非凸稀疏约束的多快拍压缩波束形成

Multiple-snapshot Compressive Beamforming with Non-convex Sparse Constraints

基于极小极大凹惩罚函数约束的压缩感知波束形成,相对于传统l1范数的压缩波束形成来说,可以增强信号的稀疏性,获得更精确的波达方向估计。然而,该算法在强噪声背景下,方位估计结果不稳定。针对这个问题,该文提出一种基于极小极大凹惩罚函数约束的多快拍压缩感知波束形成(MCP-MCSBF)算法。通过多个快拍的联合处理,提供更好的抗噪性能和更精准的波达方向估计结果。仿真结果表明与其他多快拍波达方向估计算法相比,该文算法提供了更优的精确性和更高的角度分辨率,湖试结果则进一步验证了所提算法的有效性。

Compressive beamforming based on the minimax-concave penalty function constraint,compared with the traditional l1 norm compressive beamforming,can enhance the sparsity of the signal and obtain a more accurate Direction Of Arrival(DOA)estimation.However,under the background of strong noise,the azimuth estimation result of this algorithm is unstable.In response to this problem,a Multiple-Snapshot Compressed sensing BeamForming based on the constraint of the Minimax Concave Penalty(MCP-MCSBF)function is proposed.Through the joint processing of multiple snapshots,it provides better anti-noise performance and more accurate direction of arrival estimation results.The simulation results show that compared with other multi-snapshot direction of arrival estimation algorithms,the proposed algorithm provides better accuracy and higher angular resolution.The lake test results verify further the effectiveness of the proposed algorithm.