Iet Radar Sonar and Navigation最新文献_第5页

Machine Learning Doppler-Tolerant One-Bit Radar Detectors 机器学习容忍多普勒位雷达探测器

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-06-13 DOI: 10.1049/rsn2.70011

Kyle P. Wensell, Changshi Zhou, Alexander M. Haimovich, Abdallah Khreishah, Brent Lozneanu, Brandon Cannizzo, Evan A. Young, Lam T. Vo

{"title":"Machine Learning Doppler-Tolerant One-Bit Radar Detectors","authors":"Kyle P. Wensell, Changshi Zhou, Alexander M. Haimovich, Abdallah Khreishah, Brent Lozneanu, Brandon Cannizzo, Evan A. Young, Lam T. Vo","doi":"10.1049/rsn2.70011","DOIUrl":"10.1049/rsn2.70011","url":null,"abstract":"Doppler-tolerant waveforms are some of the most common radar waveforms used in practice. However, their deterministic and repetitive nature impedes control of mutual interference when multiple radars operate in close proximity. Noise radar technology may address this problem but is not Doppler tolerant. In this study, we design a machine learning radar detector capable of Doppler-tolerant performance with noise waveforms. The detector is implemented as a feedforward multilayer neural network. We show that the detector may be trained to operate with one-bit data. Further, to evaluate the proposed detector's performance, we derive a closed-form expression of the receiver operating characteristic (ROC) for one-bit detection of a Swerling 1 target using the square-law detector under the assumption of low signal-to-noise ratio (SNR). Numerical results demonstrate that the proposed machine learning detector, when suitably trained, is capable of operating with Doppler tolerance over a wide range of Doppler shifts.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Depth Estimation Method for Continuous Acoustic Signal Targets in Shallow Water Using a Linear Array 基于线性阵列的浅水连续声信号目标深度估计方法

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-06-07 DOI: 10.1049/rsn2.70034

Siqi Du, Dong Han, Ning Li

{"title":"Depth Estimation Method for Continuous Acoustic Signal Targets in Shallow Water Using a Linear Array","authors":"Siqi Du, Dong Han, Ning Li","doi":"10.1049/rsn2.70034","DOIUrl":"10.1049/rsn2.70034","url":null,"abstract":"To overcome the limitations of existing methods in processing continuous acoustic signals—particularly issues related to modal aliasing and the constraints of Pekeris waveguide applications—this study proposes a depth estimation approach for continuous acoustic targets using hydrophone linear arrays. A horizontal linear array, designed to meet the resolution requirements of the F–K transform, is deployed to receive continuous acoustic signals. Environmental parameters are incorporated to fit the sound speed profile, and modal time-delay differences are calculated based on normal mode propagation models. Temporal compensation is then applied to each modal component of the received signals across array elements. The corrected signal matrix undergoes a bidirectional F–K transform transformation into the frequency–wavenumber domain, allowing for clear separation of the normal modes of continuous signals. Frequency–wavenumber curves are characterised based on the sound speed profile, and binary mask filters are designed to extract modal energy. Finally, a depth estimation matching function is constructed to facilitate energy search and matching. Simulation results indicate that the proposed method achieves depth estimation errors of less than 5% for 10-s broadband acoustic signals under negative sound speed profiles and real shallow-sea waveguide conditions. The method demonstrates improved stability and applicability in variable sound speed environments, offering greater practical value for real-world shallow-sea scenarios.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Passive Target Motion Analysis With Own-Ship Location Uncertainty in the Presence of Non-Gaussian Sensor Noise 非高斯传感器噪声存在下具有自船位置不确定性的被动目标运动分析

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-06-05 DOI: 10.1049/rsn2.70043

Rohit Kumar Singh, Shreya Das, Shovan Bhaumik

{"title":"Passive Target Motion Analysis With Own-Ship Location Uncertainty in the Presence of Non-Gaussian Sensor Noise","authors":"Rohit Kumar Singh, Shreya Das, Shovan Bhaumik","doi":"10.1049/rsn2.70043","DOIUrl":"10.1049/rsn2.70043","url":null,"abstract":"Passive target motion analysis (TMA) is traditionally performed using angle-only measurements, which requires the own-ship to execute a manoeuvre to make the tracking system observable. These manoeuvres are burdensome for the naval community. In contrast, this work explores underwater TMA by incorporating time delay and Doppler frequency measurements along with angle data, eliminating the need for own-ship manoeuvre and improving estimation accuracy. Measurement noises are assumed to follow a non-Gaussian distribution, and maximum correntropy (MC)-based Bayesian filtering framework is adopted to solve the problem. Furthermore, the own-ship's position is inherently uncertain due to navigation errors, and this work addresses the uncertainty by modifying the measurement noise covariance matrix within the estimation framework. Simulation results demonstrate that the proposed methodology achieves improved tracking performance in terms of root mean square error (RMSE) and <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $%$</annotation>\u0000 </semantics></math> track loss compared to existing state-of-the-art MC Kalman filtering approaches.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Area Controllable Suppression Jamming Method Against SAR Based on Two-Dimensional Phase Mismatch 基于二维相位失配的SAR多区域可控抑制干扰方法

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-06-05 DOI: 10.1049/rsn2.70040

Guangyuan Li, GuiKun Liu, Zhenyang Xu, Haoming Xu, Zhengshuai Li, Peng Wang, Yueyang Zhang, Liang Li

{"title":"Multi-Area Controllable Suppression Jamming Method Against SAR Based on Two-Dimensional Phase Mismatch","authors":"Guangyuan Li, GuiKun Liu, Zhenyang Xu, Haoming Xu, Zhengshuai Li, Peng Wang, Yueyang Zhang, Liang Li","doi":"10.1049/rsn2.70040","DOIUrl":"10.1049/rsn2.70040","url":null,"abstract":"In the imaging process of SAR, the secondary phase mismatch can cause image defocusing. In this paper, a suppression jamming method against SAR based on two-dimensional (2D) phase mismatch is proposed through a designed jammer system. By extracting and resampling the intercepted radar signal through the designed jammer, the bandwidth of the linear frequency modulation (LFM) signal can be changed, which causes defocusing in the range dimension after matching filtering. Azimuth phase mismatch is achieved through velocity mismatch, which leads to azimuth defocusing after azimuth matching filtering. Efficient coverage of multiple dispersed important regions can be achieved by adjusting the parameters of jamming targets reasonably, such as modulation bandwidth, azimuth velocity, jamming positions and jamming power. Theoretical analysis is conducted on the implementation of the algorithm and its 2-D controllability in terms of jamming location and jamming area, as well as the required jamming power. The correctness of the theoretical model is verified by simulation results of spaceborne SAR. This method is quite simple to implement and can achieve efficient coverage of multiple dispersed targets, providing a basis for the implementation and application of SAR jamming in active radar responders.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

XAI-Driven Resilient Image Classification in the Presence of Adversarial Perturbations 存在对抗性扰动的xai驱动的弹性图像分类

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-06-04 DOI: 10.1049/rsn2.70041

Amir Hosein Oveis, Elisa Giusti, Alessandro Cantelli-Forti, Marco Martorella

{"title":"XAI-Driven Resilient Image Classification in the Presence of Adversarial Perturbations","authors":"Amir Hosein Oveis, Elisa Giusti, Alessandro Cantelli-Forti, Marco Martorella","doi":"10.1049/rsn2.70041","DOIUrl":"10.1049/rsn2.70041","url":null,"abstract":"Deep learning (DL) architectures, although being employed in widespread applications, often raise concerns about their trustworthiness due to their opacity in their decision-making processes. Explainable AI (XAI) emerges as a promising solution to mitigate these concerns by providing interpretable rationales for DL network outputs. In domains where risk tolerance is minimal, ensuring trustworthy predictions is essential. This study introduces expmax, a new classifier rooted in XAI principles, designed for multiclass classification problems using convolutional neural network (CNN) architectures. The key strength of expmax, compared to the conventional softmax, lies in its ability to evaluate the model's focus on salient features of targets rather than being distracted by unrelated patterns from the background. This characteristic allows expmax for increased resilience, especially in scenarios with adversarial samples, where conventional classifiers may fail to correctly recognise the target class. The methodology behind expmax is based on fitting a regressor with features that are extracted from the training dataset using the SHapley Additive exPlanations (SHAP) algorithm, along with a target mask area detection algorithm. By using the SHAP-based extracted features, expmax reduces vulnerabilities to perturbations introduced by adversarial inputs. The method is validated on the MTARSI dataset for aircraft recognition in remote sensing images.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Radar Anti-Jamming Performance Evaluation: A Novel Model Based on Measurement Error and RCS Distributions 雷达抗干扰性能评估：一种基于测量误差和RCS分布的新模型

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-05-30 DOI: 10.1049/rsn2.70038

Linqi Zhao, Liang Yan, Xiaojun Duan, Zhengming Wang, Yike Xiao

{"title":"Radar Anti-Jamming Performance Evaluation: A Novel Model Based on Measurement Error and RCS Distributions","authors":"Linqi Zhao, Liang Yan, Xiaojun Duan, Zhengming Wang, Yike Xiao","doi":"10.1049/rsn2.70038","DOIUrl":"10.1049/rsn2.70038","url":null,"abstract":"The growing complexity of the electromagnetic environment makes accurate radar anti-jamming performance evaluation essential for assessing the effectiveness of electronic countermeasure systems. In jamming scenarios, smaller measurement errors in range indicate a radar's enhanced ability to counteract interference. This paper presents a novel approach to radar anti-jamming performance evaluation based on statistical models of radar cross-section (RCS) fluctuations. Assuming that the RCS follows one of several distributions—Swerling I–II, Swerling III–IV, or Rayleigh—we derive the corresponding distributions of radar parameter measurement errors. In our model, the measurement error is assumed to follow a conditional Gaussian distribution, with its standard deviation modelled as a random variable dependent on both the RCS and the signal-to-interference ratio (SIR). This formulation establishes a quantitative relationship between measurement error, SIR, and RCS, and enables derivation of the error's probability density function (PDF). Consequently, we obtain a novel expression for the radar anti-jamming rate. We compare this model to two conventional approaches: one that assumes a constant error variance across all target ranges and another that assumes a fixed variance that varies with target ranges but without incorporating distributional uncertainty. The proposed Error Distribution Estimation (EDE) model leverages the full probability distribution of measurement errors together with real-time parameter error data fusion. This integration provides a more continuous and nuanced evaluation of radar anti-jamming performance, potentially leading to more reliable assessments under a range of operating conditions.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software-Defined Sonar for Unmanned Underwater System 无人水下系统软件定义声纳

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-05-27 DOI: 10.1049/rsn2.70037

Hongkun Zhou, Xiyun Ge, Ningyang Wei, Yuhang Gao, Xinyu Liu

引用次数: 0

Sparse Array Design Based on the Combination of Improved Binary Grey Wolf Optimisation and Genetic Algorithm 基于改进二值灰狼优化与遗传算法相结合的稀疏阵列设计

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-05-22 DOI: 10.1049/rsn2.70028

Weinian Li, Lichun Li, Hongyi Pan, Chaoyue Song, Siyao Tian

{"title":"Sparse Array Design Based on the Combination of Improved Binary Grey Wolf Optimisation and Genetic Algorithm","authors":"Weinian Li, Lichun Li, Hongyi Pan, Chaoyue Song, Siyao Tian","doi":"10.1049/rsn2.70028","DOIUrl":"10.1049/rsn2.70028","url":null,"abstract":"Traditional adaptive beamforming techniques focus solely on optimising the excitation weights of array elements while neglecting the critical influence of element positioning on beamforming performance. To enhance array degrees of freedom and achieve superior beamforming capabilities, this paper proposes a novel joint optimisation method that simultaneously adjusts both element positions and excitation coefficients, targeting maximum output signal-to-interference-plus-noise ratio (MaxSINR). Under the minimum variance distortionless response (MVDR) framework, we derive and analyse the theoretical relationship between output SINR and array configuration. We reformulate the sparse array design as a binary integer optimisation problem by introducing a position selection vector. The solution is efficiently obtained through our enhanced hybrid algorithm, which combines improved binary grey wolf optimisation with genetic algorithm (IBGWO-GA). Compared with the traditional beamforming method, the proposed algorithm can effectively improve the degree of freedom of the array position and realise interference suppression under underdetermined conditions. The optimal design of sparse linear array and sparse planar array in simulation experiments verifies the effectiveness of the proposed method.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study on the Influence of Space–Time Adaptive Processor on Single Point Position and Real-Time Kinematic for GNSS Antenna Array Anti-jamming Receiver 时空自适应处理器对GNSS天线阵抗干扰接收机单点位置和实时运动学影响的研究

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-05-21 DOI: 10.1049/rsn2.70035

Yaoding Wang, Xiaozhou Ye, Si Chen, Zhenxin Liu

{"title":"Study on the Influence of Space–Time Adaptive Processor on Single Point Position and Real-Time Kinematic for GNSS Antenna Array Anti-jamming Receiver","authors":"Yaoding Wang, Xiaozhou Ye, Si Chen, Zhenxin Liu","doi":"10.1049/rsn2.70035","DOIUrl":"10.1049/rsn2.70035","url":null,"abstract":"Space-time adaptive processor (STAP) has been widely used in the GNSS antenna array anti-jamming receiver. The cost of STAP algorithms, including the minimum variance distortion-less response (MVDR) algorithm and power inversion (PI) algorithm, is introducing measurement errors. However, there is no systematic answer to the principle of error introduction, the magnitude of error and its influence on single point position (SPP) and real-time kinematic (RTK). We have conducted a systematic study on the above-mentioned issues. Firstly, the principle of error introduction was theoretically studied. Then, a large number of simulations were conducted to evaluate the magnitude of the error. Finally, simulated errors are introduced into the B1I and B3I real measurements to implement SPP and RTK to evaluate the influence of the STAP algorithms on SPP and RTK. Results show that for SPP, the influence of STAP algorithms on the B1I + B3I dual-frequency ionosphere-free combination SPP is larger than that on the B1I single-frequency SPP; for RTK, the influence of STAP algorithms on the B1I + B3I dual-frequency uncombined RTK is smaller than that on the B1I single-frequency RTK. In addition, the influences of the MVDR algorithm on SPP and RTK are smaller than those of the PI algorithm.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Multi-Agent Reinforcement Learning Against Adversarial Attacks for Cooperative Self-Driving Vehicles 针对协作式自动驾驶车辆对抗攻击的鲁棒多智能体强化学习

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-05-19 DOI: 10.1049/rsn2.70033

Chuyao Wang, Ziwei Wang, Nabil Aouf

{"title":"Robust Multi-Agent Reinforcement Learning Against Adversarial Attacks for Cooperative Self-Driving Vehicles","authors":"Chuyao Wang, Ziwei Wang, Nabil Aouf","doi":"10.1049/rsn2.70033","DOIUrl":"10.1049/rsn2.70033","url":null,"abstract":"Multi-agent deep reinforcement learning (MARL) for self-driving vehicles aims to address the complex challenge of coordinating multiple autonomous agents in shared road environments. MARL creates a more stable system and improves vehicle performance in typical traffic scenarios compared to single-agent DRL systems. However, despite its sophisticated cooperative training, MARL remains vulnerable to unforeseen adversarial attacks. Perturbed observation states can lead one or more vehicles to make critical errors in decision-making, triggering chain reactions that often result in severe collisions and accidents. To ensure the safety and reliability of multi-agent autonomous driving systems, this paper proposes a robust constrained cooperative multi-agent reinforcement learning (R-CCMARL) algorithm for self-driving vehicles, enabling robust driving policy to handle strong and unpredictable adversarial attacks. Unlike most existing works, our R-CCMARL framework employs a universal policy for each agent, achieving a more practical, nontask-oriented driving agent for real-world applications. In this way, it enables us to integrate shared observations with Mean-Field theory to model interactions within the MARL system. A risk formulation and a risk estimation network are developed to minimise the defined long-term risks. To further enhance robustness, this risk estimator is then used to construct a constrained optimisation objective function with a regulariser to maximise long-term rewards in worst-case scenarios. Experiments conducted in the CARLA simulator in intersection scenarios demonstrate that our method remains robust against adversarial state perturbations while maintaining high performance, both with and without attacks.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0