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.

中文翻译：

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有限步收敛的车间传感器网络抗延迟卡尔曼滤波融合算法

自动驾驶和避障等智能车辆应用通常需要车辆精确的相对状态。因此，本研究重点关注具有时变传输延迟的车间传感器网络所经历的车辆状态问题的坐标融合。利用巧妙设计的低复杂度集成与共识策略和缓冲技术，提出了一种具有有限步收敛的抗延迟分布式卡尔曼滤波器（DKF）。通过引入适当的权重矩阵来评估局部估计，最佳融合状态结果可作为线性最小方差获得。此外，为了适应智能汽车的实际工程，还考虑了通信权重系数和单向传输的有向拓扑。从理论角度，给出了不同延迟通信拓扑的误差协方差上限的证明。此外，向后推导车间传感器网络的最大允许延迟。仿真验证了在考虑上述各种非理想因素的情况下，所提出的DFK算法比现有算法产生更准确和鲁棒的融合估计状态结果，使其在实际应用中更有价值。此外，还进行了移动小车轨迹跟踪实验，进一步验证了所提算法的可行性。