Sun Baoliang, Jiang Chunlan, Li Ming
State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2016 Nov 1;16(11):1823. doi: 10.3390/s16111823.
An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs.
为解决无线传感器网络（WSN）的多节点目标跟踪问题，提出了一种基于模糊神经网络（FNN）的多模型交互多节点目标跟踪算法。在多模型交互输出阶段，利用测量误差协方差矩阵的理论值与估计值之间的差异，自适应调整测量误差方差。在多节点融合过程中建立FNN融合系统，将来自不同节点的目标状态估计数据进行融合，从而获得网络目标状态估计。基于一个由九个检测节点组成的网络验证了该算法的可行性。实验结果表明，该算法在传感器故障和未知系统测量误差情况下，能够有效地跟踪机动目标。该算法在WSN的多节点目标跟踪中具有很强的实用性。
