Anti-delay Kalman filter fusion algorithm for inter-vehicle sensor network with finite-step convergence

Abstract

Intelligent vehicle applications in autonomous driving and obstacle avoidance commonly require the precise relative state of vehicles. Accordingly, this study focuses on the coordinate fusion of vehicle state problem experienced by an inter-vehicle sensor network with time-varying transmission delays. Using the ingeniously designed low-complexity integration with a consensus strategy and buffer technology, an anti-delay distributed Kalman filter (DKF) with finite-step convergence is proposed. By introducing an appropriate weight matrix to assess local estimates, the optimal fusion state result is available as a linear minimum variance. In addition, to accommodate practical engineering in intelligent vehicles, the communication weight coefficient and directed topology with unidirectional transmission are 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 inter-vehicle sensor network are derived backwards. Simulations verify that while considering the various non-ideal factors presented above, the proposed DFK algorithm produces more accurate and robust fusion estimation state results than those of the existing algorithms, making it more valuable in practical applications. Moreover, a mobile car trajectory tracking experiment is conducted, which further verifies the feasibility of the proposed algorithm.