This paper proposes a new position tracking algorithm by integrating extended Kalman filter (EKF) and direction-of-arrival (DOA)-based geolocation into one factor graph (FG) framework. A distributed sensor network is assumed for detecting an anonymous target, where the process and observation equations in the state space model (SSM) are unknown. Importantly, the predicted state information can be utilized not only for filtering, but also for enhancing the observation process. To be specific, by taking the prediction into account as the a priori, a new FG scheme is proposed for GEolocation, denoted by FG-GE. The benefits are two-fold, compared with the conventional geolocation scheme which does not rely on the a priori information. First of all, significant performance improvement can be observed, in terms of the root mean square error (RMSE), when severe sensing errors are suddenly encountered. Furthermore, the proposed FG-GE can achieve dramatic reduction of computational complexity. In addition, this paper also proposes the use of a predicted Cramer-Rao lower bound (P-CRLB) to dynamically estimate the observation error variance, which demonstrates more robust tracking performance than that with only fixed average variance approximation.

中文翻译：

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基于 DOA 的地理定位和跟踪的综合因子图算法

本文通过将扩展卡尔曼滤波器 (EKF) 和基于到达方向 (DOA) 的地理定位集成到单因子图 (FG) 框架中，提出了一种新的位置跟踪算法。假设分布式传感器网络用于检测匿名目标，其中状态空间模型 (SSM) 中的过程和观测方程未知。重要的是，预测的状态信息不仅可以用于过滤，还可以用于增强观察过程。具体而言，通过将预测作为先验考虑，提出了一种新的用于地理定位的 FG 方案，记为 FG-GE。与不依赖先验信息的传统地理定位方案相比，好处是双重的。首先，就均方根误差 (RMSE) 而言，可以观察到显着的性能改进，当突然遇到严重的传感错误时。此外，所提出的 FG-GE 可以显着降低计算复杂度。此外，本文还提出使用预测的 Cramer-Rao 下界 (P-CRLB) 来动态估计观测误差方差，这证明了比仅使用固定平均方差近似的跟踪性能更稳健。