Tag: Radar

Posted on

The article titled “Performance Analysis of Spectrum Sharing Radar in Multipath Environment” has been accepted for publication in the IEEE Open Journal of the Communications Society (2023).

Gunnery Srinath, Bethi Pardhasaradhi, Ashoka Chakravarthi Mahipathi,
Prashantha Kumar H, Pathipati Srihari, and Linga Reddy Cenkeramaddi, “Performance Analysis of Spectrum Sharing Radar in Multipath Environment,” has been accepted for publication in the IEEE Open Journal of the Communications Society (2023).

Keywords: Radar, Communication systems, Measurement, Wireless sensor networks, Wireless communication, Receivers, Interference cancellation

Abstract: 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.

More details: DOI: 10.1109/OJCOMS.2023.3240116

Posted on

The article titled, “Multi-target Angle of Arrival Estimation using Rotating mmWave FMCW Radar and Yolov3,” has been accepted for publication in the IEEE Sensors Journal (2022).

Wilson A N, Abhinav Kumar, Ajit Jha, and Linga Reddy Cenkeramaddi, “Multi-target Angle of Arrival Estimation using Rotating mmWave FMCW Radar and Yolov3,” has been accepted for publication in the IEEE Sensors Journal (2022).

Keywords: Estimation, Radar, Millimeter wave communication, Sensors, Receiving antennas, Chirp, Radar detection

Abstract: It is still challenging to accurately localize unmanned aerial vehicles (UAVs) from a ground control station (GCS) using various sensors. The mmWave frequency-modulated continuous wave (FMCW) radars offer excellent performance for target detection and localization in harsh environments and low lighting conditions. However, the estimated angle of arrival (AoA) of targets in the captured scene is quite poor. This article focuses on improving AoA estimation by combining the cutting-edge machine learning (ML) algorithms with a mechanical radar rotor setup. An mmWave FMCW radar system is mounted on a programmable rotor to capture range–angle maps of targets at various locations. The range–angle images are then labeled and trained further with the Yolov3 algorithm. Subsequent testing reveals that for detected target objects, the centroid of the bounding boxes from the detected objects provides accurate AoA estimation with very low root mean square error (RMSE). The results show that the proposed approach outperforms traditional methods in terms of performance and estimation accuracy.

More details:DOI: 10.1109/JSEN.2022.3231790

Posted on

The article titled, “Multi Target Detection and Tracking by Mitigating Spot Jammer Attack in 77GHz mm-Wave Radars: An Experimental Evaluation,” has been accepted for publication in IEEE Sensors Journal 2022.

Kumuda D K, Vandana G S, Bethi Pardhasaradhi, B S Raghavendra, Pathipati Srihari, and Linga Reddy Cenkeramaddi, “Multi Target Detection and Tracking by Mitigating Spot Jammer Attack in 77GHz mm-Wave Radars: An Experimental Evaluation,” has been accepted for publication in IEEE Sensors Journal 2022.

Keywords: Radar tracking, Sensors, Radar, Target tracking, Jamming, Radar detection, Millimeter wave communication

Abstract: 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.

More details:DOI: 10.1109/JSEN.2022.3227012

Posted on

The article titled, “Automatic Contact-less Monitoring of Breathing Rate and Heart Rate utilizing the Fusion of mmWave Radar and Camera Steering System,” has been accepted for publication in the IEEE Sensors Journal (2022).

Khushi Gupta, Srinivas M. B., Soumya J, Om Jee Pandey, Linga Reddy Cenkeramaddi, “Automatic Contact-less Monitoring of Breathing Rate and Heart Rate utilizing the Fusion of mmWave Radar and Camera Steering System,” has been accepted for publication in the IEEE Sensors Journal (2022).

Keywords: Radar, Heart rate, Monitoring, Sensors, Cameras, Radar measurements, Personnel

Abstract: The demand for noncontact breathing and heart rate measurement is increasing. In addition, because of the high demand for medical services and the scarcity of on-site personnel, the measurement process must be automated in unsupervised conditions with high reliability and accuracy. In this article, we propose a novel automated process for measuring breathing rate and heart rate with mmWave radar and classifying these two vital signs with machine learning. A frequency-modulated continuous-wave (FMCW) mmWave radar is integrated with a pan, tilt, and zoom (PTZ) camera to automate camera steering and direct the radar toward the person facing the camera. The obtained signals are then fed into a deep convolutional neural network to classify them into breathing and heart signals that are individually low, normal, and high in combination, yielding six classes. This classification can be used in medical diagnostics by medical personnel. The average classification accuracy obtained is 87% with precision, recall, and an F1 score of 0.93.

More details: DOI: 10.1109/JSEN.2022.3210256

Posted on

The article, “A Novel Angle Estimation for mmWave FMCW radars using Machine Learning,” has been accepted for publication in the IEEE Sensors Journal.

L. R. Cenkeramaddi, P. K. Rai, A. Dayal, J. Bhatia, A. Pandya, J. Soumya, A. Kumar, & A. Jha., “A Novel Angle Estimation for mmWave FMCW radars using Machine Learning,” IEEE Sensors Journal 2021 (in press).

Keywords: Radar, Radar antennas, Estimation, Azimuth, Chirp, Sensors, Machine learning

Abstract: In this article, we present a novel machine learning based angle estimation and field of view (FoV) enhancement techniques for mmWave FMCW radars operating in the frequency range of 77 – 81 GHz. Field of view is enhanced in both azimuth and elevation. The Elevation FoV enhancement is achieved by keeping the orientation of antenna elements in elevation. In this orientation, radar focuses the beam in vertical direction there by enhancing the elevation FoV. An Azimuth FoV enhancement is achieved by mechanically rotating the radar horizontally, which has antenna elements in the elevation. With the proposed angle estimation technique for such rotating radars, root mean square error (RMSE) of 2.56 degrees is achieved. These proposed techniques will be highly useful for several applications in cost-effective and reliable autonomous systems such as ground station traffic monitoring and control systems for both on ground and aerial vehicles.

More details:DOI: 10.1109/JSEN.2021.3058268

Posted on

ACPS Research Group along with top the Indian Institutes lead the Low-altitude UAV communication and tracking (LUCAT) project

  • 2018 – The Low-altitude UAV communication and tracking (LUCAT) project funded by INDNOR programme jointly by the Norwegian research council (NFR) and the Department of Science and Technology (DST), Project Manager and PI: Professor Linga Reddy Cenkeramaddi, ACPS Research Group, Department of ICT, UiA Grimstad, Project duration: 2019-2024.

**************************************************************************************************************

The LUCAT project aims to develop advanced and robust algorithms to detect and accurately trace rapidly moving UAVs, popularly called drones. The research takes place at the University of Agder’s ACPS Research Group, Department of Computational and Data Sciences (CDS), IISc Bangalore, India, the Robert Bosch Centre for Cyber-Physical Systems, IISc Bangalore, India, and the Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India. This is the only project where the prestigious Indian University IISc collaborates with a Norwegian university in relation to the areas of signal processing, communication technology, and machine learning.

Full project name: Low-altitude UAV Communication and Tracking (LUCAT)

Funding: Researcher Project, IKTPLUSS INDNOR Program, Research Council of Norway

Principal Managers: Prof. Linga Reddy Cenkeramaddi, Prof. Abhinav Kumar

and Prof. Phaneendra K. Yalavarthy.

Topic: The LUCAT project funded by IKTPLUSS-INDNOR (Joint Indo-Norwegian researcher projects within Information and Communication Technology) develops the technology for both communication and precise tracking of both manned and unmanned aerial vehicles operating in low-altitude corridors. The Autonomous and Cyber-Physical Systems (ACPS) research group at the University of Agder, Campus Grimstad, Norway in collaboration with the Department of Electrical Engineering at the Indian Institute of Technology, Hyderabad, India, and the Department of Computational and Data Science and Robert Bosch Centre for cyber-physical systems at the Indian Institute of Science, Bangalore, India, will jointly design, develop and implement the proposed technology. This project aims to detect and precisely track multiple rapidly moving unmanned aerial vehicles using smart radar sensors, as well as novel signal processing and wireless communication algorithms. New methods will be developed also to classify the objects in the flight corridors and the communication modules located within the unmanned aerial vehicles will be advanced software-defined radio modules with the ability to sense on-the-fly the radio-frequency environment, leading to the discovery of opportunities for communications (what is called spectrum cognizant communications). The tasks of tracking and communication will be cooperating and enhancing each other, improving substantially the performance of tracking and classification, as compared to the currently existing solutions.

Participant Institutions: ACPS-UiA, IISc Bangalore, IIT Hyderabad

Period: 2018 – 2024

Project details: In the near future, a large number of unmanned aerial vehicles, also known as drones, will pervade populated areas’ skies, serving millions of people worldwide for goods transportation, construction, agriculture, medical, surveillance, search-and-rescue operations, and a variety of other applications. Daily tasks such as food or packet delivery, grocery shopping, and surveillance, among others, will be carried out by autonomous UAVs in densely populated areas, resulting in profound changes in day-to-day human life. The LUCAT project, funded by IKTPLUSS-INDNOR (Joint Indo-Norwegian researcher projects in Information and Communication Technology by NFR and DST), is working to develop technology for accurate sensing, precise tracking, and communication of both manned and unmanned aerial vehicles operating in low-altitude corridors. We, the Autonomous and Cyber-Physical Systems Research Group at the University of Agder, Campus Grimstad, Norway, will design, develop, and implement the proposed technology in collaboration with the Department of Electrical Engineering, Indian Institute of Technology, Hyderabad, India, and the Department of Computational and Data Sciences, & the Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Sciences, Bangalore, India. Using mmWave radar sensors and other sensors, as well as novel signal processing and machine learning models, and wireless communication algorithms, this project aims to sense/detect and precisely track multiple rapidly moving unmanned aerial vehicles. New methods will also be developed to classify objects in flight corridors, and communication modules located within unmanned aerial vehicles will include advanced software-defined radio modules with the ability to sense the radio-frequency environment on-the-fly, leading to the discovery of communications opportunities (what is called spectrum cognizant communications). Sensing, tracking, and communication tasks will collaborate and enhance each other, significantly improving sensing, tracking, and classification performance when compared to currently available solutions for low-altitude traffic management systems. New techniques for the detection, localization, and classification of UAVs are developed using ground station mmWave radars. Hybrid communication schemes are explored for UAV-UAV and UAV-Ground station communications.

**************************************************************************************************************