基于不平衡最优传输的传感器阵列与相机融合用于3D源定位
Sensor array and camera fusion via unbalanced optimal transport for 3D source localization
AI总结：
本文提出结合传感器阵列与相机观测的3D源定位方法，通过引入不平衡最优传输正则化项实现传感器响应与视觉先验的软对齐，提升定位精度。
摘要：
我们通过结合传感器阵列测量与相机观测，解决三维多源定位问题。我们提出一种融合框架，扩展了协方差矩阵拟合方法，并引入不平衡最优传输正则化项，以软对齐传感器阵列响应与视觉先验，同时允许质量分配的灵活性。为解决由此产生的大规模问题，我们采用贪心坐标下降算法高效更新传输计划。其计算效率使得实际全3D定位成为可能。所提框架模块化且不依赖标注数据或训练，不同于基于深度学习的融合方法。尽管在此处验证于声学阵列，该方法适用于任意传感器阵列。实验表明，所提方法相比仅传感器基线提高了定位精度。
英文摘要：
We address the problem of localizing multiple sources in 3D by combining sensor array measurements with camera observations. We propose a fusion framework extending the covariance matrix fitting method with an unbalanced optimal transport regularization term that softly aligns sensor array responses with visual priors while allowing flexibility in mass allocation. To solve the resulting largescale problem, we adopt a greedy coordinate descent algorithm that efficiently updates the transport plan. Its computational efficiency makes full 3D localization feasible in practice. The proposed framework is modular and does not rely on labeled data or training, in contrast with deep learning-based fusion approaches. Although validated here on acoustic arrays, the method is general to arbitrary sensor arrays. Experiments on real data show that the proposed approach improves localization accuracy compared to sensor-only baselines.
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