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.

中文翻译：

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使用 OFDM 通信信号的多目标位置和速度估计

在本文中，我们考虑一种无源雷达系统，该系统通过使用由完全不协调和不同步的照明器和多个接收器传输的 OFDM 信号来估计多个移动目标的位置和速度。假设数据解调是基于直接路径信号单独执行的，并且容易出错的估计数据符号可用于无源雷达接收器，无源雷达接收器分两个阶段估计目标的位置和速度。首先，我们利用系统的两种稀疏性，即反射体（即目标和杂波）的数量和解调误差都较小，提出了反射体延迟多普勒和解调误差的联合估计问题。 . 这个问题是非凸的，提出了共轭梯度下降法来解决它。然后在第二阶段，我们根据第一阶段估计的延迟多普勒确定目标的位置和速度。对于第二阶段，提出了两种方法：第一种基于数值求解一组非线性方程，而第二种基于神经网络，效率更高。所提出算法的性能通过广泛的模拟进行评估。