基于随机森林的无源时差定位误差估计与误差修正算法
2025,33(1):269-275
摘要：针对无源时差定位算法在时差估计误差未知条件下，无法通过计算几何稀释因子准确估计定位误差的问题，提出基于随机森林的无源时差定位误差估计与误差修正算法，通过学习信号特征参数和定位信息与定位误差之间的映射关系，准确估计定位误差并对定位结果进行修正，实现高精度定位；通过匹配时差定位结果与参考源信息制作了测试数据集，并使用该数据集验证了所提算法的有效性；对各特征参数的重要性进行了定量分析，并验证了随机森林模型在时间上的泛化能力。实验结果表明，所提算法实现了在时差估计误差未知条件下对时差定位误差的准确估计，提高无源时差定位的精度，具有较高的工程应用价值。
关键词：无源定位;时差定位;随机森林;误差估计;误差修正
Estimation and Correction Algorithm for Passive TDOA Localization based on Random Forest
Abstract：In order to address the problem of the inability of passive time difference of arrival (TDOA) positioning algorithm to accurately estimate positioning error by calculating geometric dilution of precision (GDOP) under the condition of unknown TDOA estimation error, this paper proposes a TDOA positioning error estimation and correction algorithm based on random forest. By learning the mapping relationship between signal feature parameters, positioning information, and positioning error, it accurately estimates the positioning error and corrects the positioning result to achieve high-precision positioning. A test dataset was created by matching the TDOA positioning result with reference source information, and this dataset was used to verify the effectiveness of the proposed algorithm. The importance of each feature parameter was quantitatively analyzed, and the temporal generalization ability of the random forest model was validated. Experimental results show that the proposed algorithm achieves accurate estimation of TDOA positioning error under the condition of unknown TDOA estimation error, improves the accuracy of passive TDOA positioning, and has high engineering application value.
Key words：Passive localization; TDOA;Random Forest; Error Estimation;Error Correction
收稿日期：2024-08-28
基金项目：国家自然科学基金(U20B2071)
