Constrained Multiple Model Bayesian Filtering for Target Tracking in Cluttered Environment. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: He, Shaoming; Shin, Hyo-Sang; Tsourdos, Antonios
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 425
• Page End: 430
• Publication Date: 2017-07
• Subjects: Unmanned aerial vehicle -- Trajectory estimation -- Random finite set -- Multiple model filtering -- System state constraint -- Sequential Monte Carlo implementation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.192
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper proposes a composite Bayesian filtering approach for unmanned aerial vehicle trajectory estimation in cluttered environments. More specifically, a complete model for the measurement likelihood function of all measurements, including target-generated observation and false alarms, is derived based on the random finite set theory. To accommodate several different manoeuvre modes and system state constraints, a recursive multiple model Bayesian filtering algorithm and its corresponding Sequential Monte Carlo implementation are established. Compared with classical approaches, the proposed method addresses the problem of measurement uncertainty without any data associations. Numerical simulations for estimating an unmanned aerial vehicle trajectory generated by generalised proportional navigation guidance law clearly demonstrate the effectiveness of the proposed formulation.