Pub Date : 2025-04-28DOI: 10.1109/TRS.2025.3564861
Alaa G. Zahra;Ahmed Youssef;Peter F. Driessen
Due to the ongoing advancements in small unmanned systems (SUSs), the field of study on detecting targets with small radar cross section (RCS) areas is constantly expanding. Due to their widespread use in both military and civilian fields, drones are considered the most significant class of small unmanned devices, garnering significant attention. As a result, numerous methods have been developed to improve radar detection performance by mainly increasing its processing gain (PG) in order to keep up with the advancement of drone capabilities. In this article, we introduce a multirate algorithm for improving the PG of the pulsed radar to enhance its detection performance of small RCS targets. The proposed method depends on acquiring multiple samples per subpulse from the received phase-coded signal and two coherent pulse intervals (CPIs) for decision-making. The simulation results are represented to show the provided PG to the system. Moreover, the field-programmable gate array (FPGA) implementation results and the utilized resources of the suggested algorithm are shown to demonstrate the superiority of our technique compared to other conventional methods.
{"title":"Design and Field-Programmable Gate Array Realization of a Multirate Multisampling Algorithm for Improving Signal-to-Noise Ratio in Pulse Compression Radars","authors":"Alaa G. Zahra;Ahmed Youssef;Peter F. Driessen","doi":"10.1109/TRS.2025.3564861","DOIUrl":"https://doi.org/10.1109/TRS.2025.3564861","url":null,"abstract":"Due to the ongoing advancements in small unmanned systems (SUSs), the field of study on detecting targets with small radar cross section (RCS) areas is constantly expanding. Due to their widespread use in both military and civilian fields, drones are considered the most significant class of small unmanned devices, garnering significant attention. As a result, numerous methods have been developed to improve radar detection performance by mainly increasing its processing gain (PG) in order to keep up with the advancement of drone capabilities. In this article, we introduce a multirate algorithm for improving the PG of the pulsed radar to enhance its detection performance of small RCS targets. The proposed method depends on acquiring multiple samples per subpulse from the received phase-coded signal and two coherent pulse intervals (CPIs) for decision-making. The simulation results are represented to show the provided PG to the system. Moreover, the field-programmable gate array (FPGA) implementation results and the utilized resources of the suggested algorithm are shown to demonstrate the superiority of our technique compared to other conventional methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"756-767"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1109/TRS.2025.3564236
Matthew B. Heintzelman;Daniel B. Herr;Charles A. Mohr;Shannon D. Blunt;Cenk Sahin;Andrew Kordik
This work seeks to elucidate the relationship between interfering frequency-modulated (FM) radar waveforms and their observed separability. A statistical and analytical framework is developed through which the average separability is determined as a function of the mutual time–bandwidth product between the interfering waveforms. The analytically derived predictor for waveform separability is then compared to a long-observed heuristic. Since random waveforms exhibit stochastic cross correlations, the maximum deviation above the analytically derived predictor is also examined. High-dimensional Monte Carlo simulations are used to numerically validate the analytical results.
{"title":"Separability Analysis of Random FM Radar Waveforms","authors":"Matthew B. Heintzelman;Daniel B. Herr;Charles A. Mohr;Shannon D. Blunt;Cenk Sahin;Andrew Kordik","doi":"10.1109/TRS.2025.3564236","DOIUrl":"https://doi.org/10.1109/TRS.2025.3564236","url":null,"abstract":"This work seeks to elucidate the relationship between interfering frequency-modulated (FM) radar waveforms and their observed separability. A statistical and analytical framework is developed through which the average separability is determined as a function of the mutual time–bandwidth product between the interfering waveforms. The analytically derived predictor for waveform separability is then compared to a long-observed heuristic. Since random waveforms exhibit stochastic cross correlations, the maximum deviation above the analytically derived predictor is also examined. High-dimensional Monte Carlo simulations are used to numerically validate the analytical results.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"768-788"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1109/TRS.2025.3563492
Ruxin Zheng;Shunqiao Sun;Hongshan Liu;Honglei Chen;Jian Li
Millimeter-wave (mmWave) radars are indispensable for the perception tasks of autonomous vehicles, thanks to their resilience in challenging weather and light conditions. Yet, their deployment is often limited by insufficient spatial resolution for precise semantic scene interpretation. Classical super-resolution techniques adapted from optical imaging inadequately address the distinct characteristics of radar data. In response, our study herein redefines super-resolution radar imaging as a 1-D signal super-resolution spectral estimation problem by harnessing the radar domain knowledge, introducing innovative data normalization, signal-level augmentation, and a domain-informed signal-to-noise ratio (SNR)-guided loss function. Like an image drawn with points and lines, radar imaging can be viewed as generated from points (antenna elements) and lines (frequency spectra). Our tailored deep learning (DL) network for automotive radar imaging exhibits remarkable scalability and parameter efficiency, alongside enhanced performance in terms of radar imaging quality and resolution. We further present a novel real-world dataset, pivotal for both advancing radar imaging and refining super-resolution spectral estimation techniques. Extensive testing confirms that our super-resolution angular spectral estimation network (SR-SPECNet) sets a new benchmark in producing high-resolution radar range-azimuth (RA) images, outperforming existing methods. The source code and radar dataset utilized for evaluation will be made publicly available at https://github.com/ruxinzh/SR-SPECNet
{"title":"Model-Based Knowledge-Driven Learning Approach for Enhanced High-Resolution Automotive Radar Imaging","authors":"Ruxin Zheng;Shunqiao Sun;Hongshan Liu;Honglei Chen;Jian Li","doi":"10.1109/TRS.2025.3563492","DOIUrl":"https://doi.org/10.1109/TRS.2025.3563492","url":null,"abstract":"Millimeter-wave (mmWave) radars are indispensable for the perception tasks of autonomous vehicles, thanks to their resilience in challenging weather and light conditions. Yet, their deployment is often limited by insufficient spatial resolution for precise semantic scene interpretation. Classical super-resolution techniques adapted from optical imaging inadequately address the distinct characteristics of radar data. In response, our study herein redefines super-resolution radar imaging as a 1-D signal super-resolution spectral estimation problem by harnessing the radar domain knowledge, introducing innovative data normalization, signal-level augmentation, and a domain-informed signal-to-noise ratio (SNR)-guided loss function. Like an image drawn with points and lines, radar imaging can be viewed as generated from points (antenna elements) and lines (frequency spectra). Our tailored deep learning (DL) network for automotive radar imaging exhibits remarkable scalability and parameter efficiency, alongside enhanced performance in terms of radar imaging quality and resolution. We further present a novel real-world dataset, pivotal for both advancing radar imaging and refining super-resolution spectral estimation techniques. Extensive testing confirms that our super-resolution angular spectral estimation network (SR-SPECNet) sets a new benchmark in producing high-resolution radar range-azimuth (RA) images, outperforming existing methods. The source code and radar dataset utilized for evaluation will be made publicly available at <uri>https://github.com/ruxinzh/SR-SPECNet</uri>","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"709-723"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1109/TRS.2025.3563787
Joseph W. Sapp;Zorana Jelenak;Paul S. Chang;Stephen R. Guimond;James R. Carswell
Historically, the Imaging Wind and Rain Airborne Profiler (IWRAP) radar system has been used as a research instrument aboard the National Oceanic and Atmospheric Administration (NOAA) WP-3D Hurricane Hunter airplanes collecting data for postflight processing and analysis. For the 2020 hurricane season, we demonstrated an initial near-real-time (NRT) atmospheric 3D wind processing capability, where retrievals were produced during a flight and transmitted to servers on the ground. Subsequently, the 3D wind retrieval algorithms have advanced to use the Doppler spectrum sampled by the IWRAP radars to reject surface clutter. This allowed wind retrievals closer to the ocean surface but increased the complexity of the retrieval processor. This article describes the latest technology implemented in the IWRAP radar system from the raw measurement to the final NRT 3D wind retrieval product, including the calibration/validation methodologies.
{"title":"Near-Real-Time IWRAP 3D Wind Retrievals","authors":"Joseph W. Sapp;Zorana Jelenak;Paul S. Chang;Stephen R. Guimond;James R. Carswell","doi":"10.1109/TRS.2025.3563787","DOIUrl":"https://doi.org/10.1109/TRS.2025.3563787","url":null,"abstract":"Historically, the Imaging Wind and Rain Airborne Profiler (IWRAP) radar system has been used as a research instrument aboard the National Oceanic and Atmospheric Administration (NOAA) WP-3D Hurricane Hunter airplanes collecting data for postflight processing and analysis. For the 2020 hurricane season, we demonstrated an initial near-real-time (NRT) atmospheric 3D wind processing capability, where retrievals were produced during a flight and transmitted to servers on the ground. Subsequently, the 3D wind retrieval algorithms have advanced to use the Doppler spectrum sampled by the IWRAP radars to reject surface clutter. This allowed wind retrievals closer to the ocean surface but increased the complexity of the retrieval processor. This article describes the latest technology implemented in the IWRAP radar system from the raw measurement to the final NRT 3D wind retrieval product, including the calibration/validation methodologies.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"832-842"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multiple-input multiple-output (MIMO) radar technologies can improve radar detection capabilities and share frequencies with adjacent radar sites by transmitting nearly uncorrelated waveforms. Under certain system constraints, a set of finite-resolution digital-to-analog converters (DACs) can reduce hardware cost and power consumption. However, the waveform quantization process through DACs forces a continuous phase to lie within a discrete phase, which degrades auto- and cross-correlations. Therefore, it is usually desirable that the sequence has a finite alphabet achieving good correlation properties. Recently, uncorrelated waveform design by applying neural networks (NNs) in place of coding theory has received much attention. However, the design of phase-quantized sequences using NNs has been delicate because of differentiability with sequences modulated by discrete phase. This article proposes a framework for designing phase-quantized sequences using an NN. Numerical results show that sequences designed using the proposed framework have better correlation properties compared with those designed using existing algorithms.
{"title":"Use of ResNet Autoencoders for Designing Phase-Quantized Sequences With Good Correlation for MIMO Radar Systems","authors":"Ryota Sekiya;Hiroki Mori;Hiromi Hashimoto;Junichiro Suzuki","doi":"10.1109/TRS.2025.3562698","DOIUrl":"https://doi.org/10.1109/TRS.2025.3562698","url":null,"abstract":"Multiple-input multiple-output (MIMO) radar technologies can improve radar detection capabilities and share frequencies with adjacent radar sites by transmitting nearly uncorrelated waveforms. Under certain system constraints, a set of finite-resolution digital-to-analog converters (DACs) can reduce hardware cost and power consumption. However, the waveform quantization process through DACs forces a continuous phase to lie within a discrete phase, which degrades auto- and cross-correlations. Therefore, it is usually desirable that the sequence has a finite alphabet achieving good correlation properties. Recently, uncorrelated waveform design by applying neural networks (NNs) in place of coding theory has received much attention. However, the design of phase-quantized sequences using NNs has been delicate because of differentiability with sequences modulated by discrete phase. This article proposes a framework for designing phase-quantized sequences using an NN. Numerical results show that sequences designed using the proposed framework have better correlation properties compared with those designed using existing algorithms.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"681-694"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-17DOI: 10.1109/TRS.2025.3562000
Adnan Albaba;Marc Bauduin;S. Hamed Javadi;Eddy De Greef;André Bourdoux;Hichem Sahli
This work addresses the problem of autofocusing for forward-looking MIMO synthetic aperture radar (FL-MIMO-SAR) images. To this end, we first present and analyze the detailed geometry and signal model of the FL-MIMO-SAR autofocusing problem. Then, we propose and test a comprehensive pipeline for FL-MIMO-SAR autofocusing with automatic radar motion parameters estimation and compensation. The approach leverages a combination of three SAR image quality indicators (IQIs) to assess the performance of the autofocusing process, which is compatible with both time-domain and frequency-domain image reconstruction algorithms. Moreover, the computational complexity of the optimization problem is reduced by employing a guided backprojection (GBP) algorithm. Furthermore, we compare the three IQIs with respect to their sensitivity to different types of positioning errors. The performance of the proposed solution is quantitatively evaluated using different simulated scenarios and controlled experimental data from an anechoic chamber. Finally, we test the applicability of the proposed solution using real data from automotive scenarios. The results show that the proposed pipeline is capable of handling phase-only as well as range-cell-migration defocusing models.
{"title":"Image-Quality-Indicator-Based Autofocusing for High-Resolution Forward-Looking MIMO-SAR","authors":"Adnan Albaba;Marc Bauduin;S. Hamed Javadi;Eddy De Greef;André Bourdoux;Hichem Sahli","doi":"10.1109/TRS.2025.3562000","DOIUrl":"https://doi.org/10.1109/TRS.2025.3562000","url":null,"abstract":"This work addresses the problem of autofocusing for forward-looking MIMO synthetic aperture radar (FL-MIMO-SAR) images. To this end, we first present and analyze the detailed geometry and signal model of the FL-MIMO-SAR autofocusing problem. Then, we propose and test a comprehensive pipeline for FL-MIMO-SAR autofocusing with automatic radar motion parameters estimation and compensation. The approach leverages a combination of three SAR image quality indicators (IQIs) to assess the performance of the autofocusing process, which is compatible with both time-domain and frequency-domain image reconstruction algorithms. Moreover, the computational complexity of the optimization problem is reduced by employing a guided backprojection (GBP) algorithm. Furthermore, we compare the three IQIs with respect to their sensitivity to different types of positioning errors. The performance of the proposed solution is quantitatively evaluated using different simulated scenarios and controlled experimental data from an anechoic chamber. Finally, we test the applicability of the proposed solution using real data from automotive scenarios. The results show that the proposed pipeline is capable of handling phase-only as well as range-cell-migration defocusing models.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"668-680"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-14DOI: 10.1109/TRS.2025.3560355
Jiawei Luan;Jinshan Ding;Yuhong Zhang
Deep learning-based automatic target recognition (ATR) for synthetic aperture radar (SAR) has made significant advancements in recent years. However, many challenges persist, particularly in cross-domain applications from simulation training to measurement recognition. Although the electromagnetic simulation can provide abundant labeled training data, the domain shift between simulation and measurement results in poor generalization performance. Current methods often aim to reduce this discrepancy without a comprehensive analysis of domain shift. We adopt a novel perspective by splitting the SAR ATR into three parts: input, feature extraction, and output to analyze the domain shift. Guided by this analysis, we propose a multilevel alignment cross-domain recognition (MACR) network designed to progressively mitigate domain shift at the input, feature, and output levels, ultimately achieving full-process domain alignment between simulation and measurement. First, the gap is bridged through mutual conversion, generating simulated-like and measured-like samples to reduce the domain shift at the input level. Subsequently, adversarial learning is employed to diminish domain shift at the feature level. Finally, cross-domain knowledge distillation and pseudolabel filtering enforce consistency regularization based on category consistency priors between unlabeled measured and simulated-like samples, reducing domain shift at the output level. Experiments conducted on the synthetic and measured paired labeled experiment (SAMPLE) and SAMPLE+ datasets demonstrate the effectiveness of the proposed MACR, achieving state-of-the-art (SOTA) performance on both datasets.
{"title":"Unsupervised Cross-Domain Radar Target Recognition Using Multilevel Alignment","authors":"Jiawei Luan;Jinshan Ding;Yuhong Zhang","doi":"10.1109/TRS.2025.3560355","DOIUrl":"https://doi.org/10.1109/TRS.2025.3560355","url":null,"abstract":"Deep learning-based automatic target recognition (ATR) for synthetic aperture radar (SAR) has made significant advancements in recent years. However, many challenges persist, particularly in cross-domain applications from simulation training to measurement recognition. Although the electromagnetic simulation can provide abundant labeled training data, the domain shift between simulation and measurement results in poor generalization performance. Current methods often aim to reduce this discrepancy without a comprehensive analysis of domain shift. We adopt a novel perspective by splitting the SAR ATR into three parts: input, feature extraction, and output to analyze the domain shift. Guided by this analysis, we propose a multilevel alignment cross-domain recognition (MACR) network designed to progressively mitigate domain shift at the input, feature, and output levels, ultimately achieving full-process domain alignment between simulation and measurement. First, the gap is bridged through mutual conversion, generating simulated-like and measured-like samples to reduce the domain shift at the input level. Subsequently, adversarial learning is employed to diminish domain shift at the feature level. Finally, cross-domain knowledge distillation and pseudolabel filtering enforce consistency regularization based on category consistency priors between unlabeled measured and simulated-like samples, reducing domain shift at the output level. Experiments conducted on the synthetic and measured paired labeled experiment (SAMPLE) and SAMPLE+ datasets demonstrate the effectiveness of the proposed MACR, achieving state-of-the-art (SOTA) performance on both datasets.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"630-644"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-09DOI: 10.1109/TRS.2025.3559394
Mihail S. Georgiev;Aaron D. Pitcher;Timothy N. Davidson
A promising nonimagining approach to the classification of radar targets is to use the frequencies and attenuation rates of the resonant modes that present during a target’s late-time response (LTR) as features. Unfortunately, the estimation of these resonance parameters is rather sensitive to noise. However, we observe that when a large number of measurements of the LTR can be taken in a short time, the probability distribution of the estimates of the parameters can be estimated and then matched against a database of such distributions. That has the potential to reduce the sensitivity of the classification problem to noise. In this article, we develop a pragmatic approach to target classification using this distribution-matching approach and demonstrate its effectiveness through physical experiments. The proposed approach is shown to be highly robust to environmental clutter and somewhat robust to target orientation.
{"title":"Classification of Radar Targets via Distribution Matching of Late-Time Resonance Parameters","authors":"Mihail S. Georgiev;Aaron D. Pitcher;Timothy N. Davidson","doi":"10.1109/TRS.2025.3559394","DOIUrl":"https://doi.org/10.1109/TRS.2025.3559394","url":null,"abstract":"A promising nonimagining approach to the classification of radar targets is to use the frequencies and attenuation rates of the resonant modes that present during a target’s late-time response (LTR) as features. Unfortunately, the estimation of these resonance parameters is rather sensitive to noise. However, we observe that when a large number of measurements of the LTR can be taken in a short time, the probability distribution of the estimates of the parameters can be estimated and then matched against a database of such distributions. That has the potential to reduce the sensitivity of the classification problem to noise. In this article, we develop a pragmatic approach to target classification using this distribution-matching approach and demonstrate its effectiveness through physical experiments. The proposed approach is shown to be highly robust to environmental clutter and somewhat robust to target orientation.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"645-655"},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The recent deployment of 5G technology in the C band has raised concerns regarding potential interference with aeronautical radar altimeters. The 5G systems in the C band operate within a frequency range of 3.7–3.98 GHz, which closely aligns with the operational frequency of radar altimeters, falling within the range of 4.2–4.4 GHz. This proximity in operational frequencies increases the possibility of interference between the two systems. In this article, we explore two primary objectives: first, to examine the potential for interference between the 5G C band and radar altimeters, and second, to develop techniques for mitigating this interference. To achieve these objectives, we assess interference in a real-world scenario, where multiple base stations (BSs) are deployed to serve an operational runway. In addition, two interference management techniques were proposed and evaluated within the assessed real-life scenario. The first involves the implementation of adaptive BS using the power control (PC) method, which aims to mitigate interference with minimal impact on coverage by adjusting the transmitting power for the BS that contributes the most to the interference model. A modification to this technique was applied to loop over the coverage areas instead of individual BSs. This technique is useful in scenarios, where BSs are implemented close to each other with overlapping coverage. Finally, a sequential quadratic programming (SQP) optimization algorithm was developed to optimize the locations of BSs, minimizing interference while maintaining coverage. This work has explored the impact of potential interference between 5G in the C band and radar altimeters and suggested practical methods to allow the coexistence of both systems, thereby ensuring aviation safety and fulfilling the telecommunication sector’s objectives.
{"title":"Assessment and Mitigation Approaches of 5G C-Band Interference With Aeronautical Radar Altimeter","authors":"Aisha Elsayem;Ali Massoud;Haidy Elghamrawy;Aboelmagd Noureldin","doi":"10.1109/TRS.2025.3557219","DOIUrl":"https://doi.org/10.1109/TRS.2025.3557219","url":null,"abstract":"The recent deployment of 5G technology in the C band has raised concerns regarding potential interference with aeronautical radar altimeters. The 5G systems in the C band operate within a frequency range of 3.7–3.98 GHz, which closely aligns with the operational frequency of radar altimeters, falling within the range of 4.2–4.4 GHz. This proximity in operational frequencies increases the possibility of interference between the two systems. In this article, we explore two primary objectives: first, to examine the potential for interference between the 5G C band and radar altimeters, and second, to develop techniques for mitigating this interference. To achieve these objectives, we assess interference in a real-world scenario, where multiple base stations (BSs) are deployed to serve an operational runway. In addition, two interference management techniques were proposed and evaluated within the assessed real-life scenario. The first involves the implementation of adaptive BS using the power control (PC) method, which aims to mitigate interference with minimal impact on coverage by adjusting the transmitting power for the BS that contributes the most to the interference model. A modification to this technique was applied to loop over the coverage areas instead of individual BSs. This technique is useful in scenarios, where BSs are implemented close to each other with overlapping coverage. Finally, a sequential quadratic programming (SQP) optimization algorithm was developed to optimize the locations of BSs, minimizing interference while maintaining coverage. This work has explored the impact of potential interference between 5G in the C band and radar altimeters and suggested practical methods to allow the coexistence of both systems, thereby ensuring aviation safety and fulfilling the telecommunication sector’s objectives.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"615-629"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-31DOI: 10.1109/TRS.2025.3556323
Gruffudd Jones;Morgan Coe;Lily Beesley;Leah-Nani Alconcel;Marco Martorella;Marina Gashinova
This article is concerned with the investigation and analysis of a new operational and technical capability to assess geosynchronous orbit (GEO) satellites from spaceborne platforms using extremely high-frequency radar operating at sub-THz frequencies. The concept of close monitoring and highly detailed imagery of GEO assets from all aspects, including those unattainable from the Earth, is developed based on the analysis of two proposed orbital deployment scenarios. Accounting for orbital perturbation factors during an extended period of time, the ability to build multiaspect ISAR imagery of the asset during single and multiple encounters is demonstrated, based on the mutual attitudes of the asset and the radar platform. A linearized model of the encounter geometry is presented and the approach to generate a sequence of ISAR image frames according to the geometry of the proposed scenarios is detailed. The simulation of ISAR frames at two frequency bands, centered at 75 and 300 GHz produced in a developed metaheuristic simulator, graphical electromagnetic ISAR simulator for sub-THz (GEIST), is demonstrated, to highlight the transition of scattering mechanisms and the change in visibility of particular features. Attitude-agnostic frame-to-frame image alignment and linear feature extraction using the Hough transform are then demonstrated on a sequence of simulated images.
{"title":"Strategies for Monitoring of Assets in Geosynchronous Orbit (GEO) Using Space-Based Sub-THz Inverse Synthetic Aperture Radar (ISAR)","authors":"Gruffudd Jones;Morgan Coe;Lily Beesley;Leah-Nani Alconcel;Marco Martorella;Marina Gashinova","doi":"10.1109/TRS.2025.3556323","DOIUrl":"https://doi.org/10.1109/TRS.2025.3556323","url":null,"abstract":"This article is concerned with the investigation and analysis of a new operational and technical capability to assess geosynchronous orbit (GEO) satellites from spaceborne platforms using extremely high-frequency radar operating at sub-THz frequencies. The concept of close monitoring and highly detailed imagery of GEO assets from all aspects, including those unattainable from the Earth, is developed based on the analysis of two proposed orbital deployment scenarios. Accounting for orbital perturbation factors during an extended period of time, the ability to build multiaspect ISAR imagery of the asset during single and multiple encounters is demonstrated, based on the mutual attitudes of the asset and the radar platform. A linearized model of the encounter geometry is presented and the approach to generate a sequence of ISAR image frames according to the geometry of the proposed scenarios is detailed. The simulation of ISAR frames at two frequency bands, centered at 75 and 300 GHz produced in a developed metaheuristic simulator, graphical electromagnetic ISAR simulator for sub-THz (GEIST), is demonstrated, to highlight the transition of scattering mechanisms and the change in visibility of particular features. Attitude-agnostic frame-to-frame image alignment and linear feature extraction using the Hough transform are then demonstrated on a sequence of simulated images.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"656-667"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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