基于卡尔曼滤波的单矢量水听器水中多目标方位跟踪

对单矢量水听器接收的声压和质点振速信息进行联合处理,采用量子粒子群求解声压和质点振速组成的非线性相关方程组,从而实现多目标声源方位的估计。对于低信噪比的情况,难免会引入较大的方位估计误差。采用最小二乘法对目标方位轨迹进行拟合并建立预测模型,然后通过卡尔曼滤波对单矢量水听器估计的目标方位轨迹进行优化。结果表明：单矢量水听器能够同时分辨多个目标方位,解算结果应用统计特性表示；信噪比越高,分辨率和精度越高,偏差越小；对于水中目标而言,1阶多项式足以进行方位轨迹拟合,再采用卡尔曼滤波能够有效提高目标方位跟踪精度。

The DOA tracking for multiple targets under water with single vector hydrophone based on Kalman filter

The DOA (direction of arrival) of multiple targets are acquired by solving non-liner correlation equations involving acoustic pressure and particleSvelocity with QPS (quantum particle swarm) algorithm. However, If the SNR (signal to noise ratio) is not enough, the error of DOA will be unacceptable. In order to solve this problem, The DOAStracks of multiple targets are fitted with the method of least squares, the predictionSmodel is found and then The DOAStracks are optimized by Kalman filter. The results indicate that The DOAS of multiple targets can be resolved with single vector hydrophone and the results should be expressed by statistic characteristics. When the SNR is higher, the resolutionSratio and precision are also higher but the deviation is smaller. For the multiple targets under water, the first order polynomial is enough to fit the tracks and the precision of DOA can be improved effectively by Kalman filter.