Multi-Sensor Information Fusion Kalman Filter Weighted by Scalars for Systems with Colored Measurement Noises

An optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense. Based on this fusion criterion, a scalar weighting information fusion decentralized Kalman filter is given for discrete time-varying linear stochastic control systems measured by multiple sensors with colored measurement noises, which is equivalent to an information fusion Kalman predictor for systems with correlated noises. It has a two-layer fusion structure with fault tolerant and robust properties. Its precision is higher than that of each local filter. Compared with the fusion filter weighted by matrices and the centralized filter, it has lower precision when all sensors are faultless, but has reduced computational burden. Simulation researches show the effectiveness.

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An Information Fusion Algorithm Based on Kalman Filtering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.444-445.1072 ◽

2013 ◽

Vol 444-445 ◽

pp. 1072-1076

Author(s):

Xiu Hu Tan

Keyword(s):

Mathematical Model ◽

Information Fusion ◽

Minimum Mean Square Error ◽

Minimum Variance ◽

Probability Method ◽

Fusion Algorithm ◽

Multisensor Systems ◽

Optimal Information ◽

Noise Statistics ◽

The Mathematical Model

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

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Fault tolerant navigation method for satellite based on information fusion and unscented Kalman filter

Journal of Systems Engineering and Electronics ◽

10.3969/j.issn.1004-4132.2010.04.023 ◽

2010 ◽

Vol 21 (4) ◽

pp. 682-687 ◽

Cited By ~ 5

Author(s):

Dan Li ◽

Jianye Liu ◽

Li Oiao ◽

Zhi Xiong

Keyword(s):

Kalman Filter ◽

Information Fusion ◽

Unscented Kalman Filter ◽

Fault Tolerant ◽

Navigation Method

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Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion

Sensors ◽

10.3390/s21175808 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5808

Author(s):

Dapeng Wang ◽

Hai Zhang ◽

Baoshuang Ge

Keyword(s):

Kalman Filter ◽

Information Fusion ◽

Stochastic Systems ◽

Unscented Kalman Filter ◽

Minimum Variance ◽

Measurement Noise ◽

Time Varying ◽

Measurement Noise Covariance ◽

Noise Covariance ◽

Sensor Information

In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.

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