Multi-UUV Collaborative Bearing-only Target Tracking Based on Boundary Constrained Particle Filter
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摘要: 面向海上跨域协同中多无人水下航行器(UUV)协同探测水面目标需求, 针对现有纯方位目标跟踪算法所面临的滤波器初始化困难和水声数据传输丢包问题, 提出了一种基于边界约束粒子滤波的多UUV协同纯方位目标跟踪算法。首先提出了主从式协同探测模型, 利用跟随者向领航者上报状态估计结果进行数据融合。其次, 基于UUV传感器和目标的先验信息设计了初始阶段可靠粒子生成方法和更新阶段的指标函数粒子权重优化方法。最后提出了基于灰色预测的分布式融合算法, 得到目标预测结果。仿真实验将所提算法和其他常见算法进行对比, 在通信丢包以及噪声干扰情况下验证了算法的有效性和可行性。Abstract: Due to the difficulties of filter initialization and packet loss in underwater acoustic data transmission faced by existing bearing-only target tracking algorithms, a multi-unmanned undersea vehicle(UUV) collaborative bearing-only target tracking algorithm based on boundary constrained particle filter was proposed, so as to meet the demand for multi-UUV collaborative detection of water surface targets in cross-domain collaboration at sea. Firstly, a master-slave collaborative detection model was proposed, which utilized the follower to report the state estimation results to the navigator for data fusion. Secondly, based on the prior information of UUV sensors and targets, a reliable particle generation method for the initial stage and a particle weight optimization method for the indicator function in the update stage were designed. Finally, a distributed fusion algorithm based on gray prediction was proposed to obtain the target prediction results. The simulation experiment compared this algorithm with other common algorithms and verified its effectiveness and feasibility under communication packet loss and noise interference.
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图 2 多UUV纯方位协同跟踪典型场景
Figure 2. Typical scenarios of multi-UUV collaborative bearing-only target tracking
图 9 多UUV协同估计目标速度分量
Figure 9. Target velocity components for multi-UUV collaborative estimation
图 10 多UUV协同估计的x分量位置RMSE
Figure 10. RMSE of x-component position for multi-UUV collaborative estimation
图 11 多UUV协同估计的y分量位置RMSE
Figure 11. RMSE of y-component position for multi-UUV collaborative estimation
图 12 多UUV协同估计的x分量速度RMSE
Figure 12. RMSE of x-component speed for multi-UUV collaborative estimation
图 13 多UUV协同估计的y分量速度RMSE
Figure 13. RMSE of y-component speed for multi-UUV collaborative estimation
图 14 不同丢包率下x分量位置RMSE
Figure 14. RMSE of target position at different packet loss rates on x component
图 15 不同丢包率下y分量位置RMSE
Figure 15. RMSE of target position at different packet loss rates on y component
图 16 不同丢包率下x分量速度RMSE
Figure 16. RMSE of target speed at different packet loss rates on x component
图 17 不同丢包率下y分量速度RMSE
Figure 17. RMSE of target speed at different packet loss rates on y component
图 19 主从UUV滤波算法运行时间
Figure 19. Calculation time of the filtering algorithm for master-slave UUV
图 20 基于灰色预测的数据融合算法单次计算时间
Figure 20. Single calculation time based on grey prediction data fusion algorithm
图 21 不同观测噪声强度下位置估计RMSE
Figure 21. Position estimation RMSE under different observation noise intensities
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