稳健收敛的时差频差定位技术

为实现对目标位置和速度的精确定位，该文提出一种基于正则化理论的时差频差定位技术。该算法首先利用最大似然方法确定目标函数，然后通过传统牛顿法对目标位置和速度进行迭代求解。众所周知传统牛顿法对初始值要求较高，较差初始值会导致Hess矩阵趋于病态，从而致使迭代发散，该文引入正则化理论修正Hess矩阵，使其更加合理，保证算法稳健收敛。实验结果表明：相对于传统牛顿法，该文算法在初始值的选取上具有稳健性，对误差选取较大的初始值，仍能够保证算法的收敛性；相对于现有闭合式定位方法，该文算法在噪声较大时具有较好的定位精度，定位精度接近于Cramer-Rao界，具有广泛的实用价值。

A Robustly Convergent Algorithm for Source Localization Using Time Difference of Arrival and Frequency Difference of Arrival

To pursue accurate source location and velocity, this paper proposes a method based on the Regularization theory to solve the source localization problem utilizing Time-Difference-Of-Arrival (TDOA) and Frequency-Difference-Of-Arrival (FDOA). The proposed algorithm determines the objective function using the maximum likelihood estimator, and then uses classical Newton method to estimate the source position and velocity in an iterative way. It is known that the Newton method requires a good initial value, and a bad initial value can cause an ill-posed Hess matrix which leads to the iteration divergence. This paper introduces the Regularization theory to modify the Hess matrix to make it more proper, which ensures the iteration convergence. The experiment results show that compared with the classical Newton method, the proposed algorithm is robust to the initial value, and is still able to ensure its convergence even with an inaccurate initial value of large error. Compared with some other closed-form source location methods, the proposed algorithm has better location accuracy in large noise levels which can achieve the Cramer-Rao bound. The proposed algorithm can be widely applied in practice.