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Intelligent vehicles in autonomous driving and obstacle avoidance, theprecise relative state of vehicles put forward a higher demand. For avehicle-borne sensor network with time-varying transmission delays, the problemof coordinate fusion of vehicle state is the focus of this paper. By theingeniously designed low-complexity integration with a consensus strategy andbuffer technology, an anti-delay distributed Kalman filter (DKF) withfinite-time convergence is proposed.By introducing the matrix weight to assesslocal estimates, the optimal fusion state result is available in the sense oflinear minimum variance. In addition, to accommodate practical engineering inintelligent vehicles, the communication weight coefficient and directedtopology with unidirectional transmission are also considered. From atheoretical perspective, the proof of error covariances upper bounds withdifferent communication topologies with delays are presented. Furthermore, themaximum allowable delays of vehicle-borne sensor network is derived backwards.Simulations verify that while considering various non-ideal factors above, theproposed DFK algorithm produces more accurate and robust fusion estimationstate results than existing algorithms, making it more valuable in practicalapplications. Simultaneously, a mobile car trajectory tracking experiment iscarried out, which further verifies the feasibility of the proposed algorithm.

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