基于粒子群优化和M-H采样粒子滤波的传感器网络目标跟踪方法

针对实际应用条件下传感器节点的观测数据与目标动态参数间呈现为非线性关系的特性，提出了一种基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法。该方法采用分布式结构，在动态网络拓扑结构下，由粒子群优化和M-H抽样技术实现滤波中的重抽样过程，抑制粒子退化现象，并通过粒子间共享历史信息，降低单个粒子历史状态间的相关性使各粒子能快速收敛至最优分布，从而实现高精度的目标跟踪效果。仿真结果表明，相比现有的基于信息粒子滤波和并行粒子滤波技术的传感器网络目标跟踪方法，所提出的方法能降低网络总能耗，同时保证目标跟踪的精度。

Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter

For the characteristic of the nonlinear relationship between the observation information of sensor nodes and the target dynamic parameters under the real application conditions, a target tracking algorithm for wireless sensor networks based on particle swarm optimization and Metropolis-Hasting sampling particle filter was proposed. Distributed architecture is adopted in this target tracking scheme. And under the dynamic network topology, particle swarm optimization and Metropolis-Hasting sampling are introduced into the resampling period to reduce sample degeneracy. In order to achieve the goal of high-precision tracking performance, the history information is shared among the particles to reduce the correlation between the history states of a single particle, so that the particles can rapidly converge to an optimal distribution. The simulations corroborate that compared with currently existing target tracking schemes based on the technology of information particle filter and parallel particle filter, the proposed scheme can reduce the total energy consumption, while ensuring the accuracy of target tracking.