A Close Examination of Multiple Model Adaptive Estimation Vs Extended Kalman Filter for Precision Attitude Determination

2013 ◽  
Author(s):  
Quang M. Lam ◽  
John L. Crassidis
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Determination ◽  
Adaptive Estimation ◽  
Multiple Model ◽  
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 A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

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