Improving the reliability of drone navigation solutions based on the interacting multiple model Kalman filter

Abstract

The article considers the problem of decision-making in drone navigation in noisy conditions and with sensor degradation, when the classic Kalman filter with fixed noise statistics is unable to adapt quickly enough to changes in measurement quality. An approach based on the Interacting Multiple Model Kalman Filter is proposed and implemented, in which several estimation modes with different assumptions about the degradation of LiDAR, optical flow, IMU, and compass work in parallel, and the resulting state estimate is formed by a weighted combination of the posterior probabilities of the modes.

The evaluation was performed in a drone flight simulation in two series of experiments. In series A, a comparison of KF and IMM-KF was performed at four noise levels, where the success rate, mission duration, and RMSE of the state estimation on successful runs were measured, as well as the causes of failures. In series B, the correctness of the IMM mode switching mechanism was verified in controlled scenarios of spatial and temporal degradations. The probabilities of modes, the determination of the noise mode by the drone, the contrast between active and inactive intervals, and the switching delay were analyzed. The results show that IMM-KF increases stability in strong degradations without changing the autopilot, and the mode probability metric is an interpretable indicator of changes in the quality of sensor measurements.