Multiple-model adaptive estimation using a residual correlation Kalman filter bank

2000 ◽  
Vol 36 (2) ◽  
pp. 393-406 ◽  
Author(s):  
P.D. Hanlon ◽  
P.S. Maybeck
Keyword(s):  
Kalman Filter ◽  
Filter Bank ◽  
Adaptive Estimation ◽  
Multiple Model ◽  
Residual Correlation ◽  
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.

Window based Multiple Model Adaptive Estimation for Navigational Framework

2016 ◽  
Vol 50 ◽  
pp. 88-95 ◽  
Author(s):  
Rahul Kottath ◽  
Shashi Poddar ◽  
Amitava Das ◽  
Vipan Kumar
Keyword(s):  
Adaptive Estimation ◽  
Multiple Model ◽  
Multiple Model Adaptive Estimation

scholarly journals Multiple Model Adaptive Estimation of a Hybrid Solid Oxide Fuel Cell Gas Turbine Power Plant Simulator

2018 ◽  
Vol 15 (3) ◽  
Author(s):  
Alex Tsai ◽  
David Tucker ◽  
Tooran Emami
Keyword(s):  
Fuel Cell ◽  
Power Plant ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Adaptive Estimation ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Multiple Model ◽  
Multiple Model Adaptive Estimation ◽  
Operating Points

Operating points of a 300 kW solid oxide fuel cell gas turbine (SOFC-GT) power plant simulator are estimated with the use of a multiple model adaptive estimation (MMAE) algorithm. This algorithm aims to improve the flexibility of controlling the system to changing operating conditions. Through a set of empirical transfer functions (TFs) derived at two distinct operating points of a wide operating envelope, the method demonstrates the efficacy of estimating online the probability that the system behaves according to a predetermined dynamic model. By identifying which model the plant is operating under, appropriate control strategies can be switched and implemented. These strategies come into effect upon changes in critical parameters of the SOFC-GT system—most notably, the load bank (LB) disturbance and fuel cell (FC) cathode airflow parameters. The SOFC-GT simulator allows the testing of various FC models under a cyber-physical configuration that incorporates a 120 kW auxiliary power unit and balance-of-plant (Bop) components. These components exist in hardware, whereas the FC model in software. The adaptation technique is beneficial to plants having a wide range of operation, as is the case for SOFC-GT systems. The practical implementation of the adaptive methodology is presented through simulation in the matlab/simulink environment.

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