Browsing by Author "Cenkarmaddi, L.R."

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A GNSS Position Spoofing Mitigation Algorithm using Sparse Estimation
(Institute of Electrical and Electronics Engineers Inc., 2022) Pardhasaradhi, B.; Gunnery, G.; Mahipathi, A.C.; Srihari, P.; Cenkarmaddi, L.R.
The Global Navigation Satellite Systems (GNSS) are widespread for providing Position, Velocity, and Time (PVT) information across the globe. The GNSS usually employs the Extended Kalman Filter (EKF) framework to estimate the PVT information of the receiver. The GNSS receivers PVT information is falsified by using a mimic GNSS signals is called a spoofing attack. This paper focuses mainly to combat the spoofing attack using sparse estimation theory. A generalized mathematical model is proposed for authentic and spoofed pseudoranges at the GNSS receiver. After that, a generalized pseudorange measurement model is presented by combining the authentic and spoofed pseudorange measurements. It is assumed that, only a part of satellite signals are spoofed. Further, the GNSS receiver's state is estimated by mitigating the spoofed pseudoranges and it is formulated as a Least Absolute Shrinkage and Selection Operator (LASSO) optimization problem. The simulated results, compares the proposed LASSO based EKF algorithm with traditional EKF framework. It is observed that, the proposed algorithm suppresses the spoofing effect. Moreover, the Position Root Mean Square Error (PRMSE) of the proposed algorithm decreases by increasing the number of spoofed measurements. Â© 2022 IEEE.
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 0.43Î»x 0.4Î» x 0.005Î» where Î» 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. Â© 2024 IEEE.
A Survey on Waveform Design for Radar-Communication Convergence
(Institute of Electrical and Electronics Engineers Inc., 2024) Chakravarthi Mahipathi, A.; Pardhasaradhi, B.; Lingadevaru, P.; Srihari, P.; D'Souza, J.; Cenkarmaddi, L.R.
To provide service to an abundant number of communication users and to avoid the spectrum scarcity problem, many researchers are fascinated to work towards the convergence of radar sensing and communication systems. In addition, future intelligent systems like autonomous vehicles, Vehicle-to-everything (V2X), Unmanned Aerial Vehicles (UAV), and all smart systems are going to implement both radar and communication systems on the same platform, which motivates the researchers to focus on the development of Joint Radar-Communication Systems (JRCS). Cooperative Radar-Communication System (CRCS) and Dual Functional Radar Communication (DFRC) systems provide an opportunity for communication users to utilize radar resources without disturbing radar operation. Waveform design is essential in the development of new models and designs related to joint radar-sensing and communication systems. A cooperative radar communication system uses separate waveforms for radar and communication systems. The DFRC system uses the same waveform for radar and communication operations. So to model both joint radar communication systems one should have a clear idea regarding waveform design and its approaches. Therefore, this review paper focused on different waveform design approaches for modeling CRCS and DFRC systems. In addition, the prime objective of this review paper is to give a detailed view of the existing cooperative and dual-function waveform design approaches and provide a kick-start for new learners to work on this area. © 2023 IEEE.
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 97.74Â± 0.34 accuracy (with 18.32 MB size) compared to the denseNet201 of 99.13Â± 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. Â© 2024 IEEE.
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. Â© 2024 IEEE.
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. Â© 2024 IEEE.
Depth Information Fusion Using Radar-LiDAR-Camera Experimental Setup for ADAS Applications
(Institute of Electrical and Electronics Engineers Inc., 2024) Dayananda, N.B.; Srivastava, N.; Achala, G.; Nandagiri, A.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.
Improved scene perception makes the safe driving of automotive vehicles (AVs) feasible. The most common automotive sensors for AV perception for detection, classification, a nd t racking a re t he Light Detection And Ranging (LiDAR), radar, and camera sensors. The most reliable sensors for determining range are LiDAR and radar. In this research, we consider the referencing from the camera-based object recognition to fuse the LiDAR and radar point cloud data. To minimize any unintended effects from sensor orientation and sampling time, all three sensors are installed, calibrated, and time-aligned for this experiment. Subsequently, the obtained camera sensor data is subjected to object detection using a MobileNet-based deep neural network (DNN). The radar and LiDAR point cloud data are projected with the two-dimensional bounding box width, length, and height used for object recognition. Following that, the range information from the radar and LiDAR is retrieved and combined using a weighted average fusion algorithm. This experiment is run on the ROS platform, using AWR1642 radar sensor and RealSense LiDAR camera L515 sensor. The object detection from the camera and conducting fusion on the radar and LiDAR sensor is a potential algorithm for the Advanced Driver Assistant System (ADAS) emergency brake assistant (EBA) function. Â© 2024 IEEE.
Design of Barker-7 Radar Pulse Compressor Using DSP Architecture Minimization Techniques
(Institute of Electrical and Electronics Engineers Inc., 2024) Anoop, K.; Dayananda, B.N.; Achala, G.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.
Pulse compression algorithms play an important role in achieving better range resolution (RR) in automobile radar and ultrasonic sensors. The RR is the sensor's capacity to distinguish between two targets positioned in the same angular direction but at different distances. The Digital Signal Processing Architecture (DSPA) used in the pulse compressor is crucial for reaching high speeds or reducing hardware needs. This work presents two unique radar pulse compressor DSPAs for Barker-7 sequences, based on latency and area. One proposed design is the unfolding DSPA technology, which increases sample rate and speed. At the same time, another architecture, the folding DSPA approach, reduces hardware utilization and power consumption. The proposed DSPA design architectures are implemented and verified with the Artix-7 FPGA Kit. The unfolded Barker-7 pulse compressor increased the speed by 1.87 times as compared to the standard broadcasting Barker-7 realization, as demonstrated by the hardware implementation results. Folded Barker-7 architecture consumed 89% less power than existing Barker-7 broadcast architecture. The DSPAs proposed for the Barker-7 pulse compressor are ideal for ultrasonic, sonar, and radar applications that require high speed and low power. Â© 2024 IEEE.
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. Â© 2024 IEEE.
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. Â© 2024 IEEE.
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. Â© 2024 IEEE.
FPGA Implementation of SSRS Codes for NAND Flash Memory Device
(Institute of Electrical and Electronics Engineers Inc., 2024) Achala, G.; Nandana, S.; Jomy, F.; Girish, M.M.; Shripathi Acharya, U.S.; Srihari, P.; Cenkarmaddi, L.R.
NAND flash memory is a non-volatile storage device that is extensively used in personal electronic gadgets, digital television, digital cameras, and many consumer/ professional electronics devices. Error control coding techniques have been incorporated to improve the integrity of information stored in these devices. We have synthesized the Subfield Subcodes of Reed Solomon codes (SSRS) for use on Multi-Level cell (MLC), Triple Level Cell (TLC), and Quadruple Level Cell (QLC) NAND flash devices. The primary advantage of these codes is that the codeword symbols can be correctly matched to the number of bits that can be stored in these multilevel cells. Deployment of these codes improves the integrity of information storage and useful life. This paper describes the implementation of the encoder and decoder of SSRS codes synthesized for MLC, TLC, and QLC NAND flash devices. The encoder circuit is designed using addition and multiplication tables derived from elements of synthesized SSRS codes. The Non-binary decoding procedure consists of the syndrome computation, Berlekamp -Massey algorithm, Chein search, and Forney's algorithm. The designed encoder requires 16% resources for MLC, 18% of resources for TLC, and 18% of resources for QLC. This research work has reported the design of very high rate (R ≥ 0.97) codes that can bring about significant improvements to the Undetected Bit Error Rate (UBER) even when the Raw Bit Error rate (RBER) values are significant (> 10-3). © 2013 IEEE.
High-Speed Strassen Matrix Multiplication Accelerators for 2D Kalman Filter
(Institute of Electrical and Electronics Engineers Inc., 2024) Pudi, M.; Dayananda, B.N.; Achala, G.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.
High-speed and low-area Kalman filter (KF) algorithms are critical in autonomous vehicles, robotics, military, target tracking, and other applications. KF requires a large number of matrix multiplications (complexity of order O(n3)), which increases the number of cycles needed to process data. There is a vital requirement to develop a matrix multiplication accelerator module to enhance the efficiency of the entire Kalman Filter (KF) process. This paper proposes two accelerator models for KF, namely pipelined Strassen matrix multiplication and hybrid Strassen matrix multiplication. We propose pipelining the existing Strassen matrix multiplication architecture to reduce critical time to a single multiplication operation. To accelerate the KF and target the low area, we present a hybrid Strassen matrix multiplication architecture that reduces higher dimensions using the Strassen algorithm and computes the 2 Ã— 2 matrix using standard matrix multiplication. The Strassen algorithm, pipelined Strassen algorithm, and hybrid Strassen algorithm-based accelerators are designed for the Vertix-7 FPGA to determine the maximum possible frequency and space limitations. For demonstration purposes, a 2D-KF is studied with a four-dimensional state vector. Â© 2024 IEEE.
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. Â© 2024 IEEE.
MIMO-SAR Image Reconstruction Experiment Using Back-Projection Algorithm with Automotive Radar for ADAS Applications
(Institute of Electrical and Electronics Engineers Inc., 2024) Jena, P.; Singh, A.; Vandana, S.G.; Nandagiri, A.; Srihari, P.; Pardhasaradhi, B.; Cenkarmaddi, L.R.
Synthetic aperture radar (SAR) imaging has numerous uses in surface mapping, civil infrastructure, remote sensing, and terrain monitoring. Despite the benefits of multiple input multiple outputs (MIMO) in automotive radars, they are primarily used to provide range, azimuth, and elevation information for automotive applications. Obtaining acceptable angular resolution for automotive radar is a recurring difficulty due to vehicle-to-vehicle, vehicle-to-ground, vehicle-to-guardrail, and vehicle-to-tunnel discrimination. The purpose of this work is to demonstrate MIMO-SAR for finer angular resolution utilizing the 77-GHz Texas Instruments (TI) frequency-modulated continuous wave (FMCW) AWR1642 radar. SAR and MIMO radar topologies are used to increase synthetic or virtual aperture while maintaining adequate angular resolution. SAR is used to rebuild images from experimental data, and the images are created using a backpropagation algorithm. The findings are presented for SAR, MIMO, and MIMO-SAR. Furthermore, the experimental demonstration of MIMO-SAR using 77 GHz automobile radar verifies the prior modeling results. In addition, MIMO-SAR has been shown to provide better angular resolution than SAR and MIMO approaches. This algorithm's superior performance makes it appropriate for the automotive industry to perform SAR imaging on ego-corner deployed short-range radars (SRR) to picture guard rails, crossing vehicles, and VRUs. Â© 2024 IEEE.
Multitarget Detection and Tracking by Mitigating Spot Jammer Attack in 77-GHz mm-Wave Radars: An Experimental Evaluation
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumuda, D.K.; Vandana, G.S.; Pardhasaradhi, B.; Raghavendra, B.S.; Srihari, P.; Cenkarmaddi, L.R.
Small form factor radar sensors at millimeter wavelengths find numerous applications in the industrial and automotive sectors. These radar sensors provide improved range resolution, good angular resolution, and enhanced Doppler resolution for short range and ultrashort ranges. However, it is challenging to detect and track the targets accurately when a radar is interfered by another radar. This article proposes an experimental evaluation of a 77-GHz IWR1642 radar sensor in the presence of a second 77-GHz AWR1642 radar sensor acting as a spot jammer. A real-time experiment is carried out by considering five different targets of various cross sections, such as a car, a larger size motorcycle, a smaller size motorcycle, a cyclist, and a pedestrian. The collected real-time data are processed by four different constant false alarm rate detectors, cell averaging (CA)-CFAR, ordered statistics (OS)-CFAR, greatest of CA (GOCA)-CFAR, and smallest of CA (SOCA)-CFAR. Following that, data from these detectors are fed into two different clustering algorithms (density-based spatial clustering of applications with noise (DBSCAN) and K-means), followed by the extended Kalman filter (EKF)-based tracker with global nearest neighbor (GNN) data association, which provide tracks of various targets with and without the presence of a jammer. Furthermore, four different metrics [tracks reported (TR), track segments (TSs), false tracks (FTs), and track loss (TL)] are used to evaluate the performance of various tracks generated for two clustering algorithms with four detection schemes. The experimental results show that the DBSCAN clustering algorithm outperforms the K-means clustering algorithm for many cases. © 2001-2012 IEEE.
Performance Analysis of Spectrum Sharing Radar in Multipath Environment
(Institute of Electrical and Electronics Engineers Inc., 2023) Gunnery, G.; Pardhasaradhi, B.; Mahipathi, A.C.; Prashantha Kumar, P.K.; Srihari, P.; Cenkarmaddi, L.R.
Radar based sensing and communication systems sharing a common spectrum have become a potential research problem in recent years due to spectrum scarcity. The spectrum sharing radar (SSR) is a new technology that uses the total available bandwidth (BW) for both radar based sensing and communication. Unlike traditional radar, the SSR divides the total available BW into radar-only and mixed-use bands. In a radar-only band, only radar sensor signals can be transmitted and received. In contrast, radar and communication signals can both be transmitted and received in the mixed-use band. Taking such BW sharing into account, this paper investigates the performance of SSR in an information-theoretic sense. To evaluate performance, mutual information (MI), spectral efficiency (SE) and capacity (C) metrics are used. Initially, this paper considered a clean environment (no multipath) in order to evaluate performance metrics in the mixed-use band with and without successive interference cancellation. Following that, this paper addresses the performance of BW allocation by allocating low to high BW in mixed-band. Furthermore, the performance metrics are extended to account for the multipath environment, and the same analogy as in a clean environment is used. In addition, the MI and SE of traditional radar system is taken into account when comparing the performance of SSR with and without the use of the SIC. Finally, MI and capacity results show that using the SIC scheme in a mixed-use band yields performance comparable to traditional radar and communication system. In terms of SE, the SSR with SIC scheme outperforms traditional radar and communication system. © 2020 IEEE.
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. Â© 2024 IEEE.
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. Â© 2024 IEEE.
Robust Positioning and Grubbs Outlier Test for Navigation in GPS Spoofing Environment
(Institute of Electrical and Electronics Engineers Inc., 2022) Pardhasaradhi, B.; Lingadevaru, P.; Bn, B.R.; Srihari, P.; Cenkarmaddi, L.R.
Global positioning system (GPS) is favored to provide the position, velocity, and time (PVT) details across the globe. This paper proposes an epoch-by-epoch robust positioning algorithm followed by the Grubbs outlier test to address the GPS spoofing problem. We propose to accept both authentic and spoofed GPS signals to compute the robust positions. The robust positioning considers all possible combinations of measurements and generates several position estimates, which contain actual position, spoof position, and biased positions. In this case, the positions evolved due to spoof pseudorange measurements must be removed. Hence, we model eliminating spoof locations as an outlier problem and is addressed using Grubbs outlier test. The median of the processed data after the Grubbs test is the positional information at that epoch. Moreover, this problem is also extended to the Kalman filter's (KF) framework to address the time-varying kinematics of the target. Simulations are carried out for various numbers of actual and spoofed pseudorange measurements. In order to illustrate the robustness of the proposed technique, position root mean square error (PRMSE) is taken as a metric. Â© 2022 IEEE.