Discriminative Parameter Training of the Extended Particle-Aided Unscented Kalman Filter for Vehicle Localization

Abstract

Location is one of the most important parameters of a self-driving car. To filter the sensor noise, we proposed the extended particle-aided unscented Kalman filter (PAUKF). Although the performance of the PAUKF improved, it still needed parameter tuning as other Kalman filter applications do. The characteristic of noise is important to the filter’s performance; the most important parameters therefore are the variances of the measurement. In most Kalman filter research, the variance of the filter is tuned manually, costing researchers plenty of time and yielding non-optimized results in most applications. In this paper, we propose a method that improves the performance of the extended PAUKF based on the coordinate descent algorithm by learning the most appropriate measurement variances. The results show that the performance of the extended PAUKF improved compared to the manually tuned extended PAUKF. By using the proposed training algorithm, practicability, training time efficiency and the estimation precision of the PAUKF improved compared to previous research.