| 引用本文: | 汤文俊,张国良,曾静,徐君,姚二亮.适用于稀疏动态无线传感器网络的并行融合分布式无迹信息滤波算法[J].控制理论与应用,2016,33(7):903~914.[点击复制] |
| TANG Wen-jun,ZHANG Guo-liang,ZENG Jing,XU Jun,YAO Er-liang.Parallel fusion distributed unscented information filter algorithm for sparse dynamic wireless sensor network[J].Control Theory and Technology,2016,33(7):903~914.[点击复制] |
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| 适用于稀疏动态无线传感器网络的并行融合分布式无迹信息滤波算法 |
| Parallel fusion distributed unscented information filter algorithm for sparse dynamic wireless sensor network |
| 摘要点击 2272 全文点击 2074 投稿时间：2015-07-25 修订日期：2016-03-17 |
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| DOI编号 10.7641/CTA.2016.50643 |
| 2016,33(7):903-914 |
| 中文关键词 稀疏动态无线传感器网络 分布式无迹信息滤波 局部无迹信息滤波器 加权平均一致性滤波器 并行融合 均方收敛速率 |
| 英文关键词 sparse dynamic wireless sensor network distributed unscented information filter local unscented information filter weighted average consensus filter parallel fusion mean-square convergence rate |
| 基金项目 中国工程科技中长期发展战略研究项目(2014–zcq–10)资助. |
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| 中文摘要 |
| 稀疏和随机动态变化是实际无线传感器网络(wireless sensor network, WSN)中普遍共同存在的两种通信拓
扑不稳定因素, 使基于一致性算法的分布式无迹信息滤波(distributed unscented information filter, DUIF)算法适用于
稀疏动态WSN, 将极大提高其实用性. 为此, 本文提出一种并行融合DUIF(parallel fusion DUIF, PF–DUIF)算法. 在
PF–DUIF算法中, 通过将实时局部后验估计均值和协方差用于局部无迹信息滤波器(local unscented information
filter, LUIF)的Sigma点采样, 使LUIF和加权平均一致性滤波器(weighted average consensus filter, WACF)得以并行运
行, 从而有效抵制由通信拓扑随机动态变化带来的较大一致跟踪误差的困扰; 同时, WACF通过对LUIF输出的无偏
局部信息矩阵和向量分别进行平均一致性滤波, 最终得到不包含由稀疏通信拓扑引起的平均一致误差的分布式后
验估计结果; 进而, 建立即时更新机制有效抑制随机动态通信拓扑引起的PF–DUIF算法滤波异步问题, 同时, 基于稀
疏动态WSN的平均网络模型, 在通信能量消耗受限条件下优化WACF均方收敛速率, 从而提高PF–DUIF算法的整体
滤波效率. 仿真实验结果表明, PF–DUIF算法能够有效应用于稀疏动态WSN机动目标跟踪. |
| 英文摘要 |
| Sparsity and stochastic dynamic change are two kinds of instability factors of communication topology, which
universal joint exist in real wireless sensor network (WSN). The practicability of distributed unscented information filter
(DUIF) will be improved vastly if making it applicable to the sparse dynamic WSN. For this purpose, a parallel fusion
DUIF (PF–DUIF) is proposed. In the PF–DUIF algorithm, the local unscented information filter (LUIF) and weighted
average consensus filter (WACF) can be implemented parallelly by applying the real local posterior estimated mean and covariance
to generat the sigma points. And then the consensus tracking errors caused by stochastic dynamic communication
topolagy can be avoided effectively. Meanwhile, by implementing average consensus filter on the unbias local information
matrices and vectors output by the LUIF respectively in the WACF, the distributed posterior estimated results without average
consensus error can be got. Furthermore, employing the real-time update mechanism to avoid the problem of filter
asynchronization in PF–DUIF algorithm caused by stochastic dynamic communication topolagy. And meanwhile, on the
basis of the average network model of the sparse dynamic WSN, the convergence rate of the WACF is modified under the
condition of limited communication energy consumption, so as to improve the global efficiency of PF–DUIF algorithm.
The simulation results show that PF–DUIF algorithm can efficiently track the target in sparse dynamic WSN. |
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