Dual radar vision: A feature fusion approach for advanced object detection in IoT radar networks

IF 4.9

Machine learning with applications Pub Date : 2025-07-16 DOI:10.1016/j.mlwa.2025.100703

Philipp Reitz, Tobias Veihelmann, Norman Franchi, Maximilian Lübke

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引用次数: 0

Abstract

60 GHz radar technology is one of the most promising movement detector solutions for Internet of Things (IoT) applications. However, challenges remain in accurately classifying different objects and detecting small objects in a multi-target scenario. This work investigates whether sensor fusion between multiple radars can enhance object detection and classification performance. A one-stage detection architecture, designed based on the features of the latest YOLO generations, is used to perform fusion based on range-Doppler (RD) maps of two non-coherent spatially separated radars. A complete physical 3D propagation simulation using ray tracing evaluates the fusion methods. This approach enables precise ground truth, as all unprocessed signal components are known, and guarantees a consistent, error-free reference. Results demonstrate that dynamic, attention-based fusion significantly improves detection and classification compared to static fusion in homogeneous and heterogeneous radar setups.

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双雷达视觉：物联网雷达网络中高级目标检测的特征融合方法

60 GHz雷达技术是物联网（IoT）应用中最有前途的运动探测器解决方案之一。然而，在多目标场景下，如何准确分类不同的目标和检测小目标仍然是一个挑战。本文研究了多雷达之间的传感器融合是否能够提高目标检测和分类性能。基于最新一代YOLO雷达的特点，设计了一种单级探测架构，用于对两个非相干空间分离雷达的距离-多普勒（RD）图进行融合。一个完整的物理三维传播模拟使用光线追踪评估融合方法。由于所有未处理的信号成分都是已知的，因此这种方法可以实现精确的接地真值，并保证一致，无错误的参考。结果表明，在均匀和非均匀雷达设置中，与静态融合相比，动态、基于注意力的融合显著提高了检测和分类能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Machine learning with applications Management Science and Operations Research, Artificial Intelligence, Computer Science Applications

自引率

0.00%

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审稿时长

98 days