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

Abstract

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.

Integrated autonomous optical navigation using Q-Learning extended Kalman filter

“…It is a good candidate for performance enhancement as the same optical instruments can be shared to measure both the inter-star angle and inter-satellite LOS. The autonomous navigation technology based on the inter-satellite LOS measurements is shown to produce impressive navigation results for scenarios involving two or more cooperative satellites (Psiaki, 2011; Xiong et al , 2019). For the integrated optimal navigation (ION) method that fuses the inter-star angle and the inter-satellite LOS measurements, as the target satellite may be non-cooperative, the kinematic states of both the spacecraft and the target satellite are estimated, such that the position information from the navigation system on the target satellite is not necessary.…”

Research on velocity measurement and relative positioning method of maglev train based on multi-sensor information fusion

“…S. Han 20 uses a two-speed Kalman filter to deal with the rate difference problem. Xiong et al 21 proposed a parallel model adaptive Kalman filter algorithm, which incorporates the noise statistical estimator into the framework of multi-model adaptive estimation, thereby reducing the complexity of the estimator. L. Tan 22 studied the satellite lock-out and indoor pedestrian navigation models in outdoor sheltered areas, but they were greatly affected by other interference signals.…”