scholarly journals Distributed Weighting Fusion and Covariance Intersection Fusion Kalman Smoother for Systems with Colored Measurement Noises

2016 ◽  
Author(s):  
Peng Zhang ◽  
Wenjun Qi
Keyword(s):  
Kalman Smoother ◽  
Covariance Intersection ◽  
Measurement Noises

Covariance Intersection Fusion Robust Steady-State Kalman Smoother for Multisensor System with Uncertain Noise Variances

2013 ◽  
Vol 475-476 ◽  
pp. 476-481
Author(s):  
Wen Juan Qi ◽  
Peng Zhang ◽  
Zi Li Deng
Keyword(s):  
Monte Carlo Simulation ◽  
Steady State ◽  
Tracking System ◽  
Lyapunov Equation ◽  
Conservative System ◽  
Upper Bounds ◽  
Worst Case ◽  
Multisensor System ◽  
Kalman Smoother ◽  
Covariance Intersection

This paper deals with the problem of designing covariance intersection fusion robust steady-state Kalman smoother for multisensor system with uncertain noise variances. Using the minimax robust estimation principle, the local and covariance intersection (CI) fusion robust steady-state Kalman smoothers are presented based on the worst-case conservative system with the conservative upper bounds of noise variances. Their robustness is proved based on the proposed Lyapunov equation, and the robust accuracy of CI fuser is higher than that of each local robust Kalman smoother. A Monte-Carlo simulation of three sensors tracking system verifies their robustness and robust accuracy relations.

Covariance Intersection Fusion Smoothers for Multichannel ARMA Signal with Colored Measurement Noises

2013 ◽  
Vol 373-375 ◽  
pp. 716-722 ◽  
Author(s):  
Wen Juan Qi ◽  
Peng Zhang ◽  
Zi Li Deng ◽  
Yuan Gao
Keyword(s):  
Moving Average ◽  
State Space Model ◽  
Minimum Variance ◽  
Autoregressive Moving Average ◽  
Matrix Weights ◽  
Weighted Fusion ◽  
Accuracy Comparison ◽  
Covariance Intersection ◽  
Measurement Noises ◽  
Augmented State

For multichannel autoregressive moving average (ARMA) signal with colored measurement noises, based on classical Kalman filtering theory, a covariance intersection (CI) fusion smoother without cross-covariances is presented by the augmented state space model. It has the advantage that the computation of cross-covariances is avoid, so it can significantly reduce the computational burden, and it can solve the fusion problem for multi-sensor systems with unknown cross-covariances. Under the unbiased linear minimum variance (ULMV) criterion, three optimal weighted fusion smoothers with matrix weights, scalar weights and diagonal weights are also presented respectively. The accuracy comparison of the CI fuser with the other three weighted fusers is given. It is shown that its accuracy is higher than that of each local smoother, and is lower than or close to that of the optimal fuser weighted by matrices. So the presented fusion smoother is better in performance.

Multi-Channel ARMA Signal Covariance Intersection Fusion Kalman Smoother

2013 ◽  
Vol 655-657 ◽  
pp. 701-704
Author(s):  
Peng Zhang
Keyword(s):  
Steady State ◽  
Kalman Filtering ◽  
Geometric Interpretation ◽  
Fusion Method ◽  
Filtering Method ◽  
Kalman Smoother ◽  
Cross Covariance ◽  
Covariance Intersection ◽  
Optimal Signal

For the multi-channel ARMA signal with two sensors, by the classical Kalman filtering method and the covariance intersection (CI) fusion method, a covariance intersection fusion steady-state Kalman signal smoother is presented, which is independent of the unknown cross-covariance. It is proved that its accuracy is higher than that of each local Kalman signal smoother, and is lower than that of the optimal signal fuser weighted by matrices. The geometric interpretation of the above accuracy relations are presented based on the covariance ellipses. A simulation example result shows its effectiveness and correctness.

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