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16th International conference on Computer and Automation Engineering (ICCAE)

作者： Samuktha, V. Shukla, Hershita Kumar, Nitish Tejasri, N. Reddy, D. Santhosh Rajalakshmi, P. IIT Hyderabad Dept Artificial Intelligence Hyderabad India IIT Hyderabad Ctr Interdisciplinary Programs Integrated Sensor Syst Hyderabad India IIT Hyderabad Dept Elect Engn Hyderabad India IIT Hyderabad Dept Elect Engn NM ICPS TiHAN Hyderabad India

ISBN: (纸本)9798350370058;9798350370164

In today's world, a sensor-based autonomous industry needs a range of sensors to understand its environment and decide how to function safely and productively. The precise and trustworthy assessment of the surroundings around a vehicle has made a variety of autonomous or assisted driving approaches possible. Hence, we provide a novel radar camera fusion technique based on deep learning that greatly enhances object identification performance. The combination of radar and camera data in particular has emerged as a promising strategy for improving the precision and dependability of object identification systems. However, the performance of current fusion algorithms is frequently constrained by issues with occlusion, variable illumination, and sensor noise. With enhancing capabilities of autonomous vehicles through different algorithms and perception methodologies, it has become important to look into the importance of computationally lighter algorithms which work efficiently as well. Based on the knowledge gleaned from analyzing several deep learning models, we came up with a modified fusion algorithm by adding a number of new operations. The processes of this algorithm consist of improved object identification techniques, feature extraction, and feature fusion. Our approach successfully addresses the issues posed by occlusion, varying illumination, and sensor noise by utilizing the complementary nature of radar and camera technologies, resulting in enhanced object detection performance. In conclusion, this study offers a cutting-edge deep learning-based radar camera fusion technique that surpasses current methods for object detection. Our approach provides improved accuracy, robustness, and real-time performance by using insights from several deep-learning models and including extra processes. The suggested approach provides a sizable improvement in sensor fusion methods, opening the door for more improvements to perception system enhancement.

关键词： radar signal processing Deep Learning Object detection Error Estimation Convolution Neural Networks Multi-sensor Calibration data Annotation, and Multi-modal fusion of sensors

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Research on Error Analysis Method of radar Over-the-Air Simulation 4

Research on Error Analysis Method of Radar Over-the-Air Simu...

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4th International conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Yu, Xiaojun Qu, Ge Zheng, Yingdong Zhang, Zhiqiang China Automotive Technology & Research Center Co. Ltd Tianjin China

ISBN: (纸本)9798331531409

sensor simulation is an important part of simulation test, and there are various ways to simulate sensors. Among them, there is a millimeter wave radar simulation method is called over-the-air (OTA) simulation, which uses a radar OTA platform to simulate the speed, angle, and distance of the target object, form radar echoes, and transmit them back to the radar sensor. This sensor simulation method retains the hardware of real radar sensor and combines virtual environments with real samples. However, there is still a problem of simulation error in this simulation method. This article proposes an error identification method for radar OTA simulation. A comparative experiment is designed to quantify the error of the OTA simulation method by comparing the results of track testing and simulation data under fixed scenes and target objects. The results show that the simulation data of OTA simulation has a small error compared to the simulation test data, which is completely within a reasonable error range. © 2024 IEEE.

关键词： radar simulators

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Cost-Efficient Computation Offloading in SAGIN: A Deep Reinforcement Learning and Perception-Aided Approach

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第12期42卷 3462-3476页

作者： Gao, Yulan Ye, Ziqiang Yu, Han Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China

The Space-Air-Ground Integrated Network (SAGIN), crucial to the advancement of sixth-generation (6G) technology, plays a key role in ensuring universal connectivity, particularly by addressing the communication needs of remote areas lacking cellular network infrastructure. This paper delves into the role of unmanned aerial vehicles (UAVs) within SAGIN, where they act as a control layer owing to their adaptable deployment capabilities and their intermediary role. Equipped with millimeter-wave (mmWave) radar and vision sensors, these UAVs are capable of acquiring multi-source data, which helps to diminish uncertainty and enhance the accuracy of decision-making. Concurrently, UAVs collect tasks requiring computing resources from their coverage areas, originating from a variety of mobile devices moving at different speeds. These tasks are then allocated to ground base stations (BSs), low-earth-orbit (LEO) satellite, and local processing units to improve processing efficiency. Amidst this framework, our study concentrates on devising dynamic strategies for facilitating task hosting between mobile devices and UAVs, offloading computations, managing associations between UAVs and BSs, and allocating computing resources. The objective is to minimize the time-averaged network cost, considering the uncertainty of device locations, speeds, and even types. To tackle these complexities, we propose a deep reinforcement learning and perception-aided online approach (DRL-and-Perception-aided Approach) for this joint optimization in SAGIN, tailored for an environment filled with uncertainties. The effectiveness of our proposed approach is validated through extensive numerical simulations, which quantify its performance relative to various network parameters.

关键词： radar sensors Resource management Spaceborne radar visualization Autonomous aerial vehicles Space-air-ground integrated networks Space-air-ground integrated network (SAGIN) deep reinforcement learning perception computation offloading vision sensor

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Traffic target detection algorithm based on 4D radar and vision fusion 4

Traffic target detection algorithm based on 4D radar and vis...

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4th International conference on Machine Learning and Intelligent Systems Engineering (MLISE)

作者： Lin, Li Feng, Lin Chongqing Univ Posts & Telecommun Sch Automat Chongqing Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In the intelligent road traffic system, the sensor module is required to quickly distinguish each traffic target, but the traditional camera-based or millimeter-wave radar-based detection system is difficult to meet this requirement. Aiming at the problem that the limitation of a single sensor makes the longitudinal traffic target indistinct and the detection speed slow, a method for target detection is developed by combining 4D millimeter wave radar with a camera. The ROI of the target detection region of interest is constructed by using the characteristics of 4D millimeter wave radar with stronger perception ability and faster data processing, which reduces the computing power for the subsequent visual traffic target detection. The test results of the proposed detection algorithm in the experimental field show that the detection method is suitable for practical road traffic target detection applications.

关键词： radar vision fusion Traffic target detection Adaptive clustering

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Elderly Fall Detection System Using mm-Wave radar sensor 4

Elderly Fall Detection System Using mm-Wave Radar Sensor

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4th International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Joy, Jeslet John, Amalda Theresa Anna Alex, Angel Nair, Adityakrishna S Sreesh, P.R. Manuel, Anto St Joseph's College of Engineering and Technology Dept. of Electronics and Communication Engineering Palai India

ISBN: (纸本)9798350343670

This project focuses on the implementation of an elderly fall detection system using millimeter-wave radar technology, prioritizing privacy preservation within indoor environments. By harnessing mm-wave radar, our system offers technical advantages over camera-based solutions. radar operates in the radio frequency spectrum, ensuring privacy, as it does not capture visual data, addressing concerns regarding surveillance and consent. Technical aspects encompass mm-wave radar sensor deployment, signal processing algorithm development for fall detection, and real-time data analysis integration. radar mitigates issues posed by low lighting, occlusions, and line-of- sight limitations common in camera-based systems. Additionally, machine learning enhances fall detection accuracy, reducing false alarms while maintaining high sensitivity. © 2024 IEEE.

关键词： fall detection machine learning millimeter wave mm-wave radar sensors Point Cloud radar sensor signal processing

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Geolocation using synthetic aperture radar multilateration 28

Geolocation using synthetic aperture radar multilateration

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conference on radar sensor technology XXVIII

作者： Doerry, A. W. Bickel, D. L. Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9781510674158;9781510674141

A single Synthetic Aperture radar (SAR) image is a 2-Dimensional projection of a 3-Dimensional scene, with very limited ability to estimate surface topography. However, with multiple SAR images collected from suitably different geometries, they may be compared with multilateration calculations to estimate characteristics of the missing dimension. The ability to employ effective multilateration algorithms is highly dependent on the geometry of the data collections, and can be cast as a least-squares exercise. A measure of Dilution of Precision (DOP) can be used to compare the relative merits of various collection geometries.

关键词： SAR geolocation stereo multilateration

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A quantitative study of the benefits of the Third-order over the Second-order Harmonic radar 28

A quantitative study of the benefits of the Third-order over...

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conference on radar sensor technology XXVIII

作者： Lima, Rita J. Abad Li, Changzhi Texas Tech Univ TTU Dept Elect & Comp Engn ECE Lubbock TX 79409 USA

ISBN: (纸本)9781510674158;9781510674141

Nonlinear radar technology has been employed in various applications for a few decades, primarily focusing on second-order harmonic radar systems. This study delves into the potential benefits and drawbacks of utilizing higher-order harmonic radar systems. We present a comparative analysis between second- and third-order harmonic systems using Cadence AWR Visual System Simulator (VSS) followed by experimental validation. In the experiments, target emulation is achieved through nonlinear tags. In contrast, cluttered environments near the targets are simulated using corner reflectors, with the tag and the corner reflector moving at different frequencies facilitated by Zaber linear actuators. Our findings reveal both the advantages and limitations associated with higher-order systems, offering valuable insights into an underexplored area of research within the domain of harmonic radar technology. This contribution addresses the existing gap in the literature pertaining to higher-order harmonic radars by providing comparative analyses using both measurement and simulation data.

关键词： harmonic radars nonlinear tag clutter clutter rejection nonlinear systems RFID tag CW radars

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Real-Time Human Motion Tracking Technique Based On a 77 GHz FMCW MIMO radar System 16

Real-Time Human Motion Tracking Technique Based On a 77 GHz ...

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16th International conference on Microwave and Millimeter Wave technology (ICMMT)

作者： Zhang, Jiayu Zhang, Zhiwei Li, Yuchen Ge, Changzhan Mao, Junfa Shanghai Jiao Tong Univ State Key Lab Radio Frequency Heterogeneous Integ Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China

ISBN: (纸本)9798350389968

In the realm of intelligent living, millimeter-wave radar is gradually finding application, leading to a heightened level of convenience. In this paper, a novel technique enabling real-time orientation of an electronic fan towards a human target based on a 77 GHz radar sensor system is proposed. Leveraging frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar theory and density-based spatial clustering of applications with noise (DBSCAN) algorithm, the radar system accurately acquires center angle related to the human target. Subsequently, the angle data is transmitted to the STM32 microcontroller via UART, facilitating the generation of a pulse width modulated wave for precise control of stepper motor rotation. The radar system not only accurately detects angle from a corner reflector, validating measurement accuracy but also successfully achieves real-time human presence sensing, dynamically adjusting the fan's rotation. The experiments show that the system can significantly implement the real-time rotation of the fan following the human body in the office environment.

关键词： frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) millimeter wave radar sensing

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Enhanced Maritime Target Recognition Using YOLO-LSTM-MF radar-Infrared Fusion technology 4

Enhanced Maritime Target Recognition Using YOLO-LSTM-MF Rada...

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4th International conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Peng, Yufei Sun, Dianxing Peng, Ruihui Zhang, Tianyuan Qingdao Innovation and Development Center Harbin Engineering University Shandong Qingdao China

ISBN: (纸本)9798331534622

This paper presents a study on radar-infrared composite guidance technology, focusing on the integration of radar and infrared sensors for enhanced target detection and tracking in complex environments. The primary challenge addressed in this work is the efficient fusion of multi-sensor data to improve guidance accuracy and resistance to countermeasures. We explore the development of novel algorithms for sensor data synchronization, spatial alignment, and real-time processing, aiming to optimize the overall performance of the guidance system. The paper also examines the key technical hurdles in combining radar and infrared systems, such as sensor fusion, error correction, and anti-jamming capabilities. By focusing on these critical issues, this study contributes to the advancement of composite guidance technology, offering potential applications in missile guidance systems and other precision targeting fields. Experimental results validate the effectiveness of the proposed approach, demonstrating its superior performance compared to conventional guidance methods. The findings highlight the potential for significant improvements in both military and civil applications. © 2024 IEEE.

关键词： radar target recognition

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A Newly Developed Method for Georegistration of Synthetic Aperture radar Imagery 28

A Newly Developed Method for Georegistration of Synthetic Ap...

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conference on radar sensor technology XXVIII

作者： Shore, Aimee Tellez, Joshua West, R. Derek Koehler, Frederick W. Schwartzkopf, Wade Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA Natl Geospatial Intelligence Agcy Springfield VA USA

ISBN: (纸本)9781510674158;9781510674141

Georegistration of synthetic aperture radar (SAR) images is an ongoing problem. We present a SAR-sensor independent method that enables assessment of image geolocation accuracy at nearly any terrestrial scene location. To achieve this, a collected image is co-registered against a global reference image set, such as the high-fidelity X-band RCS layer of the TanDEM-X High Resolution Elevation data Exchange Program (TREx) data set, which has well-characterized horizontal and vertical errors. Measuring sensor geolocation accuracy against a high-fidelity broad-area reference set can mitigate the effect of location errors that, if left uncorrected, may have a deleterious impact on downstream applications. In particular, the proposed method could help automate the assessment and quality control of existing and emerging constellations of commercial satellite systems. Our co-registration approach first applies a pre-warp operation that exhausts known SICD (sensor Independent Complex data) metadata from a collected image to get within the neighborhood of the true geolocation in the reference image. Then we attain a fine scale registration through local feature detection and extraction, via an established computer vision algorithm. Finally, we compute a similarity transformation on matching points between the test and reference images;the transformation includes translation values, which are used for assessing geolocation accuracy, as well as scale and rotation estimates that are used for a confidence measure.

关键词： Synthetic aperture radar SAR geolocation georegistration co-registration sensor location error

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