Tracking targets with bearings-only measurement is a great challenge caused by poor observability and highly nonlinear estimation. In this brief, a novel augmented ensemble Kalman filter (AEnKF) is presented to address this bearings-only tracking problem. Different from the conventional ensemble Kalman filter (EnKF), the AEnKF overcomes the limitation of the linear measurement update rule in the linear minimum mean-square error (LMMSE) framework. The AEnKF utilizes a nonlinear transform of the measurement, called uncorrelated conversion (UC), to augment the measurement space. This conversion serves as a pseudomeasurement and is uncorrelated with the original measurement statistically. Unlike other UC filters based on the Gaussian assumption in the existing literature, the AEnKF does not impose any assumption on the probability density of the measurement by using generalized orthogonal polynomials to construct the UCs in a systematic way. The simulation results show that the AEnKF outperforms the conventional EnKF and other UC filters in the bearings-only tracking problem.

中文翻译：

---

使用增强的集成卡尔曼滤波器进行仅轴承的跟踪

仅靠方位测量来跟踪目标是可观察性差和高度非线性估计所带来的巨大挑战。在本简介中，提出了一种新颖的增强集合卡尔曼滤波器（AEnKF），以解决仅轴承的跟踪问题。与传统的集成卡尔曼滤波器（EnKF）不同，AEnKF克服了线性最小均方误差（LMMSE）框架中线性测量更新规则的限制。AEnKF利用测量的非线性变换（称为不相关转换（UC））来扩大测量空间。此转换用作伪测量，并且在统计上与原始测量不相关。与现有文献中基于高斯假设的其他UC滤波器不同，通过使用广义正交多项式以系统的方式构造UC，AEnKF不会对测量的概率密度施加任何假设。仿真结果表明，在仅轴承跟踪问题中，AEnKF优于传统的EnKF和其他UC滤波器。