Anti-Delay Kalman Filter Fusion Algorithm for Vehicle-borne Sensor Network with Finite-Time Convergence

TL;DR

This paper introduces a novel distributed Kalman filter algorithm designed for vehicle sensor networks that effectively handles communication delays, ensuring accurate and robust state estimation for autonomous vehicles in real-world conditions.

Contribution

The paper proposes an anti-delay distributed Kalman filter with finite-time convergence, incorporating matrix weights, buffer technology, and directed topology considerations, advancing vehicle state estimation methods.

Findings

The proposed algorithm achieves more accurate state estimation compared to existing methods.

It provides theoretical bounds on error covariances under various delay conditions.

Experimental results confirm the algorithm's robustness and practical applicability in vehicle trajectory tracking.

Abstract

Intelligent vehicles in autonomous driving and obstacle avoidance, the precise relative state of vehicles put forward a higher demand. For a vehicle-borne sensor network with time-varying transmission delays, the problem of coordinate fusion of vehicle state is the focus of this paper. By the ingeniously designed low-complexity integration with a consensus strategy and buffer technology, an anti-delay distributed Kalman filter (DKF) with finite-time convergence is proposed.By introducing the matrix weight to assess local estimates, the optimal fusion state result is available in the sense of linear minimum variance. In addition, to accommodate practical engineering in intelligent vehicles, the communication weight coefficient and directed topology with unidirectional transmission are also considered. From a theoretical perspective, the proof of error covariances upper bounds with different communication topologies with delays are presented. Furthermore, the maximum allowable delays of vehicle-borne sensor network is derived backwards. Simulations verify that while considering various non-ideal factors above, the proposed DFK algorithm produces more accurate and robust fusion estimation state results than existing algorithms, making it more valuable in practical applications. Simultaneously, a mobile car trajectory tracking experiment is carried out, which further verifies the feasibility of the proposed algorithm.