Faculty Publications
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Item FMCW Radar-Based UAV Detection and Tracking Using Transfer Learning(Institute of Electrical and Electronics Engineers Inc., 2024) Sreekumar, S.; Shashank, S.K.; Srihari, P.; Nandagiri, A.; Vandana, G.S.; Pardhasaradhi, B.P.; Cenkarmaddi, L.R.This research investigation offers a novel method for monitoring and detecting unmanned aerial vehicles (UAVs) by combining transfer learning neural networks with Frequency Modulated Continuous Wave (FMCW) radar. The system utilizes a 60 GHz Texas Instruments IWR6843ISK radar with a DCA1000 board to capture raw radar signals, which are subsequently processed to generate range-angle heat maps. Ground truth data for UAV positioning is meticulously obtained using a dual GPS setup, where one GPS is stationed at the radar and the other is mounted on the UAV. The processed range-angle heat maps serve as the input for various transfer learning models, including DenseNet, InceptionV3, MobileNet, ResNet, and VGG, which are employed to compute the range data and angle data of the UAV. The results emphasize the potential of transfer learning in improving radar signal processing by demonstrating the effectiveness of these models in attaining accurate UAV detection and tracking. This approach is pivotal for applications requiring precise UAV monitoring, offering a robust solution for scenarios where traditional radar systems may fall short. The study underscores the advantages of leveraging transfer learning for improved radar-based UAV detection and sets the stage for future advancements in autonomous aerial monitoring and surveillance systems. © 2024 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 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.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 A High-Gain Half-Bow-Tie Antenna with Tapered Slots for Foliage Penetration Radar Application(Institute of Electrical and Electronics Engineers Inc., 2024) Shetty, A.K.; Goud, M.G.; Kandasamy, K.; Srihari, P.; Pardhasaradhi, B.P.; Cenkarmaddi, L.R.In this study, a compact, low-profile, high-gain half-bow-tie antenna with tapered slots is designed specifically for foliage penetration radar (FOPEN) applications. The proposed antenna design begins by vertically bisecting a conventional bow-tie antenna, followed by the addition of two vertical metal strips to enhance the operational bandwidth. To achieve effective impedance matching across the desired frequency range, two horizontal conductive strips and a central balun circuit are integrated into the design. To further optimize the antenna for integration into aerial platforms, the gain and radiation pattern are refined by incorporating two additional metal strips strategically placed on the right side of the bow-tie structure. These design modifications result in a compact antenna with dimensions of 0.43λx 0.4λ x 0.005λ where λ is the wavelength corresponding to the lower frequency of operation. The optimized antenna achieves a realized gain of 6.5 dBi and an impedance bandwidth of 110 MHz, making it highly suitable for FOPEN applications. The enhanced gain, achieved with a single dielectric layer, demonstrates the potential of this design for efficient foliage penetration radar systems. © 2024 IEEE.Item Improved GNSS Anti Spoofing by Integrating Attribute Information into Measurement to Measurement Association Framework(Institute of Electrical and Electronics Engineers Inc., 2024) Pardhasaradhi, B.P.; Srihari, P.; Cenkarmaddi, L.R.Global navigation satellite system (GNSS) is famous for providing position, navigation, and time (PNT) information at low cost anywhere globally. However, this GNSS is highly misled in the name of spoofing because of its readily available blueprints. This paper proposes to integrate the attribute information within the measurement to measurement association (M2MA) mathematical framework to improve the capabilities of GNSS anti-spoofing. The satellite location, received power, clock offset, and correlation are the attributes associated with the acquired pseudo ranges. This formulation assumes that authentic and spoofed GNSS signals are locked into the multi-correlator GNSS receiver and capable of estimating each signal's attributes. This process of using the attribute information facilitates screening authentic pseudoranges from spoofed pseudoranges. The results are evaluated at an epoch in a Monte Carlo sense with hit rate, dilution of precision (DOP), and mean square error (MSE) as metrics. The results reveal that the attribute information in M2MA drastically improves the anti-spoofing capabilities. © 2024 IEEE.Item Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System(Institute of Electrical and Electronics Engineers Inc., 2024) Mahipathi, A.C.; Pardhasaradhi, B.P.; Gunnery, S.; Srihari, P.; D'Souza, J.; Jena, P.The widespread usage of the Radio Frequency (RF) spectrum for wireless and mobile communication systems generated a significant spectrum scarcity. The Joint Radar-Communication System (JRCS) provides a framework to simultaneously utilize the allocated radar spectrum for sensing and communication purposes. Generally, a Successive Interference Cancellation (SIC) based receiver is applied to mitigate mutual interference in the JRCS configuration. However, this SIC receiver model introduces a communication residual component. In response to this issue, the article presents a novel measurement model based on communication residual components for various radar waveforms. The radar system's performance within the JRCS framework is then evaluated using the Fisher Information Matrix (FIM). The radar waveforms considered in this investigation are rectangular pulse, triangular pulse, Gaussian pulse, Linear Frequency Modulated (LFM) pulse, LFM-Gaussian pulse, and Non-Linear Frequency Modulated (NLFM) pulse. After that, the Kalman filter is deployed to estimate the target kinematics (range and range rate) of a single linearly moving target for different waveforms. Additionally, range and range rate estimation errors are quantified using the Root Mean Square Error (RMSE) metric. Furthermore, the Posterior Cramer-Rao Lower Bound (PCRLB) is derived to validate the estimation accuracy of various waveforms. The simulation results show that the range and range rate estimation errors are within the PCRLB limit at all time instants for all the designated waveforms. The results further reveal that the NLFM pulse waveform provides improved range and range rate error performance compared to all other waveforms. © 2020 IEEE.