Abstract: To solve the single-observer passive location estimation using illuminators of opportunity, a jointing TDOA and FDOA location algorithm based on maximum likelihood is proposed. Firstly, according to the TDOA and FDOA measurement equations, the maximum likelihood model is constructed. Then Newton’s method is applied to solve the maximum likelihood model to obtain the estimation of the target position and velocity. The initial iteration value is given by a Least Squares（LS） algorithm. The Cramer-Rao Lower Bound（CRLB） and the theoretical error of the proposed algorithm are also derived and they are proved equal. Simulation results show that the proposed algorithm has higher estimation accuracy than the Two-Step Weighting Least Squares（TSWLS） algorithm and the Constrained Total Least Squares（CTLS） algorithm, which makes it possible to achieve the CRLB at moderate noise level before the threshold effect occurs. Moreover, from the Geometric Dilution Of Precision（GDOP） figure, it concludes target position, illuminator number and position are also important factors affecting the localization accuracy.

Key words: passive location, Time Difference Of Arrival（TDOA）, Frequency Difference Of Arrival（FDOA）, Maximum Likelihood（ML）, Newton’s method, Least Squares（LS）

摘要： 针对单站外辐射源条件下的目标定位问题，提出了一种基于最大似然的时差-频差联合定位算法。首先根据时差和频差的观测方程，构建目标位置和速度的最大似然估计模型。然后采用牛顿迭代算法对最大似然估计模型求解，得到目标位置和速度估计。最后，推导了算法的克拉美罗界和理论误差，并证明了二者相等。仿真结果表明，算法定位精度高于两步加权最小二乘算法和约束总体最小二乘算法，在测量误差较高时仍能达到克拉美罗界。通过对系统几何精度因子图的分析，确定目标及外辐射源数量和位置也是影响定位精度的重要因素。

关键词: 无源定位, 到达时差, 到达频差, 最大似然, 牛顿迭代, 最小二乘