title: Distributed optimal consensus filter for target tracking in heterogeneous sensor networks.
abstract: 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.
authors.forename: Shanying
authors.initials: S
authors.lastname: Zhu
authors.forename: Cailian
authors.initials: C
authors.lastname: Chen
authors.forename: Wenshuang
authors.initials: W
authors.lastname: Li
authors.forename: Bo
authors.initials: B
authors.lastname: Yang
authors.forename: Xinping
authors.initials: X
authors.lastname: Guan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003195:Computer Communication Networks; D003661:Decision Support Techniques; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D014159:Transducers