원문정보
초록
영어
In passive bearing-only localization of onboard mono-station, if abnormal error existed in observed value, using extended Kalman filter (EKF) algorithm can lead to biased results. For enhance the robustness of the algorithm, to construct robustness equivalent gain matrix on the basis of standardized predicted residual, and apply the robustness EKF algorithm to passive bearing-only localization of onboard mono-station. According to the characteristics of inefficient robustness EKF algorithm, combined with F distribution statistics, propose the mono-station passive location algorithm based on the improved extended Kalman filtering, and by adding single abnormal error and continuous abnormal error in observed value, test the resistant ability of algorithm to different abnormal errors. Simulation shows that the proposed algorithm can weaken the influence of abnormal error on position estimation, and can improve the efficiency of positioning based on F distribution discriminant algorithm.
목차
1. Introduction
2. Passive Bearing-only Localization Model of Onboard Mono-station
3. Robustness Extended Kalman Filtering Algorithm
3.1. Robustness EKF Filtering Algorithm
3.2. Robustness EKF Algorithm based on F Distribution Discrimination
4. Simulated Analysis
4.1. Simulated Parameter Setting
4.2. Results and Analysis
5. Conclusion
References