基于克拉美罗界加权的GNSS干扰源定位方法
A Localization Algorithm Based on Weighted Least Squares with Cramér-Rao Bound for GNSS Jammer
崔智超 1胡婧 1张剑 1程剑 2李广侠2
作者信息
- 1. 陆军工程大学通信工程学院,江苏南京 210007
- 2. 南京航空航天大学电子信息工程学院,江苏南京 211106
- 折叠
摘要
利用多普勒测量信息,低轨卫星能够实现对全球导航卫星系统(global navigation satellite sys-tem,GNSS)干扰源的快速普查.然而,由于低轨卫星的高动态特性,获得的多普勒观测量有限,导致基于经典最小二乘(least squares,LS)算法得到的定位精度有限.为提高GNSS干扰源的定位精度,提出一种基于多普勒测量克拉美罗界(Cramér-Rao bound,CRB)加权的LS定位算法,利用多普勒采样时刻的信噪比计算多普勒观测量的CRB,在LS定位算法中进行加权,使不同时刻多普勒观测量的噪声服从同方差性,则关于干扰源位置的LS解接近最优估计.在不同采样率、信噪比以及采样时长下,仿真验证了所提算法的有效性,同时通过构建基于FPGA的GNSS干扰采集和定位系统进行了实验验证.仿真及实测结果表明,该算法能够有效提升定位精度,相较于LS算法,定位性能改善比例为17.43％.
Abstract
Low earth orbit(LEO)satellites are capable of conducting rapid geolocation of global navi-gation satellite system(GNSS)interference sources with Doppler measurement data.However,the highly dynamic characteristics of LEO satellites limit the quantity of acquired Doppler observations,consequently restricting the localization accuracy achievable through conventional least squares algorithms.To enhance the accuracy,a weighted least squares localization algorithm based on Doppler measurement Cramér-Rao bound(CRB)is proposed in this paper.This method incorporates a signal-to-noise ratio(SNR)lookup ta-ble for Doppler sampling instances to ascertain the Cramér-Rao bound(CRB),thereby standardizing the noise across various Doppler observation moments through weighting.This adjustment ensures that the least squares solution for the interference source location is made close to the optimal estimation.The ef-fectiveness of the proposed algorithm was verified by simulation under different sampling rates,SNRs and sampling durations.The FPGA-based GNSS interference acquisition and localization system was also con-structed and tested in real scenarios.The simulation and experiment results show that the proposed algo-rithm significantly improves the localization accuracy by 17.43％,compared with the least squares algorithm.
关键词
克拉美罗界/全球导航卫星系统/干扰源定位/加权最小二乘Key words
Cramér-Rao bound(CRB)/global navigation satellite system(GNSS)/interference source localization/weighted least squares引用本文复制引用
出版年
2024
NETL
NSTL
万方数据