基于无人机与空间稀疏性的卫星干扰源定位方法

经典的卫星干扰源定位是基于TDOA/FDOA参数测量的双星定位，包含信号参数估计和目标干扰源定位两个阶段。在参数估计阶段，该方法并没有考虑到所有的信号参数测量都是相对于同一位置的干扰源，因此该定位算法并不优化。此外，卫星星历的精度不高尤其是星历中卫星速度难以精确预测，导致了较大的多普勒频差参数估计误差，从而制约了卫星干扰源的定位精度。本文提出使用单个无人机代替卫星来接收干扰信号，利用干扰源在地面空间分布的稀疏性，求解满足目标信号多普勒频移关系的凸优化问题来实现干扰源定位。该定位方法融合了所有测量信号的信息，在一个阶段中完成定位，蒙特卡洛仿真证实该方法能实现高精度卫星干扰源定位。 

Satellite Interference Geolocation Method Using UAV and Spatial Sparsity

The classic dual-satellite geolocation method using TDOA/FDOA parameters measurement includes two stages, signal parameters estimation and interfering target localization. In the first stage, parameters are estimated by ignoring the fact that all measurements should be consistent with a single transmitter location, thus the localization algorithm is not optimal. Furthermore, the satellite ephemeris is inaccuracy especially that the satellite velocity is unpredictable, which causes serious errors in Doppler frequency offset parameter estimation. Therefore, the geolocation accuracy is limited. This paper proposes a novel satellite interference geolocation method, applies single UAV(Unmanned aerial vehicle) to receive the interference signal instead of dual satellites, uses the spatial sparsity distribution property of the transmitter, and solves a convex problem with Doppler shift related to the location of the unknown transmitter. The method combines the information of all the measured signal, performs geolocation in a single stage, Monte Carlo simulation confirms that this method achieves high geolocation accuracy.