Robust HRRP Recognition under Interrupted Sampling Repeater Jamming using a Prior Jamming Information-Guided Network

• URL: http://arxiv.org/abs/2511.23256v2
• Date: Thu, 04 Dec 2025 04:23:15 GMT
• Title: Robust HRRP Recognition under Interrupted Sampling Repeater Jamming using a Prior Jamming Information-Guided Network
• Abstract summary: We propose a robust HRRP recognition method guided by prior jamming information.<n>We show that our method consistently outperforms state-of-the-art approaches.
• Score: 16.2515496724034
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radar automatic target recognition (RATR) based on high-resolution range profile (HRRP) has attracted increasing attention due to its ability to capture fine-grained structural features. However, recognizing targets under electronic countermeasures (ECM), especially the mainstream interrupted-sampling repeater jamming (ISRJ), remains a significant challenge, as HRRPs often suffer from serious feature distortion. To address this, we propose a robust HRRP recognition method guided by prior jamming information. Specifically, we introduce a point spread function (PSF) as prior information to model the HRRP distortion induced by ISRJ. Based on this, we design a recognition network that leverages this prior through a prior-guided feature interaction module and a hybrid loss function to enhance the model's discriminative capability. With the aid of prior information, the model can learn invariant features within distorted HRRP under different jamming parameters. Both the simulated and measured-data experiments demonstrate that our method consistently outperforms state-of-the-art approaches and exhibits stronger generalization capabilities when facing unseen jamming parameters.

Related papers

• ProSR: Semantic-Prototype-Guided Discrete Modeling for Physically Consistent SAR Super-Resolution [28.150128853779268]
  High-resolution Synthetic Aperture Radar (SAR) imagery is critical for precision analysis as automatic target recognition, yet its acquisition is costly.<n>We propose Prototype-Guided Super-Resolution (ProSR) to reformulating SAR ISR as a semantically-guided discrete token prediction task.<n>ProSR achieves superior visual quality while preserving essential scattering characteristics required for practical SAR applications.
  arXiv  Detail & Related papers  (2026-09-02T09:50:06Z)
• DTI: Dynamic Trajectory Initialization for Generative Face Video Super-Resolution [12.049894329245404]
  We present Dynamic Trajectory Initialization (DTI) paradigm for Face Video Super-Resolution (FVSR)<n>With a novel enhancement-and-injection conditioning mechanism, fidelity of our model has been significantly improved without compromising perceptual quality.<n>Our method achieves a SOTA overall performance across diverse metrics and benchmarks.
  arXiv  Detail & Related papers  (2026-06-28T04:55:10Z)
• JointHRRP-Net: A Statistically Constrained Decoupling Network for Joint Target and Jamming Recognition in Composite Jamming [14.51679448895586]
  High-resolution range profile (HRRP)-based radar automatic target recognition suffers from severe performance degradation in jamming environments.<n>This paper proposes JointHRRP-Net, a unified framework for joint target-jamming recognition.
  arXiv  Detail & Related papers  (2026-05-19T08:58:31Z)
• WRF4CIR: Weight-Regularized Fine-Tuning Network for Composed Image Retrieval [58.85945491755373]
  Composed Image Retrieval (CIR) task aims to retrieve target images based on reference images and modification texts.<n>Current CIR methods rely on fine-tuning vision-language pre-trained models.<n>We introduce WRF4CIR, a Weight-Regularized Fine-tuning network for CIR.
  arXiv  Detail & Related papers  (2026-04-07T08:23:39Z)
• SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models [53.19726629537694]
  Post-training alignment of video generation models with human preferences is a critical goal.<n>Current data collection paradigms, reliant on in-prompt pairwise annotations, suffer from labeling noise.<n>We propose SoliReward, a systematic framework for video RM training.
  arXiv  Detail & Related papers  (2025-12-17T14:28:23Z)
• Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation [60.04281435591454]
  CRDA (Curriculum Reinforcement-Learning Data Augmentation) is a novel framework guiding detectors to progressively master multi-domain forgery features.<n>Central to our approach is integrating reinforcement learning and causal inference.<n>Our method significantly improves detector generalizability, outperforming SOTA methods across multiple cross-domain datasets.
  arXiv  Detail & Related papers  (2025-11-10T12:45:52Z)
• Rethinking Evaluation of Infrared Small Target Detection [105.59753496831739]
  This paper introduces a hybrid-level metric incorporating pixel- and target-level performance, proposing a systematic error analysis method, and emphasizing the importance of cross-dataset evaluation.<n>An open-source toolkit has be released to facilitate standardized benchmarking.
  arXiv  Detail & Related papers  (2025-09-21T02:45:07Z)
• Efficient Beam Selection for ISAC in Cell-Free Massive MIMO via Digital Twin-Assisted Deep Reinforcement Learning [37.540612510652174]
  We derive the distribution of joint target detection probabilities across multiple receiving APs under false alarm rate constraints.<n>We then formulate the beam selection procedure as a Markov decision process (MDP)<n>To eliminate the high costs and associated risks of real-time agent-environment interactions, we propose a novel digital twin (DT)-assisted offline DRL approach.
  arXiv  Detail & Related papers  (2025-06-23T12:17:57Z)
• On the Potential of Re-configurable Intelligent Surface (RIS)-assisted Physical Layer Authentication (PLA) [0.0]
  Re-configurable Intelligent Surfaces (RIS) technology is increasingly becoming a potential component for next-generation wireless networks. However, the broadcast nature of RIS-assisted wireless communication makes it vulnerable to malicious attacks at the physical layer. This paper investigates RIS-assisted wireless communication systems to unlock the potential of using RIS for physical layer authentication (PLA)
  arXiv  Detail & Related papers  (2024-05-01T10:17:24Z)
• InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
  Reward hacking, also termed reward overoptimization, remains a critical challenge. We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective. We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
  arXiv  Detail & Related papers  (2024-02-14T17:49:07Z)
• Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction [12.868218616042292]
  Modulated signals exhibit long temporal dependencies. Human experts analyze patterns in constellation diagrams to classify modulation schemes. Classical convolutional-based networks excel at extracting local features but struggle to capture global relationships.
  arXiv  Detail & Related papers  (2024-01-02T06:31:24Z)
• Background Debiased SAR Target Recognition via Causal Interventional Regularizer [13.49307175561975]
  We show that SAR images to be recognized include not only the foreground (i.e., the target) but also a certain size of the background area. This suggests that the presence of background impedes the ability of the DL-model to learn additional semantic information about the target. We propose a causal intervention based regularization method to eliminate the negative impact of background on feature semantic learning.
  arXiv  Detail & Related papers  (2023-08-30T02:56:55Z)
• ASR: Attention-alike Structural Re-parameterization [53.019657810468026]
  We propose a simple-yet-effective attention-alike structural re- parameterization (ASR) that allows us to achieve SRP for a given network while enjoying the effectiveness of the attention mechanism. In this paper, we conduct extensive experiments from a statistical perspective and discover an interesting phenomenon Stripe Observation, which reveals that channel attention values quickly approach some constant vectors during training.
  arXiv  Detail & Related papers  (2023-04-13T08:52:34Z)
• Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection [67.49587673594276]
  We introduce a new perceptron bias assumption that suggests discriminator models are more sensitive to certain features of the input, leading to the overconfidence problem. We demonstrate that the diffusion denoising process (DDP) of DMs serves as a novel form of asymmetric, which is well-suited to enhance the input and mitigate the overconfidence problem. Our experiments on CIFAR10, CIFAR100, and ImageNet show that our method outperforms SOTA approaches.
  arXiv  Detail & Related papers  (2022-11-21T08:45:08Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Variational Temporal Deep Generative Model for Radar HRRP Target Recognition [39.01318281591659]
  We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP) The proposed rGBN adopts a hierarchy of gamma distributions to build its temporal deep generative model. Experimental results on synthetic and measured HRRP data show that the proposed models are efficient in computation, have good classification accuracy and ability, and provide highly interpretable multi-stochastic-layer latent structure.
  arXiv  Detail & Related papers  (2020-09-28T02:03:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.