For maneuvering target tracking with sensor faults, consensus-based distributed state estimation problems are studied herein. The communication status of the nonlinear system composed of multiple agents is described using the graph theory. Considering the impacts caused by sensor failures on measurement equations, a weighted average consensus-based unscented information filter (UIF) algorithm is proposed to improve tracking accuracy. Moreover, the estimation error for the investigated nonlinear system has been analyzed based on the stochastic boundedness theory to evaluate the proposed algorithm’s performance and feasibility. Finally, simulation results are presented to assert the validity of the method.
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This work was supported by National Natural Science Foundation of China (Grant Nos. 61873011, 61803014, 61503009, 61333011), Beijing Natural Science Foundation (Grant No. 4182035), Young Elite Scientists Sponsorship Program by CAST (Grant No. 2017QNRC001), Aeronautical Science Foundation of China (Grant Nos. 2016ZA51005, 20170151001), Special Research Project of Chinese Civil Aircraft, State Key Laboratory of Intelligent Control and Decision of Complex Systems, and Fundamental Research Funds for the Central Universities (Grant No. YWF-18-BJ-Y-73).
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Yu, Y., Peng, S., Dong, X. et al. UIF-based cooperative tracking method for multi-agent systems with sensor faults. Sci. China Inf. Sci. 62, 10202 (2019). https://doi.org/10.1007/s11432-018-9581-y
- unscented information filter
- consensus theory
- cooperative tracking
- multi-agent system
- stochastic boundedness