A Novel Angle Estimation for mmWave FMCW Radars Using Machine Learning

Cenkeramaddi, Linga Reddy and Rai, Prabhat Kumar and Dayal, Aveen and Bhatia, Jyoti and Pandya, Aarav and Soumya, J. and Kumar, Abhinav and Jha, Ajit (2021) A Novel Angle Estimation for mmWave FMCW Radars Using Machine Learning. IEEE Sensors Journal, 21 (8). pp. 9833-9843. ISSN 1530-437X

Full text not available from this repository. (Request a copy)


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.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Item Type: Article
Uncontrolled Keywords: Angle estimation; Antenna element; Autonomous systems; Frequency ranges; Ground stations; Root mean square errors; Traffic monitoring; Vertical direction;Autonomous systems; azimuth angle; elevation angle; enhanced field of view; FMCW radar; machine learning; mmWave radar; root mean square error
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 07 Aug 2021 09:29
Last Modified: 07 Aug 2021 09:29
URI: http://raiith.iith.ac.in/id/eprint/8725
Publisher URL: http://doi.org/10.1109/JSEN.2021.3058268
OA policy: https://v2.sherpa.ac.uk/id/publication/3570
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 8725 Statistics for this ePrint Item