Equivalent Kalman filter bank structure for multiple model adaptive estimation (MMAE) and generalized likelihood ratio (GLR) failure detection

2002 ◽  
Author(s):  
P.D. Hanlon ◽  
P.S. Maybeck
Keyword(s):  
Kalman Filter ◽  
Likelihood Ratio ◽  
Filter Bank ◽  
Adaptive Estimation ◽  
Failure Detection ◽  
Multiple Model ◽  
Generalized Likelihood Ratio ◽  
Bank Structure ◽  
Multiple Model Adaptive Estimation

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

2018 ◽  
Vol 233 (11) ◽  
pp. 4017-4031
Author(s):  
Kai Xiong ◽  
Chunling Wei ◽  
Haoyu Zhang
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Autonomous Navigation ◽  
Adaptive Estimation ◽  
Line Of Sight ◽  
Parallel Model ◽  
Multiple Model ◽  
Filtering Algorithm ◽  
Robust Kalman Filter ◽  
Multiple Model Adaptive Estimation

In this paper, a parallel model adaptive Kalman filtering algorithm is presented for multiple sensors estimation fusion when the measurement noise statistics are uncertain. As a typical adaptive filtering algorithm, the multiple model adaptive estimation tries to reduce the dependency of the filter on the noise parameters. It utilizes multiple models with different noise levels to estimate the state and combines the model-dependent estimates with model probability. However, with the increase in the number of active sensors, a large number of models are required to cover the entire range of the possible noise parameter values, which can become computationally infeasible. The main goal of this work is to incorporate the noise statistic estimator in the framework of the multiple model adaptive estimation, such that only two models are required for each sensor, which significantly reduce the complexity of the estimator. The advantage of the presented algorithm to deal with the model uncertainty is studied analytically. The high performance of the parallel model adaptive Kalman filtering for autonomous satellite navigation using inter-satellite line-of-sight measurements is illustrated in comparison with a robust Kalman filter, an intrinsically Bayesian robust Kalman filter, and the traditional multiple model adaptive estimation.

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