观测站有位置误差的多维标度时频差定位算法

定位精度对观测站位置误差很敏感。论文针对观测站位置和速度有误差的情况，提出了一种加权多维标度时频差定位算法。该算法利用了多维标度定位通过特征结构和维度信息抑制噪声的优势，它将定位残差表示成测量误差和观测站位置误差的线性形式，然后通过加权最小二乘给出了目标位置和速度估计的闭式解。仿真结果表明，在小的测量误差和观测站位置误差时，该算法对目标位置和速度的估计能够达到克拉美劳下界。与两步加权最小二乘和约束总体最小二乘算法相比，该算法在测量噪声和观测站位置误差较大时有更高的定位精度。 

Multidimensional scaling-based passive emitter localization from time difference of arrival and frequency difference of arrival measurements with sensor location uncertainties

The accuracy of an emitter location estimate is very sensitive to the accurate receiver locations. This paper proposed a novel weighted multidimensional scaling (MDS) algorithm for estimating the position and velocity of a moving emitter with sensor location uncertainties using time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. MDS is an attractive technique for robust localization with large measurement noises due to the dimension knowledge and eigen-structure information. This weighted MDS algorithm formulates the localization residual with the linear form of measurement noises and sensor location errors and then uses weighted least squares (WLS) to estimate the location of the emitter. It is closed-form. Simulation results show that the proposed estimator can reach the Cramer-Rao lower bound (CRLB) in small noise region. It achieves better performance than the two-step WLS estimator and constrained total least squares (CTLS) estimator in moderate noise region.