Optimal and Steady-State Multisensor Covariance Intersection Fusion Kalman Filters

2011 ◽  
pp. 231-239
Author(s):  
Wenjuan Qi ◽  
Peng Zhang ◽  
Zili Deng
Keyword(s):  
Steady State ◽  
Kalman Filters ◽  
Covariance Intersection

Multisensor Covariance Intersection Fusion Kalman Filters

2013 ◽  
Vol 373-375 ◽  
pp. 946-952
Author(s):  
Wen Juan Qi ◽  
Peng Zhang ◽  
Zi Li Deng ◽  
Yuan Gao
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Tracking System ◽  
Minimum Variance ◽  
Geometric Interpretation ◽  
Weighted Fusion ◽  
Covariance Intersection ◽  
The Common ◽  
Measurement Biases ◽  
Theoretical Accuracy

For multisensor system with colored measurement noises, the common disturbance noises and measurement biases, the batch covariance intersection fusion (BCI) Kalman filter and the sequential covariance intersection fusion (SCI) Kalman filter are presented, which can avoid the computation of the local filtering errors and reduce the computational burden significantly. Under the linear unbiased minimum variance (ULMV) criterion, the three weighted fusion Kalman filters (weighted by matrices, scalars or diagonal matrices) are also presented. Their accuracy relations are analyzed and compared. Specially, the accuracy of the proposed covariance intersection fusion Kalman filters are higher than that of each local Kalman filters, and is lower than that of optimal fuser weighted by matrices. The geometric interpretation of the accuracy relations is given by the covariance ellipses. A Monte-Carlo simulation example for a tracking system verifies the correctness of the theoretical accuracy relations.

Covariance Intersection Fusion Robust Time-Varying Kalman Filter for Two-Sensor System with Uncertain Noise Variances

2013 ◽  
Vol 475-476 ◽  
pp. 470-475
Author(s):  
Wen Juan Qi ◽  
Peng Zhang ◽  
Gui Huan Nie ◽  
Zi Li Deng
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Tracking System ◽  
System Simulation ◽  
Lyapunov Equation ◽  
Conservative System ◽  
Upper Bounds ◽  
Time Varying ◽  
Worst Case ◽  
Covariance Intersection

This paper investigates the problem of designing covariance intersection fusion robust time-varying Kalman filter for two-sensor time-varying system with uncertain noise variances. Using the minimax robust estimation principle, the local and covariance intersection (CI) fusion robust time-varying Kalman filters 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 time-varying CI fuser is higher than that of each local robust time-varying Kalman filter. A two-sensor tracking system simulation verifies the robustness and robust accuracy relations.

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