Adaptive Techniques for Tuning of Process Noise in the Kalman filter
Typ
Examensarbete för masterexamen
Program
Publicerad
2021
Författare
Jose, Athulya
Raghunath Banthi, Shreya
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis work focuses on tuning the process noise of the Kalman filter for sur rounding object detection and speed estimation in automotive driver assistance ap plications. We have created a simple road scenario in Matlab, where an ego-vehicle
follows a target-car moving at different speeds and yaws in different road scenarios
to collect the required data for the Kalman filter. The tracking algorithms help
in predicting the future position of moving objects based on the measurements of
RADAR detections. The RADAR sensors attached to the ego-vehicle directly mea sure the range, azimuth angle and range rate of the target object. We considered the
compensated range rate and azimuth angle measurements from the RADAR sensors.
Since the RADAR measurements are noisy, the Kalman filter is used to obtain a
better result. It is well known that the covariance matrices of the process noise (Q)
and the measurement noise (R) have a significant impact on the performance of the
Kalman filter in estimating dynamic states. We begin by implementing the con ventional ad-hoc approaches to estimating the covariance matrices. However, these
approaches for estimating the covariance matrices, may not be suitable for achieving
the best filter performance. To address this problem, we propose a adaptive filtering
approach to estimate Q based on innovation and residual to improve the accuracy of
the dynamic state estimation of the extended Kalman filter (EKF). We also present
theoretical investigation methods for optimizing the algorithm when it is to be im plemented in an embedded platform. Following that we present the results obtained
in our work after comparing the fixed and the adaptive Kalman filter, which clearly
points that the adaptive Kalman Q is better in its response time and adaptivity.
Beskrivning
Ämne/nyckelord
Kalman filter , Process Noise , Adaptive Tuning , Extended Kalman filter (EKF)