Tag: Estimation

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The article titled, “Multi-target Angle of Arrival Estimation using Rotating mmWave FMCW Radar and Yolov3,” has been accepted for publication in the IEEE Sensors Journal (2022).

Wilson A N, Abhinav Kumar, Ajit Jha, and Linga Reddy Cenkeramaddi, “Multi-target Angle of Arrival Estimation using Rotating mmWave FMCW Radar and Yolov3,” has been accepted for publication in the IEEE Sensors Journal (2022).

Keywords: Estimation, Radar, Millimeter wave communication, Sensors, Receiving antennas, Chirp, Radar detection

Abstract: It is still challenging to accurately localize unmanned aerial vehicles (UAVs) from a ground control station (GCS) using various sensors. The mmWave frequency-modulated continuous wave (FMCW) radars offer excellent performance for target detection and localization in harsh environments and low lighting conditions. However, the estimated angle of arrival (AoA) of targets in the captured scene is quite poor. This article focuses on improving AoA estimation by combining the cutting-edge machine learning (ML) algorithms with a mechanical radar rotor setup. An mmWave FMCW radar system is mounted on a programmable rotor to capture range–angle maps of targets at various locations. The range–angle images are then labeled and trained further with the Yolov3 algorithm. Subsequent testing reveals that for detected target objects, the centroid of the bounding boxes from the detected objects provides accurate AoA estimation with very low root mean square error (RMSE). The results show that the proposed approach outperforms traditional methods in terms of performance and estimation accuracy.

More details:DOI: 10.1109/JSEN.2022.3231790

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The article, “A Novel Angle Estimation for mmWave FMCW radars using Machine Learning,” has been accepted for publication in the IEEE Sensors Journal.

L. R. Cenkeramaddi, P. K. Rai, A. Dayal, J. Bhatia, A. Pandya, J. Soumya, A. Kumar, & A. Jha., “A Novel Angle Estimation for mmWave FMCW radars using Machine Learning,” IEEE Sensors Journal 2021 (in press).

Keywords: Radar, Radar antennas, Estimation, Azimuth, Chirp, Sensors, Machine learning

Abstract: In this article, we present a novel machine learning based angle estimation and field of view (FoV) enhancement techniques for mmWave FMCW radars operating in the frequency range of 77 – 81 GHz. Field of view is enhanced in both azimuth and elevation. The Elevation FoV enhancement is achieved by keeping the orientation of antenna elements in elevation. In this orientation, radar focuses the beam in vertical direction there by enhancing the elevation FoV. An Azimuth FoV enhancement is achieved by mechanically rotating the radar horizontally, which has antenna elements in the elevation. With the proposed angle estimation technique for such rotating radars, root mean square error (RMSE) of 2.56 degrees is achieved. These proposed techniques will be highly useful for several applications in cost-effective and reliable autonomous systems such as ground station traffic monitoring and control systems for both on ground and aerial vehicles.

More details:DOI: 10.1109/JSEN.2021.3058268