Faculty Publications
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Publications by NITK Faculty
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Item Bistatic Inverse Synthetic Aperture Radar Imaging of Automotive Targets at Millimeter Frequencies(Institute of Electrical and Electronics Engineers Inc., 2023) Kumar, S.A.; Ram, S.S.; Srihari, P.High-resolution inverse synthetic aperture radar (ISAR) images of road vehicles at millimeter wave frequencies provide useful information regarding the size, shape, number of wheels, and nature of the target trajectory. They are thus effective features for target classification. However, in some geometrically adverse scenarios, it may not be possible to generate ISAR images in monostatic radar conditions. Here, bistatic ISAR images generated from well separated transmitting and receiving antennas may overcome the limitations of the monostatic geometry or supplement the information obtained from monostatic radar. In this paper, we present the bistatic ISAR radar images of a car from multiple orientations and aspects. The radar data are gathered from simulations using computer-animated design models of the car along a turning trajectory. Electromagnetic modeling of the radar scattering combines a point scatterer model of the extended automotive target along with shadowing based on ray tracing. © 2023 IEEE.Item Enhanced Intruder Detection Using mmWave Radar: A Spatiotemporal Clustering Approach for Robust Human Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Shashank, S.K.; Sreekumar, S.; Manjith Srijan, M.; Srihari, P.; Vandana, G.S.; Pardhasaradhi, B.P.; Cenkarmaddi, L.R.Intruder detection using mmWave radar presents significant challenges due to the variability in the number of detections across spatial and temporal domains. This variability complicates the clustering process, particularly when detections from different body parts, such as the head and gait, are spatially distant, leading to the potential fragmentation of clusters. To address these challenges, we propose an enhanced clustering methodology that integrates both spatial and temporal information. The approach modifies the traditional DBSCAN algorithm by introducing a delayed window accumulation technique. This technique allows the radar system to accumulate detections over a specified duration, ensuring that an adequate number of detections are gathered before initiating clustering. Additionally, our method considers the physical dimensions of the human body to merge clusters that may be incorrectly separated due to the spatial distribution of detections. We further refine this approach by implementing a modified agglomerative clustering algorithm that leverages both spatial and temporal data to enhance cluster stability. The proposed methods are evaluated against the standard DBSCAN algorithm, demonstrating superior performance in accurately identifying human intruders, even under challenging detection scenarios. Our findings suggest that incorporating a delayed window accumulation strategy and considering spatiotemporal data are critical for robust intruder detection using mmWave radar. © 2024 IEEE.Item Real-Time UAV Altitude Estimation and Data Transmission via mmWave Radar and Edge Computing(Institute of Electrical and Electronics Engineers Inc., 2024) Vandana, G.S.; Srihari, P.; Kumar, U.; Nandagiri, A.; Pardhasaradhi, B.P.; Cenkarmaddi, L.R.This paper presents a novel approach for UAV altitude estimation and data transmission using a 60 GHz IWR6843 mmWave radar mounted on a micro-drone, coupled with a Raspberry Pi edge device. The radar, configured in a long-range mode, leverages its high accuracy in altitude measurement, surpassing the performance of traditional UAV altimeters. The radar altimeter data is processed on the Raspberry Pi and wirelessly transmitted to the cloud, from which it can be accessed by a ground station for real-time monitoring and analysis. To validate the accuracy of the radar-based altitude measurements, GPS data is simultaneously recorded on the UAV, serving as a ground truth reference. Experimental results demonstrate that the radar-based measurements closely match the GPS-derived altitudes, showcasing the effectiveness of the proposed system. This approach not only improves altitude estimation accuracy but also enhances the reliability of UAV operations in various environments. Potential applications of this system include precision agriculture, disaster management, and search and rescue operations, where accurate altitude data is critical for mission success. The integration of mmWave radar with edge computing and cloud-based data management opens new avenues for real-time UAV monitoring and autonomous navigation. © 2024 IEEE.Item Adult and Child Classification using Automotive Radar for In-cabin Monitoring(Institute of Electrical and Electronics Engineers Inc., 2024) Sreekumar, S.; Shashank, S.K.; Srihari, P.; Vandana, G.S.; Pardhasaradhi, B.; Cenkarmaddi, L.R.The awareness and decision-making about the unattended child or pet inside a car is one of the emerging features in autonomous vehicles as a precaution to prevent hot car death. The automotive radars can provide the Doppler and spatial information about in-cabin passengers. This paper proposes to extract the range-Doppler images from the IQ radar data and process them using CNNs to classify the passenger as an adult or child. The IWR1642 radar module is used to collect the passenger details in both space and time within the car. A novel CNN architecture is proposed by trading off the accuracy and lightweight characteristics of the network. The proposed architecture provides 97.74± 0.34 accuracy (with 18.32 MB size) compared to the denseNet201 of 99.13± 0.3 (with 71.3 MB size) accuracy. The proposed architecture is compared against the existing pre-trained models like InceptionNet, MobileNet, EfficientNet, NASNet, VGGNet, DenseNet, ResNet, and Xception regarding accuracy and size. © 2024 IEEE.Item Real-time Radar Imaging with Time Domain Correlation and Doppler Beam Sharpening(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.A.; Achala, G.; Vandana, G.S.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.Imaging with radar serves numerous purposes across remote sensing, monitoring civil infrastructure, detecting passing vehicles, and recognizing vulnerable road users (VRUs) within Advanced Driver Assistance Systems (ADAS). In most ADAS applications, one among a variety of radars, the millimeter wave (mmWave) radars, are limited to acquiring range, azimuth, elevation, and Doppler information. Configuration of the mm-wave radar in imaging form by fully utilizing built-in sending and receiving models is proposed in the work presented in this paper. The mm-wave radar is placed on a mobile platform, and the time domain correlation (TDC) is applied, followed by Doppler beam sharpening (DBS), to obtain radar imaging. The proposed algorithm was demonstrated with the help of IWR1642 radar, and real-time experiments were conducted with targets like cars, bicycles, and bikes. The mm-wave radar equipment and platform were moved with an approximate velocity and acquired I-Q channel data, further processed with the TDC-DBS algorithm. The experimental findings demonstrate successful target detection across scenarios considered in our work. Notably, the MIMO configuration on a fast-moving platform, along with the TDC-DBS algorithm, yielded superior results compared to the TDC algorithm. This algorithm stands out as a promising choice for automotive industry applications, such as imaging guardrails, detecting passing vehicles, and identifying vulnerable road users using side-mounted radar configurations. © 2024 IEEE.