, the constructed measurement matrix has better performance of non-correlation and reconstruction. Simulation results show that compared with the traditional Gaussian compressed sensing acquisition algorithm, the acquisition probability of the improved algorithm is significantly improved in the case of lower signal-to-noise ratio.
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