A new polar alignment algorithm based on the Huber estimation filter with the aid of BeiDou Navigation Satellite System. (March 2021)

• Record Type: Journal Article
• Authors: Zhao, Bin; Zeng, Qinghua; Liu, Jianye; Gao, Chunlei; Zhao, Tianyu
• Is Part Of: International journal of distributed sensor networks. Volume 17:Number 3(2021)
• Publication Date: 2021-03
• Subjects: Initial alignment -- BeiDou Navigation Satellite System -- polar regions -- grid frame -- non-Gaussian characteristics -- Huber estimation filter; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/15501477211004115
• Languages: English
• ISSNs: 1550-1329

Abstract

For aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved.