双RIS辅助的雷达目标位置与速度测量

Position and velocity measurement in dual RIS-assisted radar systems

可重构智能表面(reconfigurable intelligent surface,RIS)是一种由大量被动反射单元构成的平面阵列,并由低功耗的电子电路控制,在空域上可对电磁波灵活调控.本文提出了一种双RIS辅助雷达测量的方法,旨在提高雷达对目标位置和速度测量的准确度.首先,建立了由双RIS辅助的单基雷达信号模型,引入了运动目标的多普勒效应对RIS反射回波信号的影响.非相干RIS反射回波信号的引入,不仅可以极大地提高雷达测量的可用信息量,还可以使单基雷达获得目标三维绝对速度测量的能力.然后,推导了综合利用雷达和RIS的时延、方位角、俯仰角、多普勒信息对目标位置和速度估计的克拉美罗下界.最后,提出以克拉美罗下界为目标,利用遗传算法优化RIS反射系数矩阵的方法.仿真结果表明,本文提出方法的测量精度显著优于单基雷达,可以实现厘米级的位置测量精度与分米级的速度测量精度.

Reconfigurable intelligent surface(RIS)is a planar array made of many passive reflective elements and controlled by low-power electronic circuits,which can flexibly modulate electromagnetic waves in the space domain.In this paper,we propose a dual RIS-assisted radar measurement method to improve the measurement accuracies of target position and velocity.First,a signal model is established,and the Doppler effect of moving targets on the RIS indirect path is considered.The noncoherent RIS indirect signals can not only greatly increase the amount of available information for radar measurement but also enable three-dimensional absolute velocity measurements of the target for single-base radar.Then,we derive the Fisher information matrix and the Cramer-Rao lower bound for evaluating the target position and velocity measurement using time delay,azimuth,elevation,doppler of radar and RIS.Finally,a genetic algorithm for optimizing the RIS reflection coefficient matrix is proposed,setting the Cramer-Rao lower bound as the target.Our results show that the proposed method has considerably better measurement accuracy than single-base radar,achieving centimeter-and decimeter-level estimation error in position and velocity,respectively.