| 摘要: |
| 现有无源定位闭式算法均考虑视距(Line of sight, LOS)环境，无法直接应用于存在遮挡的城市环境低空无人机目标定位等场景，同时，非视距(Non-line of sight, NLOS)优化定位算法计算效率较低。针对这些问题，本文开展中继辅助下的单站目标定位研究，通过引入中继收发器对目标信号进行转发，构造两条路径从而规避遮挡问题，同时考虑中继和观测站位置存在随机误差，提出了一种闭式算法来确定未知目标位置。该算法分为三个步骤：首先利用校准目标-中继收发器-观测站这一路径的额外信息，修正中继和观测站位置；随后基于未知目标-中继收发器-观测站获取的观测信息，通过引入额外变量的方式构建伪线性方程，利用加权最小二乘技术给出目标位置粗略估计；最后进一步挖掘目标位置与额外变量的非线性关系，再次构建矩阵方程并给出目标位置最终估计解。理论分析与仿真结果表明，所提算法在适度测量误差和观测站位置误差条件下可达到克拉美罗下界(Cramer-Rao lower bound, CRLB)。 |
| 关键词: 中继收发器 位置误差 校准目标 闭式算法 克拉美罗下界(Cramer-Rao lower bound, CRLB) |
| DOI： |
| 分类号:TN971 |
| 基金项目:陕西省自然科学基础研究计划资助(No.2023-JC-QN-0743) |
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| Single-Station Target Localization Algorithm assisted by Transceivers |
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| Abstract: |
| Existing passive localization closed-form algorithms all assume a line-of-sight (LOS) environment and cannot be directly applied to scenarios such as low-altitude unmanned aerial vehicle (UAV) target localization in urban environments with obstructions. Additionally, non-line-of-sight (NLOS) optimization localization algorithms have lower computational efficiency. To address these issues, this paper investigates single-station target localization assisted by transceivers. Introducing transceivers to forward target signals constructs two paths to avoid obstruction problems. A closed-form algorithm is proposed to determine the unknown target location by considering the transceiver and receiver position errors. The algorithm consists of three steps: First, we use the additional information from the propagation path of the calibrated target-transceiver-observation station to correct the sensor positions. Subsequently, based on the observation information obtained from the unknown target-transceiver-observation station, we construct pseudo-linear equations by introducing additional variables and provide the rough position estimate based on the weighted least square technique. Finally, the nonlinear relationships between the target's position and the extra variables are utilized to reconstruct the matrix equation and provide the final estimated solution. Theoretical analysis and simulation results show that the proposed algorithm can achieve the Cramer-Rao lower bound (CRLB) under moderate measurement and position errors. |
| Key words: transceivers position errors calibration object closed-form algorithm Cramer-Rao Lower Bound (CRLB) |