Combining multiple devices for localization has important applications in the military field. This paper exploits the land-based short-wave platforms and satellites for fusion localization. The ionospheric reflection height error and satellite position errors have a great impact on the short-wave localization and satellite localization accuracy, respectively. In this paper, an iterative constrained weighted least squares (ICWLS) algorithm is proposed for these two kinds of errors. The algorithm converts the nonconvex equation constraints to linear constraints using the results of the previous iteration, thus ensuring convergence to the globally optimal solution. Simulation results show that the localization accuracy of the algorithm can reach the corresponding Constrained Cramér–Rao Lower Bound (CCRLB). Finally, the localization results of the two methods are fused using Kalman filtering. Simulations show that the fused localization accuracy is improved compared to the single-means localization.

中文翻译：

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使用约束加权最小二乘法融合陆基和星基定位


组合多个设备进行定位在军事领域具有重要的应用。本文利用陆基短波平台和卫星进行融合定位。电离层反射高度误差和卫星位置误差分别对短波定位和卫星定位精度影响较大。本文针对这两类误差提出了迭代约束加权最小二乘（ICWLS）算法。该算法利用前一次迭代的结果将非凸方程约束转换为线性约束，从而确保收敛到全局最优解。仿真结果表明，该算法的定位精度可以达到相应的Constrained Cramér–Rao Lower Bound（CCRLB）。最后，使用卡尔曼滤波融合两种方法的定位结果。仿真表明，与单均值定位相比，融合定位精度有所提高。