This paper investigates the distributed 3D bearing-only target tracking problem in UAV network. Because of the limited computation capability and constrained power supply of UAV, traditional time-triggered nonlinear filters would inevitably cause serious network congestions. Thus, we proposed an efficient yet effective algorithm named event-based distributed pseudomeasurement information filter (EB-PMIF) by combining the deterministic Send-on-Delta triggering event-based mechanism and pseudomeasurement information filter in order to reduce the computation load and communication requirements. Since the correlated pseudomeasurement matrix and pseudomeasurement noise in EB-PMIF cause bias estimation, we analyzed the root cause of the bias and proposed a bias compensation filter named event-based distributed bias compensation pseudomeasurement information filter (EB-PMIF-BC) to eliminate the bias and improve the estimation accuracy. The proposed EB-PMIF and EB-PMIF-BC are demonstrated with an illustrative 3D bearing-only target tracking example. Simulation results verify the efficiency and effectiveness of the proposed methods.

本文研究了无人机网络中的分布式 3D 仅轴承目标跟踪问题。由于无人机计算能力有限、供电受限，传统的时间触发非线性滤波器不可避免地会造成严重的网络拥塞。因此，我们提出了一种高效而有效的算法，称为基于事件的分布式伪测量信息过滤器（EB-PMIF），通过结合确定性的 Send-on-Delta 触发基于事件的机制和伪测量信息过滤器，以减少计算负载和通信需求。 . 由于 EB-PMIF 中的相关伪测量矩阵和伪测量噪声会导致估计偏差，我们分析了偏差的根本原因，并提出了一种名为基于事件的分布式偏差补偿伪测量信息滤波器（EB-PMIF-BC）的偏差补偿滤波器，以消除偏差并提高估计精度。所提出的 EB-PMIF 和 EB-PMIF-BC 使用说明性的 3D 仅轴承目标跟踪示例进行了演示。仿真结果验证了所提出方法的有效性和有效性。