传统无人机机载光电平台目标定位算法由于引入大量测角误差，导致目标定位精度不高。本文从非线性角度出发，提出了一种最小二乘和高斯牛顿的混合非线性算法。首先推导了基于激光测距值的高斯牛顿迭代非线性目标定位算法，然后利用线性最小二乘的粗解作为非线性牛顿迭代法的初值进行目标定位估计。该算法结合了最小二乘法简单易实现的优点和高斯牛顿法收敛速度快精度高的优点，并满足了高斯牛顿法对初值精度的要求。实测数据实验结果显示，该方法对实测固定目标定位结果的经度误差小于(1.37×10-5)°，纬度误差小于(6.31×10-5)°，高度误差小于1.78 m，并且单次定位处理时间在6 ms 以内，符合实时定位的要求。

The target positioning algorithm of the traditional unmanned aerial vehicle (UAV) airborne optoelectronic platform introduces a large number of angle measurement errors, resulting in low target positioning accuracy. In this paper, a hybrid nonlinear algorithm of least squares and Gauss-Newton is proposed. Firstly, the Gauss-Newton iterative nonlinear target localization algorithm based on laser ranging value is derived. Then the rough solution of linear least square is used as the initial value of the nonlinear Newton iteration method for target location estimation. The algorithm combines the advantages of the simple and easy implementation of the least squares method and the high convergence accuracy of the Gauss-Newton method, and satisfies the requirements of the Gauss-Newton method for the initial value accuracy. Experimental results of measured data show that the longitude error of fixed target positioning results of this method is less than 1.37×10-5 degrees, the latitude error is less than 6.31×10-5 degrees, and the height error is less than 1.78 meters. And the processing time of each positioning is within 6 ms, which meets the requirements of real-time positioning.