Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises

Liu, Yulei; Yan, Liping; Xiao, Bo; Xia, Yuanqing; Fu, Mengyin

doi:10.1007/978-3-642-37832-4_3

Cited by 10 publications

(9 citation statements)

References 6 publications

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“…In order to avoid augmentation, the multi-rate fusion problem is transformed into an equivalent single rate fusion problem. For non-uniform sampling systems, a distributed fusion filter is given [14], for uniform sampling * This work is supported by National Natural Science Foundation (NNSF) of China under Grant 61403131. systems, the corresponding distributed fusion filters are also given in [15]. However, the multiplicative noises are not taken into account.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Hao

2015

2015 34th Chinese Control Conference (CCC)

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“…[9] studied the distributed fusion when the measurement noises are correlated across sensors and with the system noise at the same time step. When the noise of different sensors are cross-correlated and also coupled with the system noise of the previous step, we derive the optimal sequential fusion and optimal distributed fusion algorithm in [10], and generate this result to the fusion of multirate sensor cases [11,12]. When there is correlation between the process noise and the measurement noise and among measurement noises, a distributed weighted robust Kalman filter fusion algorithm is derived for uncertain systems with multiple sensors in [13].…”

Section: Introductionmentioning

confidence: 99%

Optimal fusion estimation for stochastic systems with cross-correlated sensor noises

Yan

Xia

2017

Sci. China Inf. Sci.

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This paper is concerned with the optimal fusion of sensors with cross-correlated sensor noises. By taking linear transformations to the measurements and the related parameters, new measurement models are established, where the sensor noises are decoupled. The centralized fusion with raw data, the centralized fusion with transformed data, and a distributed fusion estimation algorithm are introduced, which are shown to be equivalent to each other in estimation precision, and therefore are globally optimal in the sense of linear minimum mean square error (LMMSE). It is shown that the centralized fusion with transformed data needs lower communication requirements compared to the centralized fusion using raw data directly, and the distributed fusion algorithm has the best flexibility and robustness and proper communication requirements and computation complexity among the three algorithms (less communication and computation complexity compared to the existed distributed Kalman filtering fusion algorithms). An example is shown to illustrate the effectiveness of the proposed algorithms.

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“…In order to avoid the state augmentation, the multirate fusion problem is transformed into an equivalent single rate fusion problem. For non-uniform sampling systems, a distributed fusion filter is given [11], for uniform sampling systems, the corresponding distributed fusion filters are also given in [12][13]. However, the cross-covariance matrices are needed to obtain the fusion weights.…”

Section: Introductionmentioning

confidence: 99%

Distributed Fusion Filter for Multi-rate Sampling Stochastic Singular Systems with Multiplicative Noises

Jin¹,

Ma²,

Li³

et al. 2015

IJMUE

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The distributed fusion filtering problem is studied for multi-rate sampling stochastic singular linear systems with multiple sensors and stochastic multiplicative noises. The system is described at the highest sampling rate and different sensors may have different lower sampling rates. The white noise in measurement matrix is introduced to describe the stochastic disturbance. Firstly, based on decomposition in canonical form, the original singular system is transformed into fast and slow two subsystems. For the two reduced-order subsystems, the local filters (LFs) are given based on the "dummy" random variables. The cross-covariance matrices between any two local filtering errors are derived. Further, the distributed fusion filter weighted by matrices (FFWM) is obtained for the original singular system based on the well-known fusion algorithm in the linear minimum variance sense. Simulation example verifies the correctness and feasibility of the proposed algorithm.

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Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises

Cited by 10 publications

References 6 publications

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

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

Optimal fusion estimation for stochastic systems with cross-correlated sensor noises

Distributed Fusion Filter for Multi-rate Sampling Stochastic Singular Systems with Multiplicative Noises

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