Iet Radar Sonar and Navigation最新文献

{"title":"High-Fidelity 3D Reconstruction From Sparse Multi-Baseline CSAR Data via Conditional Diffusion Models","authors":"Xingyu Zhao,&nbsp;Yanheng Ma,&nbsp;Bingxuan Li,&nbsp;Lina Chu,&nbsp;Wei Li","doi":"10.1049/rsn2.70118","DOIUrl":"https://doi.org/10.1049/rsn2.70118","url":null,"abstract":"<p>Three-dimensional (3D) reconstruction from sparse multibaseline circular synthetic aperture radar (CSAR) data are a significant challenge, often plagued by low vertical resolution, geometric blurring and severe artefacts due to limited observation angles. To address this, this paper introduces a 3D reconstruction framework based on a conditional diffusion model. The proposed method utilises a dual-path 3D U-Net, where a condition encoder extracts geometric features from a coarse initial reconstruction. These features guide the progressive denoising process via cross-attention, integrating physical constraints with data-driven generation. Furthermore, the framework incorporates a novel cross-view consistency loss to enforce geometric coherence across sparse views, enhancing structural integrity. Experiments demonstrate superior performance in quantitative metrics (IoU and BCE) and visual quality, successfully restoring complete and detailed 3D structures.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147626388","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}

{"title":"Tracking Loop Distortion of BDS-3 B1C Signal in Spoofing Scenarios: An Experimental Analysis","authors":"Bo Jiao,&nbsp;Yue Wang,&nbsp;Weiping Liu,&nbsp;Lan Du,&nbsp;Lundong Zhang","doi":"10.1049/rsn2.70134","DOIUrl":"https://doi.org/10.1049/rsn2.70134","url":null,"abstract":"<p>An experimental analysis method for the correlation peak distortion of the BeiDou-3 satellite navigation system (BDS-3) B1C signal is proposed to address the gaps in existing research—lack of experimental validation and insufficient insights into tracking loop distortion mechanisms under spoofing. First, the autocorrelation characteristics of the B1C signal under narrow band (NB) and wide band (WB) tracking modes were analysed based on its modulation characteristics. A feeder-based BDS spoofing experimental platform was constructed to accurately simulate real-world spoofing scenarios and collect high-fidelity intermediate frequency (IF) data. Multi-correlator tracking outputs were utilised to quantify the distortion effects of spoofing types and tracking modes on the B1C tracking loop. Experimental results revealed three key findings: (1) during spoofing takeover, the combined correlation peak oscillates at high frequency (spoofing as reference, residual true signal as amplitude); (2) under position spoofing, correlation peak pulling direction/rate vary by satellite; under time spoofing, the pulling is consistent across satellites; (3) WB tracking offers higher ranging accuracy than NB tracking but is more susceptible to spoofing-induced pulling and exhibits a more complex correlation peak distortion. These results provide a theoretical and experimental basis for the design of BDS-3 anti-spoofing algorithms and equipment optimisation.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653375","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}

{"title":"Adaptive Multi-Feature Validation Algorithm for Hovering UAV Detection via LFMCW Radar: Leveraging Micro-Doppler Spectral Characteristics","authors":"Tao Chen,&nbsp;Jianbo Zhao,&nbsp;Lutao Liu","doi":"10.1049/rsn2.70112","DOIUrl":"https://doi.org/10.1049/rsn2.70112","url":null,"abstract":"<p>With the widespread use of UAVs, timely detection of non-compliant ones is critical for security. Radar excels in long-range all-weather UAV detection but faces high false alarms in cluttered environments—especially for hovering rotor UAVs, whose minimal Doppler shifts are easily masked by ground clutter. To solve this, this paper proposes the micro-Doppler spectral feature-guided LFMCW radar detection algorithm, a micro-Doppler spectral feature-guided LFMCW radar algorithm for such UAVs. Its core innovation is reconstructing signal modelling to link rotor micro-motion with radar echo spectra, plus multi-step decision logic: extracting range-Doppler (RD) maps from echoes, identifying potential targets via zero-frequency-centred peaks and verifying via three micro-Doppler features (uniform line spacing, bounded width and stable amplitude variation). Validated with a Ku-band LFMCW radar prototype, simulations show <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≥</mo>\u0000 <mn>40</mn>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> ${ge} 40%$</annotation>\u0000 </semantics></math> lower false alarms than traditional threshold methods at 30 dB SCR. Field tests in tree-cluttered environments confirm detection of micro-UAVs (RCS = <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>0.01</mn>\u0000 <mspace></mspace>\u0000 <msup>\u0000 <mi>m</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> $0.01,{mathrm{m}}^{2}$</annotation>\u0000 </semantics></math>) within 2 km, with distance resoslution <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≤</mo>\u0000 <mn>5</mn>\u0000 </mrow>\u0000 <annotation> ${le} 5$</annotation>\u0000 </semantics></math> m and speed resolution <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>≤</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <annotation> ${le} 1$</annotation>\u0000 </semantics></math> m/s, proving the algorithm's robustness and practicality.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653238","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}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Micro-Doppler-Assisted Particle Filtering for Tracking of Doppler-Blind-Zone Targets With Passive Radar","authors":"Chenyu Wen,&nbsp;Zhihai Zhuo,&nbsp;Shengheng Liu,&nbsp;Weihao Yan,&nbsp;Tianrun Wang,&nbsp;Yingshi Liu,&nbsp;Zhi Wang,&nbsp;Yuan Feng,&nbsp;Tao Shan","doi":"10.1049/rsn2.70117","DOIUrl":"https://doi.org/10.1049/rsn2.70117","url":null,"abstract":"<p>Robust detection and tracking of unmanned aerial vehicles (UAVs) in passive radar systems remains challenging when targets enter the Doppler-blind-zone (DBZ), where severe energy attenuation and ground clutter contamination cause model mismatch and degrade tracking performance. To address this issue, this study proposes a micro-Doppler-assisted particle filtering track-before-detect (mD-PF-TBD) algorithm. Firstly, within a bistatic geometry based on digital terrestrial multimedia broadcast (DTMB) signals, a detailed mD motion model of multi-rotor UAVs is established, and an observation model incorporating mD signatures is derived. To mitigate the model mismatch-induced particle weight degradation, a variable-particle strategy is introduced, which adaptively redistributes particles according to variations in Doppler frequency. As the target Doppler frequency approaches zero, the algorithm allocates more particles to the mD harmonic side peaks, which remain detectable outside the DBZ, thereby maintaining robust tracking. The proposed method is validated through both simulations and field experiments. Monte Carlo simulations demonstrate that the variable-particle strategy significantly enhances tracking performance for DBZ targets. Field experiments using DJI M300 RTK and M600 UAVs confirm that the proposed algorithm maintains stable tracking and accurate state estimation, even when the target remains in the DBZ for an extended duration.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653206","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}

{"title":"Sig2text: A Vision-Language Model for Non-Cooperative Radar Signal Parsing","authors":"Jun-Yu Yuan,&nbsp;Si-Yuan Chen,&nbsp;Shu-Jian Yao,&nbsp;Rong Zhang,&nbsp;Han Cong Feng,&nbsp;Kai-li Jiang,&nbsp;Yu-Xin Shang,&nbsp;Yu-Xin Zhao,&nbsp;Bin Tang","doi":"10.1049/rsn2.70113","DOIUrl":"https://doi.org/10.1049/rsn2.70113","url":null,"abstract":"<p>Automatic noncooperative analysis of intercepted radar signals is essential for intelligent equipment in both military and civilian domains. Accurate modulation identification and parameter estimation enable effective signal classification, threat assessment and the development of countermeasures. In this paper, we propose a symbolic approach for radar signal recognition and parameter estimation based on a vision-language model that combines context-free grammar with time-frequency representation of radar waveforms. The proposed model, called Sig2text, leverages the power of vision transformers for time-frequency feature extraction and transformer-based decoders for symbolic parsing of radar waveforms. By treating radar signal recognition as a parsing problem, Sig2text can recognise the types and estimate the parameters of signals with arbitrarily complex modulations. We evaluate the performance of Sig2text on a synthetic radar signal dataset and demonstrate its effectiveness in various scenarios.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147626356","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}

{"title":"A Space-Time-Frequency Adaptive Processing Method for Dual-Band Airborne Radar in Complex Electromagnetic Environments","authors":"Ming Hou,&nbsp;Wenchong Xie,&nbsp;Wei Chen,&nbsp;Yuanyi Xiong,&nbsp;Chenran Gao,&nbsp;Anle Wang,&nbsp;Yongliang Wang","doi":"10.1049/rsn2.70132","DOIUrl":"https://doi.org/10.1049/rsn2.70132","url":null,"abstract":"<p>With the development of modern electronic warfare technology, the electromagnetic environment faced by airborne radars has become increasingly complex. In this context, how to eliminate the impact of jamming and detect weak moving targets such as stealth aircraft in a timely and accurate manner has become a technical challenge that needs to be urgently addressed. Additionally, the rapid development of microwave photonic technology has made it possible to generate dual-band wideband signals. In this paper, we first establish the dual-band airborne radar wideband echo signal model and propose a space-time-frequency adaptive processing method based on it. Firstly, in the passive mode, the poles of the dual band are estimated by the matrix pencil method, and the incoherent components are estimated through the phase relationship between the poles. Secondly, in the active mode, the wideband linear frequency modulation signal is divided into multiple subpulses for pulse compression processing. Thirdly, based on the time-frequency analysis results, the frequency bands covered by suppressive jamming are removed by frequency point elimination. Fourthly, the incoherent components and the frequency-dependent factor compensation, as well as the phase compensation processing, are carried out. Finally, the carrier frequency-domain coherent accumulation and space-time adaptive processing are performed on the compensated data. Simulation results show that the proposed method can significantly improve the anti-mainlobe suppressive jamming and weak moving target detection performance of airborne radars in complex electromagnetic environments.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631849","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}

{"title":"Design and Optimization of Finite Alphabet Waveforms for Multiple-Antenna Radar Covariance Matching","authors":"Karim Saifullin,&nbsp;Sajid Ahmed,&nbsp;Mohamed-Slim Alouini","doi":"10.1049/rsn2.70125","DOIUrl":"https://doi.org/10.1049/rsn2.70125","url":null,"abstract":"<p>The beampattern of an MIMO radar depends on the covariance matrix of its transmitted waveforms. Although this matrix can be easily realized using waveforms with large alphabets and a high peak-to-average power ratio (PAPR), such waveforms require significant storage and are difficult to transmit within the limited linear range of power amplifiers. Designing finite-alphabet waveforms with unity PAPR, such as BPSK, is therefore a challenging yet important task, as they reduce storage requirements and satisfy hardware constraints. Although extensive research has addressed this problem, existing approaches lack theoretical insight into performance limits, and the resulting solutions are often suboptimal. This work introduces a novel formulation for binary waveform design based on constrained matrix decomposition that outperforms existing methods. The proposed approach exploits a recently introduced property of beampattern-invariant covariance matrices, which provides additional degrees of freedom and enables faster convergence. From a theoretical perspective, we investigate the feasibility of generating a given covariance matrix using binary waveforms. We prove that the covariance matrix of an autoregressive process is always feasible, and we propose methods to assess feasibility in general. For infeasible cases, we introduce a beampattern adjustment procedure. Finally, we demonstrate the advantages of binary waveforms through several application scenarios.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653165","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}

{"title":"A Cooperative Adaptive Kalman Filter for Robust Terrain-Referenced Navigation in Complex Environments","authors":"Liyue Liang,&nbsp;Daojie Yu,&nbsp;Jianping Du,&nbsp;Yijie Bai,&nbsp;Tao Li,&nbsp;Jiale Zhou","doi":"10.1049/rsn2.70101","DOIUrl":"https://doi.org/10.1049/rsn2.70101","url":null,"abstract":"<p>Accurate and reliable navigation for unmanned aerial vehicles (UAVs) in GNSS-denied environments remains a significant challenge. Conventional terrain-aided navigation (TAN) systems based on extended Kalman filters (EKFs) are prone to divergence due to their reliance on static noise models, leading to spurious lock syndrome and filter failure. Although conventional adaptive Kalman filters (AKFs) attempt to address this, many are reactive and struggle to handle the abrupt transient nature of TAN challenges. This paper proposes a cooperative adaptive Kalman filter (CAKF) that enhances robustness by dynamically aligning the uncertainty representation of the filter with real-world conditions. The CAKF integrates three adaptive mechanisms: scene-ware measurement noise adaptation, manoeuvre-aware process noise adaptation, and health-monitored state covariance intervention. These components cooperate to prevent covariance collapse and maintain filter consistency under varying terrain and dynamic conditions. Simulations in challenging scenarios, including feature-sparse plains, aggressive manoeuvres, and prolonged measurement outages, demonstrate that the CAKF achieves stable and accurate navigation, whereas the Sage-Husa EKF (SHAEKF), a representative innovation-based adaptive filter, suffers from significant errors or divergence. The results validate the effectiveness of the cooperative adaptation strategy in complex TAN applications.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653166","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}

{"title":"A Study of Standalone GNSS Positioning Precision in Covered Agricultural Environments: ETFE Polytunnels Versus Glass Greenhouses","authors":"Alex Schofield,&nbsp;Jasmine Zidan,&nbsp;Raphael Grech,&nbsp;Esther Anyaegbu,&nbsp;Emilio Loo-Monardez,&nbsp;Paul A. Jennings,&nbsp;Matthew D. Higgins","doi":"10.1049/rsn2.70130","DOIUrl":"https://doi.org/10.1049/rsn2.70130","url":null,"abstract":"<p>This paper presents a methodology to investigate the standalone performance of GNSS receivers in controlled covered agricultural environments (CAEs) and to derive implications for resilient PNT. Two GNSS receivers, a high-end survey-grade system and a mass-market system were evaluated focussing on their positioning precision (i.e., the dispersion/sigma around a mean-centred reference) within Ethylene tetrafluoroethylene (ETFE) covered polytunnels and Cambridge-style glass greenhouses relative to an open-field baseline. This study provides the first systematic evaluation of standalone GNSS performance in CAEs under controlled conditions assessing its feasibility for precise positioning without augmentation or correction services. Static positioning tests over 2-hour sessions in each environment are complemented by RF propagation measurements to characterise the attenuation properties of ETFE and greenhouse glass. All datasets are made openly available to support reproducibility and future PNT algorithm development. These findings indicate that polytunnels are more suitable for precision agriculture applications relying on standalone GNSS, offering robust and reliable decimetre level positioning. In contrast, glass greenhouses require augmentation or sensor fusion for precision-critical tasks. The insights and open dataset provide a quantitative basis for agritech system designers and PNT researchers to select appropriate environments and to design resilient PNT architectures for CAEs.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147631863","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}