Distributed fusion estimator for multi‐sensor asynchronous sampling systems with missing measurements. Issue 7 (1st September 2016)

• Record Type: Journal Article
• Authors: Lin, Honglei; Sun, Shuli
• Is Part Of: IET signal processing. Volume 10:Issue 7(2016)
• Page Start: 724
• Page End: 731
• Publication Date: 2016-09-01
• Subjects: sensor fusion -- signal sampling -- distributed sensors -- state estimation; distributed fusion estimator -- multisensor asynchronous sampling systems -- missing measurements -- linear stochastic systems -- optimal local state estimator -- cross‐covariance matrices -- estimation errors; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2016.0017
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : The fusion estimation problem for a class of multi‐sensor asynchronous sampling linear stochastic systems with missing measurements is considered, where the state is updated uniformly and each sensor non‐uniformly samples one measurement at most within a state update period. Based on the sampled measurement data of each sensor, the optimal local state estimators are designed at the state and measurement points by using the projection theory. The cross‐covariance matrices between estimation errors of any two local estimators are derived. Based on the obtained local estimators and cross‐covariance matrices, the distributed optimal fusion estimator is given by using matrix‐weighted fusion estimation algorithm in the linear minimum variance sense. A simulation example verifies the effectiveness of the algorithms.