Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Nandagiri, A.Subject: search.filters.subject.FMCW radar

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    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.
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    Cyber Attacking Active FMCW Radar Signal AoA Estimation Using Passive FMCW Radar for ADAS Applications
    (Institute of Electrical and Electronics Engineers Inc., 2024) Prakash, A.S.; Vandana, S.G.; Nandagiri, A.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.
    Millimeter-wave (mmWave) radars are a popular choice for Advanced Driver Assistant Systems (ADAS) that identify and track objects in the field of view. These mmWave radars (the primary radar on ego vehicles) are susceptible to interference signals from other mmWave radars (secondary radars on traffic participant vehicles) in the vicinity, which can result in false detection and tracking triggers. Knowing the interference signal's angle of arrival (AoA) is critical for locating the secondary radar source. This study discusses the experiments with AoA estimation of interference signals created by secondary radars when the primary radar is in a passive state. We performed a 3-dimensional Fast Fourier Transform (FFT) on the received I-Q data and used a range-angle heatmap image to determine the signal's spatial pattern. The 3D FFT (range FFT on time-domain ADC samples, velocity FFT on chirps, and angle FFT across antennas) calculates the AoA of the signals. In this experiment, the 77GHz IWR1642 primary radar is in passive mode, while the other 77GHz secondary radars (AWR1642 and AWR2944) are in active mode, providing an interference attack. We also tried with different ranges (2m, 3m, 5m, and 8m) and azimuths to determine the stealthiness of the attack. The AoA for passive radar is a good fit for identifying spurious sources/illuminators of opportunities, electronic counter-countermeasures (ECCM), source localization, knowledge-aided passive radar systems, and cognitive radar development. © 2024 IEEE.
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    Automotive Radar Signal Authentication via Correlation and Power Spectral Density
    (Institute of Electrical and Electronics Engineers Inc., 2024) Vishnu Prasad, P.; Vandana, G.S.; Nandagiri, A.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.
    Because of their comprehensive target detection, classification, and tracking capabilities, mm-wave radars are becoming increasingly popular in advanced driver assistance systems (ADAS). Unfortunately, these radars are vulnerable to attacks such as jamming and spoofing. This research presents a simple and low-cost radar signal authentication method that can be used in automotive radar receivers that lack external hardware or networking systems. The proposed technique of detecting correlation and power spectral density (PSD) classifies incoming signals as interference-free or not, and it may be swiftly implemented via a firmware update. As an example, the Texas Instruments (TI) IWR1642 frequency modulated continuous wave (FMCW) radar is tested in both non-jamming and jamming situations. The return signals are processed to get the correlation and power spectral density (PSD) observations and thereby classify the signals. © 2024 IEEE.