Multi-Target Position and Velocity Estimation Using OFDM Communication Signals

被引:61
作者
Li, Yinchuan [1 ,2 ,3 ]
Wang, Xiaodong [3 ]
Ding, Zegang [1 ,2 ]
机构
[1] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[2] Beijing Key Lab Embedded Real Time Informat Proc, Beijing 100081, Peoples R China
[3] Columbia Univ, Elect Engn Dept, New York, NY 10027 USA
基金
中国国家自然科学基金;
关键词
Passive radar; Receivers; Demodulation; OFDM; Estimation; Clutter; Localization; velocity estimation; passive radar; super-resolution; non-convex; conjugate gradient descent; atomic norm; neural network; off-grid; sparsity; PASSIVE RADAR; TARGET DETECTION; MIMO RADAR; TRACKING; ALGORITHM; REMOVAL; DVB;
D O I
10.1109/TCOMM.2019.2956928
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
In this paper, we consider a passive radar system that estimates the positions and velocities of multiple moving targets by using OFDM signals transmitted by a totally un-coordinated and un-synchronizated illuminator and multiple receivers. It is assumed that data demodulation is performed separately based on the direct-path signal, and the error-prone estimated data symbols are made available to the passive radar receivers, which estimate the positions and velocities of the targets in two stages. First, we formulate a problem of joint estimation of the delay-Doppler of reflectors and the demodulation errors, by exploiting two types of sparsities of the system, namely, the numbers of reflectors (i.e., targets and clutters) and demodulation errors are both small. This problem is non-convex and a conjugate gradient descent method is proposed to solve it. Then in the second stage we determine the positions and velocities of targets based on the estimated delay-Doppler in the first stage. For the second stage, two methods are proposed: the first is based on numerically solving a set of nonlinear equations, while the second is based on the neural network, which is more efficient. The performance of the proposed algorithms is evaluated through extensive simulations.
引用
收藏
页码:1160 / 1174
页数:15
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