采用去网格相关向量机的水下多径稀疏信道估计

在采用训练序列的水下稀疏信道估计问题中,由于采样过程的存在产生了测量矩阵的网格,所以当多径时延不恰好位于网格上时,传统的有网格方法就无法准确估计出该多径的位置,在网格间距较大时这个问题更加严重。针对这个问题,本文构建了一个去网格的稀疏信道估计模型,这个估计模型的测量矩阵包含了对网格外偏差进行调整的参数,在此基础上进一步采用相关向量机的方法对这个调整参数进行估计,得到网格外的偏差值。针对两种不同的水下稀疏多径信道模型进行的仿真试验证明,此方法相比其它有网格稀疏信道估计算法,在估计误差和误码率上有提升,在网格间距较大时,优势更加明显。

Underwater acoustic multipath sparse channel estimation via gridless relevance vector machine method

In the scenario of underwater acoustic sparse channel estimation with training sequences, grid points in the measuring matrix are caused by discretizing procedure. Estimating accuracy might not be guaranteed with state-of- the-art methods when multipath delays don't exactly locate on the grid points. In this paper, we construct a gridless measuring matrix for sparse channel estimation which contains a off-grid adjusting factor and further using Relevance Vector Machine algorithm to estimate this factor to estimate the offset. This paper first describes the approach and then testifies its estimating result in numerical experiments on two different underwater channels. The results demonstrate that this method outperforms conventional ones in estimating error and bit error rate and this is even more obvious when the grid gets coarser.