Multi-sensor IMM estimator for uncertain measurement

Authors:
Ming Cen;Xingfa Liu;Daisheng Luo
Affiliations:
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China;Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, China;School of Electronics and Information Science, Sichuan University, Chengdu, China
Venue:
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Year:
2009

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Abstract

Interacting Multiple Model (IMM) estimator can provide better performance over the single model Kalman filter. In multi-sensor system ordinarily, availability of measurement from different sensors is stochastic, and it is difficult to construct uniform global observation vector and observation matrix appropriately in current method. Then an IMM estimator for uncertain measurement is presented. By the method invalid measurement is regarded as outlier, and approximation is reconstructed by feedback of system state estimation of fusion center. Then nominally generalized certain measurement can be obtained by substituting reconstructed one for invalid one. The generalized certain measurement can be centralized to construct global measurement and provided to IMM estimator, and current multi-sensor IMM estimation method is generalized to uncertain environment. Theoretical analysis and simulation results show the effectiveness of the method.