现有单快拍条件下子空间类时延估计算法由于降低有效带宽导致估计精确度下降, 多快拍条件下压缩感知类算法由于多次采样具有相似稀疏结构也导致了性能下降。针对以上条件下2种算法鲁棒性不强的问题, 提出一种在单快拍与多快拍条件下均具有较高精确度的基于压缩感知子空间的时延估计算法。该算法首先判定快拍数与多径数的关系, 在快拍数大于等于多径数时通过求解谱峰的目标函数得到时延估计, 在快拍数小于多径数时先重构得到改进的噪声子空间, 再求解谱峰的目标函数得到时延估计。仿真结果表明, 该算法在单快拍与多快拍条件下具有较高的估计精确度, 与子空间类算法和压缩感知类算法相比具有更好的鲁棒性。

The estimation accuracy of the subspace algorithm has dropped off because the effective bandwidth has reduced under the condition of single snapshot. The accuracy of the compressed sensing algorithm has declined because of the similar sparse structure under the condition of multiple snapshots. To deal with the weak robustness of existing algorithms, a high-performance time delay estimation algorithm is presented based on compressed sensing subspace under both conditions. The proposed algorithm derives the relationship between the multipath number and the number of snapshots. An improved noise subspace has been reconstructed. The polynomial is applied to estimate time delay. The polynomial rooting avoids the spectral peak searching and reduces the computational complexity. Simulation results show that the proposed algorithm has a high performance and a better robustness compared with subspace algorithms and compressed sensing algorithms.