National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2020 Jan 31;20(3):788. doi: 10.3390/s20030788.
In airborne passive bistatic radar (PBR), the reference channel toward the opportunity illuminator is applied to receive the direct-path signal as the reference signal. In the actual scenario, the reference signal is contaminated by the multipath signals easily. Unlike the multipath signal in traditional ground PBR system, the multipath signal in the airborne PBR owns not only the time delay but also the Doppler frequency. The contaminated reference signal can cause the spatial-temporal clutter spectrum to expand and the false targets to appear. The performance of target detection is impacted severely. However, the existing blind equalization algorithm is unavailable for the contaminated reference signal in airborne PBR. In this paper, the modified blind equalization algorithm is proposed to suppress the needless multipath signal and restore the pure reference signal. Aiming at the Doppler frequency of multipath signal, the high-order moment information and the cyclostationarity of source signal are exploited to construct the new cost function for the phase constraint, and the complex value back propagation (BP) neural network is exploited to solve the constraint optimization problem for the better convergence. In final, the simulation experiments are conducted to prove the feasibility and superiority of proposed algorithm.
在空基无源雷达 (PBR) 中，参考通道朝向机会照射器，用于接收直接路径信号作为参考信号。在实际情况下，参考信号很容易受到多径信号的干扰。与传统地面 PBR 系统中的多径信号不同，空基 PBR 中的多径信号不仅具有时间延迟，还有多普勒频率。受污染的参考信号会导致空时杂波谱展宽并出现虚假目标，严重影响目标检测性能。然而，现有的盲均衡算法不适用于空基 PBR 中的受污染参考信号。本文提出了一种改进的盲均衡算法，用于抑制不必要的多径信号并恢复纯净的参考信号。针对多径信号的多普勒频率，利用高阶矩信息和源信号的循环平稳性构建新的相位约束代价函数，并利用复值反向传播 (BP) 神经网络求解约束优化问题，以获得更好的收敛性。最后，通过仿真实验验证了所提算法的可行性和优越性。
