AI 中文总结
针对多基地雷达系统中接收机姿态不确定的问题，推导了目标定位与接收机姿态估计的CRLB，提出联合优化目标与接收机参数的交替加权最小二乘算法，其在低至中等噪声下性能接近CRLB。
AI 中文摘要
多基地雷达系统中，多个接收机协同工作以提高目标定位精度，其应用场景广泛，包括协同同步定位与建图（SLAM）以及自主机器人网络。这些应用中的一个关键挑战是，由于平台的移动性，雷达接收机的位置和姿态（pose）存在不确定性。本研究通过推导执行双基地距离和方位测量的多基地雷达系统的克拉美罗下界（CRLB），探究了目标定位和接收机姿态估计两方面可实现的性能提升。我们提出了一种交替加权最小二乘算法，该算法可联合优化目标和接收机参数。蒙特卡洛仿真表明，在低至中等噪声水平下，该算法的性能接近CRLB。
英文摘要
Target localization in a multistatic radar system, where multiple receivers cooperate to improve target positioning accuracy, has many applications, including cooperative simultaneous localization and mapping (SLAM) and autonomous robot networks. A key challenge in these applications is the uncertainty in the position and orientation (pose) of the radar receivers due to platform mobility. This work investigates the achievable improvements in both target localization and receiver pose estimation by deriving the Cramer-Rao lower bound (CRLB) for a multistatic radar system performing bistatic range and bearing measurements. We propose an alternating weighted least-squares algorithm that jointly optimizes target and receiver parameters. Monte Carlo simulations demonstrate that the algorithm performance approaches the CRLB for low to moderate noise levels.
