Multi-rate optimal state estimation

Liang, Yan; Chen, Tongwen; Pan, Quan

doi:10.1080/00207170902906132

Cited by 70 publications

(27 citation statements)

References 30 publications

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“…For system without multiplicative noise, we give the MSEs of our LF and the filters in [13] and [16] for the first local sensor subsystem. From Fig3, we see that the estimation accuracy of our LF1 is the same as that of [13] and higher than that of [16]. Moreover, at the measurement sampling points, the computational cost of our LF1 is…”

Section: Simulation Resultsmentioning

confidence: 99%

“…However, the state estimators are very complex and high computational burden. On the basis of Kalman filtering theory, many useful filtering strategies are also proposed such as optimal signal reconstruction method [9], asynchronous centralized fusion algorithm [10], sequential filtering algorithm [11], left synchronously lifting technology [12], and measurement augmented approach [13]. But, the computational cost of the above filtering strategies is high since they are given by state/measurement augmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Remark 1: From Theorem 1, we see that the LFs at the state updating points are obtained by smoothing the LFs at the measurement sampling points. Therefore, compared with the state augmented approach in reference[13], the proposed filter can obviously reduce the computational burden for the l th sensor subsystem. The computational burden at each measurement sampling points: our filter is3 O( ) n and the augmented filter in [13] is 3 O(( ) ) l c n .…”

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confidence: 98%

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Distributed fusion filter for multi-rate multi-sensor systems with multiplicative noises

Hao

2015

2015 34th Chinese Control Conference (CCC)

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This paper is concerned with the distributed fusion filtering problem for a class of multi-rate multi-sensor systems with white multiplicative noises. The stochastic dynamic system sampled uniformly. Sampling period of each sensor is uniform and the integer multiple of the state update period. Moreover, different sensors have the different sampling rates. Firstly, the local filters (LFs) at state update points are obtained by smoothing of LFs at measurement sampling points. Then, the corresponding error variance matrices (EVMs) are derived to compute the fusion weights. Finally, the distributed fusion filter is given by the well-known covariance intersection fusion (CIF) algorithm. Simulation research supports the correctness of the proposed results.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 98%

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Distributed fusion filter for multi-rate multi-sensor systems with multiplicative noises

Hao

2015

2015 34th Chinese Control Conference (CCC)

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“…For multi-rate systems, the first important study goes back to the switch decomposition technique proposed by Kranc [1]. So far, many useful filtering strategies are proposed [2][3][4][5][6]. But, the computational cost of the above filtering strategies is high since they are given by state/measurement augmentation.…”

Section: Introductionmentioning

confidence: 99%

Optimal Linear Filter for Multi-rate Singular System

Jin¹,

Ma²,

Li³

et al. 2014

Advanced Science and Technology Letters

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Abstract. This paper is concerned with the optimal linear filtering problem for singular system with multiple rates. The state is updated at the higher rate and the measurement is sampled at the lower rate. The original singular system is transformed into fast and slow two subsystems. By introducing the "dummy" random variable, the filter for the slow subsystem is derived. Further, the filter for the original system is obtained based on the filters of the two subsystems.

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“…One is based on multiscale system theory and the other is based on Kalman filtering theory. On the basis of Kalman filtering theory, many useful filtering strategies are proposed [2][3][4][5][6]. But, the computational cost of the above filtering strategies is high since they are given by state/measurement augmentation.…”

Section: Introductionmentioning

confidence: 99%