压缩感知波束形成算法性能分析

Performance Analysis of Compressed Sensing Beamforming Algorithm

波束形成声源识别技术具有测量速度快、计算效率高等优点,被广泛应用。因为其“主瓣”过宽造成空间定位精度低,“旁瓣”既高又多导致出现许多“鬼影”声源,从而限制其得到进一步应用。为解决以上问题,利用压缩感知理论中贪婪算法对信号的重建方法,改进常规波束形成算法,提出一种压缩感知波束形成方法。对单极子声源和相干声源进行声源识别数值模拟仿真,结果表明:与函数波束形成方法相比,压缩感知波束形成方法在识别单极子声源和相干声源时,定位精度更高,更能使旁瓣得到有效衰减,分辨率提升更大,并能够大幅缩短识别时间,具有一定的研究意义。

Beamforming sound source identification technique has the advantages of fast measurement speed and high calculation efficiency, and is widely used. However, since the“main lobe”is very wide in this technique, its spatial positioning accuracy is low. Moreover, there are many high“side lobes”which lead to many ghost sound sources. Therefore, further application of this technique is limited. In order to solve the problem, a method of reconstructing signals by greedy algorithm in compressed sensing theory is used to improve the conventional beamforming algorithm, and a compressed sensing beamforming method is proposed. The numerical simulation of sound source identification for monopole source and coherent sound source is carried out. The results show that compared with the function beamforming method, the compressed sensing beamforming method has better positioning accuracy when identifying the monopole source and the coherent sound source. This method can effectively suppress the side lobes, greatly improve resolution and significantly shorten the recognition time.