Millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radar is currently widely deployed in modern vehicles for advanced driver-assistance systems, and is regarded as one of the most promising sensing modalities for future autonomous vehicle systems. Compared with other major vehicular sensors, such as ca...
Yudai Suzuki, Xiao-Yan Wang, Masahiro Umehira et al.· IEEE Transactions on Aerospa...· 0 citations
An innovative, flexible, and efficient mobile calibration solution for radar systems across diverse scales and deployment environments and significantly enhances both the detection precision and the Time of Arrival (TOA) estimation accuracy of the calibration signals.
Fan-Xin Wu, Yue Zhang· International Conference on...· 0 citations
Low-altitude drones pose significant challenges for airspace safety and navigation, often requiring dedicated radar systems for monitoring. Commercial millimeter-wave cellular base stations provide dense urban infrastructure, large instantaneous bandwidths, directive antenna arrays, and software-defined processing chai...
D. Datta, S. Peters· IEEE Transactions on Radar S...· 0 citations
This study examines low-, mid-, and high-level fusion approaches and proposes a hybrid framework using GPS/IMU for localization and LiDAR-camera fusion for obstacle detection, designed for real-time performance and robustness against noise and sensor failures.
Suresh Babu Reddy· International Journal of Mod...· 0 citations
Automotive radar is a key sensing modality in advanced driver-assistance systems. In dense traffic environments, mutual radar interference can significantly degrade detection performance. This article presents a robust moving mean filtering (MMF)-median absolute deviation (MAD)-iterative subspace projection (ISP) algor...
Yu Chen, Ming-Lei Yang, Bai-Xiao Chen et al.· IEEE Transactions on Aerospa...· 0 citations
Results show that, for UAV RF emissions acquired within the effective coverage of the front-end receiver, MRSF improves robust UAV recognition while providing interpretable spectrum-structure descriptors beyond class labels, demonstrating strong robustness under severe low-SNR conditions.