In this paper, a direction-of-arrival (DOA)-based factor graph (FG) technique is proposed for three-dimensional (3D) tracking. Multiple sensors are utilized in this system, which could measure both azimuth and elevation DOAs emitted from an anonymous target. To realize non-linear tracking, a modified extended Kalman filter (EKF) is proposed. Specifically, on the one hand, the proposed EKF observer is no longer independent of the EKF predictor, but instead takes the predicted target location into its operation. With this technique, the accuracy of detection is improved while the computational complexity is dramatically reduced. On the other hand, the variance of the EKF observer error is estimated in real time, based on the predicted Cramer-Rao bound (PCRB). Therefore, the robustness of detection can be guaranteed even with an unstable sensing environment. In this sense, the EKF observer and the EKF predictor are operated in an integrated FG framework. The advantages of the proposed technique are verified by both complexity analyses and simulation results.

中文翻译：

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用于多传感器系统的基于 DOA 的 3D 跟踪和因子图技术


在本文中，提出了一种用于三维（3D）跟踪的基于到达方向（DOA）的因子图（FG）技术。该系统使用多个传感器，可以测量匿名目标发射的方位角和仰角 DOA。为了实现非线性跟踪，提出了一种改进的扩展卡尔曼滤波器（EKF）。具体来说，一方面，所提出的 EKF 观测器不再独立于 EKF 预测器，而是将预测的目标位置纳入其操作中。通过这种技术，检测的准确性得到了提高，同时计算复杂度也大大降低了。另一方面，EKF 观测器误差的方差是根据预测的 Cramer-Rao 界限 (PCRB) 实时估计的。因此，即使在不稳定的传感环境下也能保证检测的鲁棒性。从这个意义上说，EKF 观察器和 EKF 预测器在集成的 FG 框架中运行。复杂性分析和仿真结果都验证了所提出技术的优点。