Distributed Optimal Consensus Filter for Target Tracking in Heterogeneous Sensor Networks
Distributed Optimal Consensus Filter for Target Tracking in Heterogeneous Sensor Networks
复制标题
异构传感器网络中目标跟踪的分布式最优一致性过滤器
DOI：
10.1109/tsmcb.2012.2236647
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发表时间：
2011-05
影响因子：
11.8
通讯作者：
Zhu Shanying, Chen Cailian, Guan Xinping
中科院分区：
文献类型：
--
作者：
Zhu Shanying, Chen Cailian, Guan Xinping
研究了传感器网络目标跟踪中的滤波器设计问题。与现有的大多数传感器网络的工作不同，我们考虑了异构传感器网络与两种类型的传感器不同的处理能力（表示为类型I和类型II传感器，分别）。然而，如何处理传感器的异质性和如何设计一个过滤器，在这样的网络目标跟踪的问题仍然在很大程度上未被探索。本文提出了一种新的分布式一致性滤波器来解决目标跟踪问题。两个标准，即无偏性和最优性，施加的滤波器设计。所谓的顺序设计方案，然后提出来解决传感器的异构性。第一类传感器采用庞特里亚金最小值原理对估计误差进行优化。对于第二类传感器，采用拉格朗日乘子法结合矩阵广义逆进行滤波器优化。此外，它被证明是保证收敛性的建议共识滤波器的过程和测量噪声的存在。仿真结果验证了该滤波器的性能。它还表明，异构传感器网络与建议的过滤器优于同质同行在光的网络成本的减少，估计性能略有下降。
This paper is concerned with the problem of filter design for target tracking over sensor networks. Different from most existing works on sensor networks, we consider the heterogeneous sensor networks with two types of sensors different on processing abilities (denoted as type-I and type-II sensors, respectively). However, questions of how to deal with the heterogeneity of sensors and how to design a filter for target tracking over such kind of networks remain largely unexplored. We propose in this paper a novel distributed consensus filter to solve the target tracking problem. Two criteria, namely, unbiasedness and optimality, are imposed for the filter design. The so-called sequential design scheme is then presented to tackle the heterogeneity of sensors. The minimum principle of Pontryagin is adopted for type-I sensors to optimize the estimation errors. As for type-II sensors, the Lagrange multiplier method coupled with the generalized inverse of matrices is then used for filter optimization. Furthermore, it is proven that convergence property is guaranteed for the proposed consensus filter in the presence of process and measurement noise. Simulation results have validated the performance of the proposed filter. It is also demonstrated that the heterogeneous sensor networks with the proposed filter outperform the homogenous counterparts in light of reduction in the network cost, with slight degradation of estimation performance.
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影响因子：
5.4
作者：
I. Schizas;G. Giannakis;S. Roumeliotis;Alejandro Ribeiro
通讯作者：
I. Schizas;G. Giannakis;S. Roumeliotis;Alejandro Ribeiro
DOI：
--
发表时间：
2001
期刊：
--
影响因子：
--
作者：
X. Li;V. Jilkov
通讯作者：
X. Li;V. Jilkov
DOI：
10.1016/s0169-7161(05)25019-8
发表时间：
2005-01-01
期刊：
BAYESIAN THINKING: MODELING AND COMPUTATION
影响因子：
--
作者：
Migon, HS;Gamerman, D;Ferreira, MAR
通讯作者：
Ferreira, MAR
DOI：
10.1016/j.sysconle.2006.11.003
发表时间：
2007-06
期刊：
Syst. Control. Lett.
影响因子：
--
作者：
V. Gupta;B. Hassibi;R. Murray
通讯作者：
V. Gupta;B. Hassibi;R. Murray
DOI：
10.1177/027836499301200102
发表时间：
1993-02
期刊：
The International Journal of Robotics Research
影响因子：
--
作者：
B. Rao;Hugh F. Durrant-Whyte;J. A. Sheen
通讯作者：
B. Rao;Hugh F. Durrant-Whyte;J. A. Sheen