基于渐进添边的准循环压缩感知时延估计算法

A quasi-cyclic compressed sensing delay estimation algorithm based on progressive edge-growth

针对时延估计问题中压缩感知类算法现有测量矩阵需要大量数据存储量的问题,提出了一种基于渐进添边的准循环压缩感知时延估计算法,实现了稀疏测量矩阵条件下接收信号时延的准确估计.该算法首先建立压缩感知与最大似然译码之间的理论桥梁,然后推导基于低密度奇偶校验码的测量矩阵的设计准则,引入渐进添边的思想构造具有准循环结构的稀疏测量矩阵,最后利用正交匹配追踪算法正确估计出时延.对本文算法的计算复杂度与测量矩阵的数据存储量进行理论分析.仿真结果表明,所提算法在测量矩阵维数相同的条件下正确重构概率高于高斯随机矩阵和随机奇偶校验测量矩阵,相比于随机奇偶校验矩阵,在数据存储量相等的条件下,以较少的计算复杂度代价得到了重构概率的较大提高.

Time delay estimation （TDE） is a hot research topic in wireless location technology. Compressed sensing （CS） theory has been widely applied to image reconstruction and direction of arrival estimation since it was proposed in 2004. The sparse model can be constructed in time domain for estimating the time delay by using the CS theory. The measurement matrix plays a crucial role in the processing of signal reconstruction which is the core problem of CS theory. Therefore the research in the measurement matrix has becomes a hotspot in recent years. The existing measurement matrix is mainly divided into two categories, i.e., random measurement matrix and deterministic measurement matrix. The performance of random measurement matrix has bottlenecks. Firstly, because of the redundant measurement matrix data, the generation and storage of the random number put forward a high requirement for hardware. Secondly the random matrix can only satisfy the restricted isometry property in a statistical sense. The research of the deterministic measurement matrix is of great value under this background. The parity check matrix of low density parity check （LDPC） code has good performance in CS theory. However, the method of randomly selecting non-zero element position has a certain probability to generate a measurement matrix with a short loop structure during generating LDPC code measurement matrix. The robustness of the reconstruction performance decreases with the increase of iteration times. A novel quasi-cyclic CS algorithm based on progressive edge-growth is constructed to estimate the time delay. The purpose of this article is to deal with the need to store a large number of data in existing measurement matrix during time delay, by using the CS theory. The algorithm presented here can achieve TDE in a high precision. First, the theoretical bridge between CS and the maximum likelihood decoding is established. And the design criterion of measurement matrix based on the LDPC code is derived. The sparse measurement matrix with quasi-cyclic structure is constructed by introducing the idea of progressive edge-growth. Finally, the orthogonal matching pursuit algorithm is used to estimate the time delay. Furthermore, the computational complexity of the algorithm and the data storage of the measurement matrix are analyzed theoretically. Simulations show that the correct reconstruction probability of the proposed approach is higher than those of the Gauss random matrix and random LDPC matrix under the same dimension. Compared with the random LDPC matrix, the proposed method can improve performance at the expense of less complexity under the condition of the same data storage.