水下多目标方位的联合检测与跟踪

针对水下多目标方位跟踪及航迹关联问题,提出了一种粒子滤波的联合检测与跟踪方法.该方法在状态滤波过程中不需要方位观测值的输入,直接根据波束能量评估粒子的似然函数;利用交叉和变异算子进化小权值样本,通过低差异性序列的重采样提高子代粒子多样性。实现了多目标的跟踪并避免了方位观测量与多目标航迹关联的问题。仿真结果表明,在航迹断续和航迹交叉的情况下,该方法能够连续准确地跟踪目标方位。利用水下无人平台舷侧线阵的试验数据对算法性能进行了验证,正横方向的跟踪误差在3°以内;在目标运动模型失配时仍可以收敛到正确的方位航迹,没有出现错跟与失跟现象,可提高对交叉、汇聚及分离的多目标方位航迹的连续检测与跟踪能力. 

A unified method for underwater multi-target bearing detection and tracking

For underwater multi-target bearing tracking and Measurement-to-Track Association(MTA) problems,a unified method for detection and tracking based on particle filter is proposed.This method doesn't require bearing observation values in the filtering process.The likelihood function of particles is directly estimated according to beamforming energy.The particles with small weight are evolved after crossover and mutation operators.The diversity of offspring particles is improved by low discrepancy sequence resampling.It could effectively realize correct multi-target tracking and avoid MTA challenges.The simulation result shows that the method can continuously and accurately track target bearings in the case of temporary target disappearance and multi-target intersection.The effectiveness of the method is verified by using the Unmanned Underwater Vehicle(UUV) experimental data and the vertical direction tracking error is within 3°.The estimated result is close to the real value even when the motion model is mismatched.