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Observable Degree Analysis to Match Estimation Performance for Wireless Tracking Networks
Author(s) -
Ge Quanbo,
Ma Jinyan,
Chen Shaodong,
Wang Yongting,
Bai Liang
Publication year - 2017
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1386
Subject(s) - observable , estimator , observability , kalman filter , mathematics , computer science , degree (music) , control theory (sociology) , statistics , artificial intelligence , physics , control (management) , quantum mechanics , acoustics
Abstract State estimation suffers some new challenging problems when wireless sensor networks (WSNs) are used in mobile target tracking systems. An important problem among them is low observability, which makes it necessary to further study performance of tracking estimators from an observable degree point of view. This paper studies observable degree analysis (ODA) to formulate the estimator performance for a kind of wireless tracking filter. For a kind of time‐varying Kalman estimator, an improved ODA method is proposed by using weighted least square, Cauchy Schwarz inequality and normalization processing. The main contributions are that the relation is firstly analytically established between the observable degree and the estimation performance, and finally the observable degree is normalized in [0,1]. Meanwhile, observable degrees are both given for local state components and the global state. Thereby, we have the conclusion that higher observable degree corresponds to better estimation performance of the Kalman filter. Accordingly, it is potentially hopeful to achieve an effective function to study estimation performance of tracking estimators by directly using the observable degree. Simulation is provided to verify the results on real‐time and generality, completeness and consistency/matching of the proposed observable degree analysis.