无线传感器网络中基于TDOA/FDOA的增强半正定松弛定位算法研究

本文在无线传感器网络定位问题中，考虑了基于到达时间差（Time-Difference-of-Arrival，TDOA）和到达频率差（Frequency-Difference-of-Arrival，FDOA）的移动未知目标定位问题，TDOA/FDOA联合定位可以有效利用传感器的位置和速度信息，提高了定位精度。本文在现有的半正定松弛（Semidefinite Relaxation, SDR）方法的基础上，提出了一种增强半正定松弛方法。通过挖掘现有半正定规划问题中优化变量之间的内在联系并将这些联系转化为凸约束，有效提高了现有半正定松弛方法的紧度，从而使估计的未知目标的位置和速度精度达到了克拉美-罗下界 (Cramer Rao lower bound，CRLB)。仿真结果表明，该方法的性能在大噪声时优于现有方法。

Enhanced Semidefinite Relaxation Method for TDOA/FDOA-Based Source Localization in Wireless Sensor Networks

In this paper, we address the moving source localization problem by using both time-difference-of arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements in wireless sensor network, joint TDOA/FDOA localization can effectively use the sensors position and velocity information to improve localization accuracy. We propose an enhanced semidefinite relaxation (SDR) method based on the existing SDR method. By excavating the intrinsic relations between the optimization variables in the existing semidefinite programing (SDP) problem and converting them into convex constraints, we effectively improve the tightness of the SDR method, so that the accuracy of the estimated source position and velocity can reach the Cramer-Rao lower bound (CRLB). Simulation results show that the performance of the proposed method outperforms that existing methods when the noise is large.