Noncircular Signal Tracking With Distributed Passive Arrays: Combining Data Fusion and Extended Kalman Filter

被引:2
作者
Cao, Jinke [1 ,2 ]
Zhang, Xiaofei [1 ,2 ]
Hao, Honghao [1 ,2 ]
Shi, Xinlei [1 ,2 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Minist Ind & Informat Technol, Nanjing 210016, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Key Lab Dynam Cognit Syst Electromagnet Spectrum S, Minist Ind & Informat Technol, Nanjing 210016, Peoples R China
关键词
Signal processing algorithms; Mathematical models; Radar tracking; Trajectory; Array signal processing; Sensor arrays; Target tracking; data fusion; distributed passive arrays; extended Kalman filter (EKF); extended signals; noncircular (NC) signal; position tracking; DIRECT POSITION DETERMINATION; DIRECTION-OF-ARRIVAL; DOA ESTIMATION; LEAST-SQUARES; MUSIC; DELAY;
D O I
10.1109/JSEN.2023.3333863
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:757 / 768
页数:12
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