Tracking and measuring targets using a variety of sensors mounted on UAVs is an effective means to quickly and accurately locate the target. This paper proposes a fusion localization method based on ridge estimation, combining the advantages of rich scene information from sequential imagery with the high precision of laser ranging to enhance localization accuracy. Under limited conditions such as long distances, small intersection angles, and large inclination angles, the column vectors of the design matrix have serious multicollinearity when using the least squares estimation algorithm. The multicollinearity will lead to ill-conditioned problems, resulting in significant instability and low robustness. Ridge estimation is introduced to mitigate the serious multicollinearity under the condition of limited observation. Experimental results demonstrate that our method achieves higher localization accuracy compared to ground localization algorithms based on single information. Moreover, the introduction of ridge estimation effectively enhances the robustness, particularly under limited observation conditions.

翻译：利用无人机搭载多种传感器对目标进行跟踪与测量，是实现目标快速准确定位的有效手段。本文提出一种基于岭估计的融合定位方法，结合序列图像场景信息丰富与激光测距精度高的优势，以提升定位精度。在远距离、小交会角、大倾角等受限条件下，采用最小二乘估计算法时设计矩阵的列向量存在严重的多重共线性。多重共线性将导致病态问题，造成显著的不稳定性和低鲁棒性。引入岭估计以缓解有限观测条件下的严重多重共线性。实验结果表明，与基于单一信息的地面定位算法相比，本方法实现了更高的定位精度。此外，岭估计的引入有效增强了鲁棒性，尤其在有限观测条件下表现更为突出。