Target Tracking with Underwater Sensor Networks Based on Grubbs Criterion and Improved Particle Filter Algorithm
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摘要: 水下无线传感网络(UWSN)执行目标跟踪时，因为各个传感器节点测量值对目标状态估计的贡献不一样以及节点能量有限，所以探索一种好的节点融合权重方法和节点规划机制能够获得更好的跟踪性能。针对上述问题，该文提出一种基于Grubbs准则和互信息熵加权融合的分布式粒子滤波(PF)目标跟踪算法(GMIEW)。首先利用Grubbs准则对传感器节点所获得的信息进行分析检验，去除干扰信息和错误信息。其次，在粒子滤波的重要性权值计算的过程中，引入动态加权因子，采用传感器节点的测量值与目标状态之间的互信息熵，来反映传感器节点提供的目标信息量，从而获得各个节点相应的加权因子。最后，采用3维场景下的簇-树型网络拓扑结构，跟踪监测区域内的目标。实验结果显示，该算法可有效提高水下传感器网络测量数据对目标跟踪预测的准确度，降低跟踪误差。Abstract: When the Underwater Wireless Sensor Network (UWSN) performs target tracking, the contributions of the measured values of the nodes are different, and the battery energy carried by the sensor node is limited. Therefore, a good node fusion weight method and node planning mechanism can obtain better tracking performance. A distributed particle filter target tracking algorithm based on Grubbs criterion and Mutual Information Entropy Weighted (GMIEW) fusion is proposed to solve the above problem in this paper. Firstly, the Grubbs criterion is used to analyze and verify the information obtained by the sensor nodes before the information fusion, and the interference information and error information are removed. Secondly, in the process of calculating the importance weight of particle filter, the dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor node and the target state is used to reflect the amount of target information provided by the sensor node, so as to obtain the corresponding weighting factor of each node. Finally, the improved cluster-tree network topology is used to track the target in three-dimensional space. Simulation results show that the proposed algorithm improves greatly the accuracy of underwater sensor measurement data for target tracking prediction and reduces the tracking error.
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表 1 目标跟踪算法仿真中的参数设置
仿真参数 数值 目标初始位置(m) (0, 60, 80) 目标初始加速度(m/s2) (5, 6, –1) 粒子数(个) 2000 声音传感器密度ρ(个/m3) 0.00008 仿真次数(次) 50 目标初始速度(m/s) (15, –20, 4) 采样间隔(s) 1 监测时长(s) 20 过程噪声方差 0.1 目标信号能量S 5000 
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表 3 3D仿真场景中不同传感器密度ρ下3种算法的平均位置RMSE(个/m3)
算法 0.00006 0.00008 0.00010 AW 3.6452 1.0442 0.9236 AHPW 2.6235 0.8940 0.5024 GMIEW 1.5261 0.5023 0.3026
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