Iet Radar Sonar and Navigation最新文献

{"title":"Dual Frequency Side Scan Sonar Image Fusion for Deep-Learning Based Underwater Target Detection","authors":"Jiajun Xian,&nbsp;Liming Li,&nbsp;Fan Zhang,&nbsp;Yingying Wu,&nbsp;Weilin Zang,&nbsp;Lingji Xu,&nbsp;Hongxun Yao,&nbsp;Adam Rushworth,&nbsp;Johannes Michael Herrmann,&nbsp;Sanming Song","doi":"10.1049/rsn2.70122","DOIUrl":"https://doi.org/10.1049/rsn2.70122","url":null,"abstract":"<p>Side-scan sonar is essential for underwater observation and seabed target detection, yet traditional single-frequency systems must compromise between imaging resolution and detection range. For object detection tasks, high-frequency sonar yields finer details contributed by stronger scattering and higher contrast but suffers from limited coverage due to heavier attenuation, whereas low-frequency sonar covers wider areas yet offers poorer small-target visibility. To overcome these trade-offs and the challenges of strong speckle noise, target–shadow coupling and resolution variation, this study proposes a dual-frequency detection model named D²FNet (dual-domain fusion network). D²FNet integrates three key modules: (1) a union domain attention (UDA) module for preliminary dual-frequency fusion via ResNet-50 and a transformer encoder; (2) a cross-domain attention (CDA) module for enhanced feature interaction across frequency domains; and (3) a target–shadow pairing (TSP) module that embeds sonar imaging priors through local window attention to suppress false alarms and improve localisation confidence. Based on a newly constructed dual-frequency side-scan sonar dataset containing over 9000 paired images from sea trials, D²FNet significantly outperforms single-frequency and baseline fusion models in mAP metrics, demonstrating its effectiveness for high-precision underwater target detection.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147320758","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":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148070874","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":"Multi-Frame Coherent Integration for Detecting High-Speed Manoeuvring Target in Bistatic Space-Based Early Warning Radar","authors":"Xingjia Yang,&nbsp;Keqing Duan,&nbsp;Wei Shen,&nbsp;Di'ang Zhao","doi":"10.1049/rsn2.70119","DOIUrl":"https://doi.org/10.1049/rsn2.70119","url":null,"abstract":"<p>Long-time coherent integration of high-speed manoeuvring weak targets is critical for enhancing detection performance in bistatic space-based early warning radar systems. The signal-to-clutter-plus-noise ratio after single-frame coherent integration often remains insufficient for reliable detection, motivating the need for multi-frame processing. However, multi-frame long-time coherent integration is severely challenged by several factors: high-speed target manoeuvring induces range and Doppler cell migration (RCM/DCM), variations in pulse repetition frequencies across frames lead to phase inconsistencies and strong clutter environments complicate effective coherent processing. To overcome these challenges, this paper proposes a joint intra-frame and inter-frame integration framework. Firstly, space-time adaptive processing is applied for clutter suppression, followed by Keystone transform and matched filter processing to correct intra-frame RCM and DCM simultaneously. Subsequently, an adaptive phase estimation algorithm is proposed in which the initial phase of each frame is estimated from the dominant eigenvector of the covariance matrix formed by the echo data. Based on this, a phase-compensated generalised Radon transform technique is developed to realise coherent phase alignment and integration across frames. Simulation results show that the proposed method achieves near-optimal coherent gain and significantly improves the detection performance for weak targets.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147280091","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":"Open-Set Recognition of Radar One-Dimensional Range Profiles Based on Attention Mechanism and Multi-Task Joint Training","authors":"Bowen Bai,&nbsp;Long Xue,&nbsp;Mingyao Xue,&nbsp;Bailiang Pu","doi":"10.1049/rsn2.70121","DOIUrl":"https://doi.org/10.1049/rsn2.70121","url":null,"abstract":"<p>Radar automatic target recognition (RATR) based on high-resolution range profiles (HRRP) has recently attracted increasing attention. The closed-set assumption prevalent in HRRP research breaks down in the presence of unseen classes, and conventional open-set methods are limited to unknown rejection rather than identification. We propose an open-set recognition strategy based on multihead self-attention mechanisms and multitask collaborative sparse training (MHA–CoST) to address this challenge. MHA–CoST combines CNN-multihead attention feature extraction with sparsely linked classification and reconstruction heads, whilst employing distance-based postprocessing to eliminate samples beyond recognised classes and subsequently categorise the unknowns. This strategy enables the HRRP recognition system to not only identify known classes but also effectively exclude unknown categories whilst achieving finer discrimination among different unknown classes. The proposed method exhibits superior rejection and classification capabilities for unknown categories compared to conventional open-set recognition techniques, significantly improving recognition efficiency and accuracy.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147268902","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":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148070210","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":"Digital Self-Interference Cancelation Method Based on Multi-Domain Jamming Frequency-Shift Fingerprint Design","authors":"Jia Sun,&nbsp;Xizhang Wei,&nbsp;Jie Xiao","doi":"10.1049/rsn2.70108","DOIUrl":"https://doi.org/10.1049/rsn2.70108","url":null,"abstract":"<p>Simultaneous transmission and reception (STAR) jammers offer superior real-time performance over transceiver time-sharing jammers, while the self-interference (SI) represents a significant challenge for their implementation. To address this issue when SI is correlated with received signal, this paper proposes a digital self-interference cancelation (SIC) method based on multi-domain jamming (MDJ) frequency-shift fingerprint design from the signal characteristic level. This method utilises the frequency-shift fingerprint and the frequency offset introduced by the jammer's inherent forwarding delay to distinct SI and radar signals. Parameters influencing the frequency-domain characteristics of the signal, such as frequency-shift, propagation delay and signal bandwidth, are related to the configuration of the band-pass filter (BPF), thereby affecting the isolation performance of the proposed method. Since the fingerprint also impacts the jamming effect, it must be carefully designed to ensure the effectiveness of the radar jamming and SIC scheme. Simulation results demonstrate that the proposed method achieves superior cancellation performance compared to traditional adaptive filtering algorithms, effectively jams the radar and exhibits better performance in broadband scenarios.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147314943","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":"WFH: A Wideband Frequency Hopping-Based Anti-Jamming Navigation Signal Structure","authors":"Chenyang Wang,&nbsp;Yihai Liao,&nbsp;Sicong Liu,&nbsp;Jianghong Shi,&nbsp;Ao Peng","doi":"10.1049/rsn2.70116","DOIUrl":"https://doi.org/10.1049/rsn2.70116","url":null,"abstract":"<p>With the increasing prevalence of complex electromagnetic threats, the antijamming capability of satellite navigation signals has become a critical factor in signal design. However, existing signal designs still exhibit inherent limitations against varying jamming patterns, restricting their applicability in heavily jammed environments. This paper proposes a navigation signal structure based on wideband frequency hopping (WFH) modulation, aiming to enhance both antijamming capabilities and measurement accuracy. Since frequency hopping induces nonstationary characteristics in the received jamming spectrum, the conventional carrier-to-noise ratio (CNR) becomes inapplicable. To address this, we propose the nonstationary effective carrier-to-noise ratio (NSCNR) model to characterise performance under dynamic spectral conditions. In addition, the impacts of various hopping parameters, specifically ionospheric effects, synchronisation errors and dwell time, on signal accuracy and antijamming performance are thoroughly analysed. Simulation results demonstrate that wideband frequency hopping provides robust resilience against both narrowband and wideband jamming.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146083423","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":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148083852","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":"Adaptive High Manoeuvring Target Tracking Algorithm Based on CNN-LSTM Fusion Architecture","authors":"Yuhan Cui,&nbsp;Chunbo Xiu,&nbsp;Yuxia Liu,&nbsp;Dawei Liu","doi":"10.1049/rsn2.70115","DOIUrl":"https://doi.org/10.1049/rsn2.70115","url":null,"abstract":"<p>High manoeuvring target tracking remains a challenging problem due to complex motion patterns, frequent model switching and strong nonlinear relationships between system states and observations. The interacting multiple model (IMM) algorithm integrated with the unscented Kalman filter (UKF) is widely used for such scenarios. However, its performance is significantly limited by reliance on a fixed or heuristically designed transition probability matrix (TPM), which leads to model switching lag and degradation in tracking accuracy during abrupt manoeuvres. Moreover, existing deep learning-assisted IMM methods often fail to effectively fuse spatiotemporal features and suppress noise. To solve these gaps, an adaptive interacting multiple model unscented Kalman filter (IMM-UKF) algorithm based on a convolutional neural network and long short-term memory network (CNN-LSTM) fusion architecture is proposed. A multi-dimensional feature space including state estimation, observation data and model probability is constructed by the algorithm, which is then used as input to the neural network model. Latent spatial features are extracted by the CNN module and subsequently processed by the LSTM network to capture temporal dynamic characteristics, which achieve real-time dynamic estimation and adaptive optimisation of the model TPM. In addition, a sliding average buffer mechanism is introduced to smooth the prediction outputs and reduce the impact of disturbances on estimation performance. Simulation results show that the proposed algorithm outperforms the IMM-UKF algorithm. In the periodic motion scenario, the proposed algorithm reduces position and velocity root mean square error (RMSE) by 11.2% and 19.96%, respectively. In the compound manoeuvring motion scenario, the proposed algorithm reduces the position and velocity RMSE by 14.87% and 21.50%, respectively. The proposed algorithm effectively improves model switching accuracy, increases the probability of matching the dominant model and significantly enhances tracking performance under high-manoeuvrability conditions.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146091068","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":"Loop Matching Pursuit Multistation TDOA Estimation Method Based on Noncircular Signal","authors":"Zeyu Wang,&nbsp;Ding Wang,&nbsp;Jiexin Yin,&nbsp;Nae Zheng","doi":"10.1049/rsn2.70114","DOIUrl":"https://doi.org/10.1049/rsn2.70114","url":null,"abstract":"<p>Time difference of arrival (TDOA) estimation is a critical parameter estimation stage of TDOA localisation in wireless monitoring networks, and its result directly determines the positioning accuracy of the signal source. However, the existing TDOA estimation methods based on orthogonal matching pursuit (OMP) only consider the scenario of circular signal and are prone to the degradation of estimation performance due to atom misselection under small samples and low signal-to-noise ratio (SNR). To address this issue, this paper proposes a loop matching pursuit multistation TDOA estimation algorithm based on noncircular signal. Firstly, by combining the noncircular characteristic of the radiation source signal, an extended data model is constructed by using multistation received data and their conjugate. Then, based on the time-domain sparsity of the time difference, an extended time-domain dictionary set is built by discretising the time grid. In addition, the support set is optimised via atom loop deletion and addition to reconstruct the sparse coefficients. Finally, the TDOA estimation is obtained from the correspondence between the time difference value and the sparse coefficient. Furthermore, the Cramer–Rao bound (CRB) for TDOA estimation of a noncircular source is derived, thereby providing a quantitative theoretical lower bound for the estimation accuracy of the new algorithm. The simulation experiment results verify the superiority of the proposed algorithm under small samples and low SNR.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146099387","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}