基于改进粒子群算法的矢量水听器多目标方位估计

为了研究单矢量水听器多目标方位估计能力,分别利用互谱声强法、MUSIC算法及信号统计量方法对多个目标方位进行估计。互谱声强法可以估计出多个不同频的单频目标方位,但对于频谱混叠的目标无法分辨；MUSIC算法可以分辨单频和宽带目标,但利用单矢量水听器最多可估计 2个目标方位。为此,针对文章提出的信号统计量方法,构建了声压和振速的统计量模型,将其与粒子群优化算法及改进算法相结合,实现了基于改进粒子群算法的多目标方位估计。对多个单频和宽带信号目标进行仿真分析,结果表明,进粒子群算法具有良好的估计效果；对 3种方法的估计结果进行比较,验证了改进粒子群算法有较好的适用性。通过对 2022年千岛湖试验数据的处理再一次验证了算法的有效性。

Multi-Target Azimuth Estimation of Vector Hydrophone Based on Improved Particle Swarm Optimization Algorithm

In order to study the multi-target azimuth estimation capability of single vector hydrophone, cross-spectrum sound intensity method, MUSIC algorithm and signal statistics method are used to estimate multiple-target azimuth. The cross-spectrum sound intensity method can estimate the azimuth of multiple single-frequency targets with different fre-quencies, but can not distinguish the targets with spectral aliasing. The MUSIC algorithm can distinguish both single-frequency and wide-band targets, but it can estimate up to two target azimuths with a single-vector hydrophone. Therefore, based on the proposed signal statistics method, the statistical models of sound pressure and vibration velocity are con-structed, which are combined with particle swarm optimization algorithm and improved algorithm to realize the multi-target azimuth estimation based on improved particle swarm optimization algorithm. The simulation analysis of several single frequency and broadband signal targets shows that improved particle swarm optimization algorithm has a good esti-mation effect, and the estimation results of the three methods are compared to verify that improved particle swarm optimi-zation algorithm has a good applicability. The validity of the algorithm is verified once again by processing the data of the 2022 Qiandao Lake experiment.