Faculty Publications
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Publications by NITK Faculty
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Item An Experimental Evaluation of MIMO-SAR Imaging with FMCW Radar(Institute of Electrical and Electronics Engineers Inc., 2022) Sriharsha Nag, T.S.; Vandana, G.S.; Pardhasaradhi, B.; Srihari, P.Synthetic aperture radar (SAR) imaging is a widely used technique to generate two-dimensional and three-dimensional imaging based on the principle of moving sensors. This paper presents an experimental investigation of SAR imaging using an IWR6843ISK frequency modulated continuous waveform(FMCW) radar sensor on a moving platform and providing the stationary targets. In this experiment, the moving platform is used to recreate images with a higher spatial resolution based on a two-dimensional fast Fourier transform (2D-FFT) algorithm. Three different configurations are explored, namely: SAR, single input multiple output SAR (SIMO-SAR), and multiple input and multiple outputs SAR for imaging multiple targets within the vicinity. The real-time data is collected by moving the radar from 1-21m and observing two different stationary targets. The received data is processed using the 2D-FFT algorithm and obtaining the imaging for various configurations. The experimental results reveal that target detection is accomplished for all three configurations and comparable with ground truth. The MIMO-SAR outperforms and is a suitable candidate for automotive SAR imaging. © 2022 IEEE.Item SAR Imaging with Automotive Radar: Range Migration Algorithm, Experiment, and Future Directions in Automotive Vehicle(Institute of Electrical and Electronics Engineers Inc., 2022) Sriharsha Nag, T.S.; Vandana, G.S.; Pardhasaradhi, B.; Srihari, P.Synthetic Aperture Radar (SAR) imaging with air-borne radar has many applications in remote sensing, surface mapping, and civil infrastructure monitoring. In contrast, the Millimeter wave (mmWave) radars are confined to the automotive industry to fetch information like range, azimuth, and elevation. In this paper, we proposed configuring the mm-wave radar in SAR mode to utilize the availability of multiple transmitters and receivers. The frequency modulated continuous wave (FMCW) radar is ported onto a moving platform, and a range migration algorithm (RMA) is performed to perform SAR imaging. The real-time experiment was conducted using IWR6843ISK radar and explored different configurations like SAR, single-input multiple-outputs SAR (SIMO-SAR), and multiple-input multiple-outputs SAR (MIMO-SAR). In addition to that, the platform movement is also considered by varying the speed from slow to high. The acquired I-Q data is processed using a range migration algorithm (RMA) by following the processing steps like a two-dimensional fast fourier transform (2D-FFT), Stolt interpolation, azimuth compression, and inverse FFT (IFFT). The experimental results reveal that the target detection is accomplished for all the cases. The best results can be obtained for MIMO configuration with a high-speed moving platform. These results can be highly applicable for the automotive industry to perform SAR imaging on SRR radars which are being deployed on the side of the vehicle to monitor the guard rails, crossing vehicles, and VRU. © 2022 IEEE.Item FMCW Radar-Based Detection and Tracking of Drones Using DBSCAN Clustering and Extended Kalman Filter for Anti-Drone Defense Systems(Institute of Electrical and Electronics Engineers Inc., 2024) Srihari, P.; Vandana, G.S.; Kumar, U.; Nandagiri, A.; Pardhasaradhi, B.P.; Cenkarmaddi, L.R.This paper aims to develop a radar-based detection and tracking system to mitigate the threats posed by drones, particularly those carrying malicious payloads. Due to the limitations of cameras in adverse weather and the high costs of LiDAR systems, radar technology is employed as a cost-effective alternative. The system utilizes 3D FFT followed by CA-CFAR for drone range-azimuth detections. The range-azimuth detections are clustered using DBSCAN. We simplified the extended target tracking problem into point target tracking based on the drone's size, with the dBSCAN cluster center acting as the measurement for the tracker. The tracking algorithm combines an Extended Kalman Filter (EKF) with Global Nearest Neighbor (GNN) data association. Experiments were conducted using a 77 GHz AWR1642 radar sensor to track a micro drone of hexacopter type within a range of 10m to 100m. The results demonstrated effective tracking capabilities with radar sensors successfully generating tracks. This study highlights the viability of radar-based systems for anti-drone applications, offering a practical solution for enhancing infrastructure security against potential drone threats. © 2024 IEEE.