Abstract
This paper is concerned with the state estimation problem for a class of non-uniform sampling systems with missing measurements where the state is updated uniformly and the measurements are sampled randomly. A new state model is developed to depict the dynamics at the measurement sampling points within a state update period. A non-augmented state estimator dependent on the missing rate is presented by applying an innovation analysis approach. It can provide the state estimates at the state update points and at the measurement sampling points within a state update period. Compared with the augmented method, the proposed algorithm can reduce the computational burden with the increase of the number of measurement samples within a state update period. It can deal with the optimal estimation problem for single and multi-sensor systems in a unified way. To improve the reliability, a distributed suboptimal fusion estimator at the state update points is also given for multi-sensor systems by using the covariance intersection fusion algorithm. The simulation research verifies the effectiveness of the proposed algorithms.
Highlights
The estimation problems for multi-rate non-uniform sampling systems have attracted much attention due to wide applications in parameter identification [1], industrial detection [2], target tracking and signal processing [3,4,5,6,7,8]
For multi-rate non-uniform sampling systems, the multi-sensor information fusion filters have been presented based on the data block method in [3,4,5] where the effect of the system noise on modeling is ignored, which brings the errors in modeling
The optimal fusion and distributed suboptimal fusion estimators are given to deal with the fusion estimation problems for multi-sensor systems
Summary
The estimation problems for multi-rate non-uniform sampling systems have attracted much attention due to wide applications in parameter identification [1], industrial detection [2], target tracking and signal processing [3,4,5,6,7,8]. For the multi-sensor networked systems with packet dropouts, a two-stage distributed fusion estimation algorithm is proposed by using a multi-rate scheme to reduce communication cost in [14], where sensors collect measurements from. The multi-rate filter in the least mean square sense is designed in [16] and can obtain more accurate estimate than the H8 filter in [15] In these studies, the sampling periods of individual sensors are uniform and integer times of the state update period though different sensors have different sampling rates. Information fusion estimation problem in multi-sensor networked systems is explored in [21,22,23,24] where packet dropouts, random delays and missing measurements are considered. The optimal fusion and distributed suboptimal fusion estimators are given to deal with the fusion estimation problems for multi-sensor systems
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