Adaptive Kalman Estimation in Target Tracking Mixed with Random One-Step Delays, Stochastic-Bias Measurements, and Missing Measurements | Zendy

Sujuan Chen | Zendy; Yinya Li | Zendy; Guoqing Qi | Zendy; Andong Sheng | Zendy

Open Access

Adaptive Kalman Estimation in Target Tracking Mixed with Random One-Step Delays, Stochastic-Bias Measurements, and Missing Measurements

Author(s) -

Sujuan Chen,

Yinya Li,

Guoqing Qi,

Andong Sheng

Publication year - 2013

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2013/716915

Subject(s) - kalman filter , bernoulli's principle , computer science , tracking (education) , control theory (sociology) , set (abstract data type) , mathematics , mathematical optimization , artificial intelligence , control (management) , psychology , pedagogy , engineering , programming language , aerospace engineering

The objective of this paper is concerned with the estimation problem for linear discrete-time stochastic systems with mixed uncertainties involving random one-step sensor delay, stochastic-bias measurements, and missing measurements. Three Bernoulli distributed random variables are employed to describe the uncertainties. All the three uncertainties in the measurement have certain probability of occurrence in the target tracking system. And then, an adaptive Kalman estimation is proposed to deal with this problem. The adaptive filter gains can be obtained in terms of solutions to a set of recursive discrete-time Riccati equations. Examples in three scenarios of target tracking are exploited to show the effectiveness of the proposed design approach

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