Noncircular Signal Tracking With Distributed Passive Arrays: Combining Data Fusion and Extended Kalman Filter
Jinke Cao, Xiaofei Zhang, Honghao Hao, Xinlei Shi
Abstract
This article is concerned with the problem of tracking the location of moving noncircular (NC) signal emitters through distributed passive arrays. Conventional methods usually assume that the emitters are stationary over a small time frame and obtain the tracking trajectory by repeated position estimation. However, these methods exhibit severe performance degradation in nonstationary environments. In this article, we propose a new method based on the extended Kalman filter (EKF) for tracking multiple emitters snapshot by snapshot. This method exploits the inherent properties of NC signals to amalgamate the received signals from observation stations, thus enabling the formulation of a novel observation equation. Furthermore, we employ the least squares method (LSM) and linear assumptions (LAs) to estimate the signals impinging on the array, ensuring that the resulting observation equation contains only the distinctive variables associated with the emitter state. Finally, we construct the multiobjective state transfer equation and iteratively implement tracking using the EKF. Compared with traditional algorithms, the proposed algorithm requires no data association and has higher tracking accuracy. The superiority of the proposed method is supported by numerous simulation results.