Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
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Item DoA Estimation for Micro and Nano UAV Targets using AWR2243 Cascaded Imaging Radar(Institute of Electrical and Electronics Engineers Inc., 2022) Kavya, T.S.; Vandana, G.S.; Srihari, P.; Leelarani, V.; Pardhasaradhi, B.Frequency-modulated continuous wave (FMCW) radars accurately estimate the target's position and velocity, but the angular resolution is inadequate. The low radar cross section (RCS) unmanned aerial vehicles (UAVs) like micro UAVs (0.01m2) and nano UAVs (0.001m2) pose a significant threat to sensitive military and civilian installations. The DoA of the low RCS targets helps in making stealthy countermeasures. In this paper, the DoA of nano and micro UAVs is experimented using Texas instruments AWR2243 cascaded imaging radar in conjunction with a digital signal processing evaluation module (DSP EVM). The data is received from all the available 16 receivers, then the subspace method of multiple signal classification (MUSIC) algorithm is applied to estimate the DoA of the low RCS UAvs in hovering mode. The ground truth of the UAVs is fixed at 10m range and 12 ° azimuth from the center of the radar using engineering protractor. The average estimated DoA for nano and micro UAV s is 12.80° and 11.43°, respectively, for the ground truth DoA. The AWR2243 cascaded imaging radar provides superior performance and suitable candidate for the DoA estimation for micro and nano UAVs compared to existing AWR1642, IWR1642, and IWR6843 radars. © 2022 IEEE.Item A Mutual Interference Mitigation Algorithm for Dense On-Road Automotive Radars Scenario(Institute of Electrical and Electronics Engineers Inc., 2023) Kumuda, D.K.; Srihari, P.; Seshagiri, D.; Raju, M.K.; Pardhasaradhi, B.The mmWave frequency modulated continuous wave (FMCW) radars are widely adopted in the automotive industry because they work in all weather conditions. Due to the increased on-road density of mmWave radars, the primary radar mounted on the ego vehicle faces mutual interference. The traditional detection scheme employs a one-dimension fast Fourier transform (FFT) followed by a constant false alarm rate (CFAR) on the intermediate frequency (IF) signal to get the target detections. In the case of mutual interference, the IF signals behavior is abnormal and leads to miss-detection and false detections within the traditional framework. We propose a weighted beat signal normalization algorithm on the intermediate frequency (IF) signal followed by a traditional detection scheme as a mutual interference mitigation mechanism. This methodology implementation is easy since it does not disturb any processing modules like the mixer, LPF, FFT, and CFAR blocks in the architecture. The results demonstrate that, the SINR increases by the proposed method thereby minimizing the probability of missing the target detection. © 2023 IEEE.Item Clipping and Hampel Filtering Algorithm to Mitigate Mutual Interference for Automotive Radars(Institute of Electrical and Electronics Engineers Inc., 2023) Kumuda, D.K.; Srihari, P.; Seshagiri, D.; Rajesh Kumar, P.R.; Pardhasaradhi, B.The frequency modulated continuous wave (FMCW) radars are widely adopted in the automotive industry to serve the emergency brake assistant (EBA) and automatic cruise control (ACC) functions. Nowadays, autonomous vehicles on-road density increasing and creating a problem of mutual interference. Due to this mutual interference, the structural properties like periodicity and amplitude of intermediate frequency (IF) signal varies and creates undeserved target detections. It is observed that, applying a one-dimension fast Fourier transform (FFT) on the intermediate frequency (IF) results in increased false alarms and missed detections. This paper process a clipping followed by a Hampel filtering on the IF signal to mitigate this mutual interference. Initially, the clipping framework chopoff the unwanted and abrupt amplitude information from the IF signal. The threshold of the clipper circuit is taken as the standard deviation of the acquired IF signal. Second, the Hampel filter was employed to identify the outliers in time-series data and replace them with more representative values. The Hampel is configured in a sliding window to calculate the median by providing the standard deviation of the acquired IF signal. This methodology implementation is easy and can be placed as an intermediate block between IF and FFT. The results demonstrate that the proposed methodology facilitating the good detection rate by decreasing the false alarm rate and missed detections. © 2023 IEEE.Item FPGA Implementation of Moving Target Indicator Filter for FMCW Radar Data(Institute of Electrical and Electronics Engineers Inc., 2023) Sreelekha, N.; Vandana, G.S.; Srihari, P.; Leelarani, V.; Raju, M.K.; Sreenivasula Reddy, T.S.This study examines several digital finite impulse response (FIR) filter approaches for moving target indication (MTI) employing short-range FMCW radar sensors. The FIR filters can filter out low doppler shift responses from undesirable stationary targets. A 77 GHz AWR1642 FMCW radar sensor and a DCA1000 data capture card are used to build a hardware configuration. A single data frame (samples × chirps) containing a target approaching the radar is been considered. The recorded radar is preserved in a 256x64 matrix of in-phase and quadrature-phase components, which is then processed using various digital filters. The radar provides insights into doppler characteristics for the observations. This study proposes designing and implementing a two-tap and a three-tap FIR filter-based MTI processing module to reduce static targets. The VLSI DSP pipelining approach is deployed to improve filter performance regarding critical path delay and throughput. © 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 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.Item 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.