An improved nonlinear filter based on adaptive fading factor applied in alignment of SINS
| Autor: | Feng Li, Feng Zha, Shiluo Guo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Mahalanobis distance
Computer science 02 engineering and technology Kalman filter Filter (signal processing) Covariance 021001 nanoscience & nanotechnology 01 natural sciences Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials 010309 optics Robustness (computer science) Nonlinear filter 0103 physical sciences Fading Electrical and Electronic Engineering 0210 nano-technology Algorithm Inertial navigation system Statistical hypothesis testing |
| Zdroj: | Optik. 184:165-176 |
| ISSN: | 0030-4026 |
| DOI: | 10.1016/j.ijleo.2019.01.100 |
| Popis: | This paper investigates the non-linear initial alignment for strapdown inertial navigation system(SINS) with main focus on improving the robustness of alignment filter. Conventional Kalman filter (KF) assumes that the statistic characteristics of the system noise are known in advance and keep unchanged during the filtering process. However, it is difficult to predict the noise characteristics in practice, which may cause the degradation in filter performance. In view of this problem, improved fading unscented Kalman filter (UKF) is proposed. The square of the Mahalanobis distance of the innovation vector, which is found to be chi-square distributed, is used as the judging index. Hypothesis test is performed to test the filter state. If the null hypothesis should be rejected, it means that the abnormal noises exist in the system model, and the fading factors should be introduced to rescale the covariance of the innovation vector. The multiple fading factors are calculated by forcing the estimated value of innovation sequence covariance to be equal to its nominal value. Simulation and experiment results show that, the new alignment algorithm performs better in terms of robustness and convergence in the condition of complex measurement noise. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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