水下目标多平台协同定位和跟踪方法

提出了一种融合多平台方位观测数据的水下声学目标定位跟踪方法。该方法采用多级多假设跟踪对目标状态进行滤波和关联,第一级基于每个平台波束域检测结果独立使用多假设跟踪方法对目标方位角进行初级跟踪;第二级首先通过多平台联合似然概率估计多目标初始位置,再用多假设跟踪方法对不同平台初级跟踪的方位角航迹进行关联,最后通过关联的角度值和扩展卡尔曼滤波对多目标位置进行序贯估计。仿真结果证明,该方法能以更少计算量实现更优定位性能,优于单级多假设跟踪方法.海试数据处理结果表明,存在较多干扰的情况下,单级方法跟踪过程中丢失目标,而多级方法实现了对目标的全程跟踪,且收敛后定位误差在70m内。该方法可降低噪声干扰对数据关联及目标定位的影响,并通过多平台协同消除线阵的方位模糊,适用于移动多平台声学目标定位和跟踪场景。 

Method of multi-platform cooperative localization and tracking for underwater targets

An approach for underwater acoustic targets localization and tracking based on bearings-only data from multi-platform was proposed.Multi-stage multiple hypothesis tracking method is used for filtering and association.In the first stage,multiple hypothesis tracking is only used to track the azimuths in the beam domain of each platform.In the second stage,the initial positions of the targets are estimated by the multi-platform joint likelihood probability,then the multiple hypothesis tracking method is used to associate the azimuth trajectory from different platforms,finally the extended Kalman filter algorithm is used to sequentially update the targets positions.The simulation results show that the method can achieve better positioning performance with less calculation than the single stage multiple hypothesis tracking method.The sea trial data processing results show that in the interference environment,the single-stage method loses the targets.However,the proposed multi-stage method achieves the full-time tracking of the targets,and the positioning error is within 70 m after convergence.The proposed method can reduce the impact of noise interferences on data association as well as target positioning,and can also solve the ambiguity problem of linear arrays,which is suitable for acoustic targets localization and tracking in mobile multi-platform scenarios.