Iet Radar Sonar and Navigation最新文献

{"title":"TriMSAN: A Triple-Path Multi-Modal Self-Attention Network for Tightly Coupled GNSS/INS Integration Under GNSS Outages","authors":"Boyuan Liu,&nbsp;Rui Xue,&nbsp;Xinman Wang,&nbsp;Xingzi Qiang","doi":"10.1049/rsn2.70259","DOIUrl":"https://doi.org/10.1049/rsn2.70259","url":null,"abstract":"<p>The GNSS/INS integrated navigation framework is widely used in urban environments. In obstructed environments, GNSS outages force the system to rely solely on INS and cause rapid error growth. Most learning-based methods predict navigation states rather than the satellite-wise observations required by tightly coupled filtering. This paper proposes TriMSAN, a per-satellite observation-domain framework that predicts pseudorange and pseudorange-rate variations from IMU/INS motion features, ephemeris-derived satellite kinematics and historical GNSS observations. Branch-wise transformer encoders, cross-modal interaction and modality-attention fusion are used to extract and fuse these features. The predicted variations are recursively accumulated to reconstruct satellite-wise pseudo-measurements and injected into the original tightly coupled filter. In four 60 s simulated outages, TriMSAN achieved an overall three-dimensional positioning RMSE of 5.051 m and lower pseudorange and pseudorange-rate prediction errors than MHSA. On the GREAT Dataset, cross-trajectory evaluation using two injected 90 s complete outages yielded an overall horizontal positioning RMSE of 15.506 m for TriMSAN, compared with 23.253 m for MHSA and 123.358 m for INS propagation. Controlled partial and time-varying satellite-availability experiments further evaluated the joint use of reconstructed and retained real observations. These results support per-satellite multi-modal observation-domain prediction for tightly coupled GNSS/INS integration under the evaluated GNSS outage conditions.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149259482","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":"Interference-Mitigated Doppler Frequency Estimation for Broadband Incoherent ADCPs in Ocean Current Profiling","authors":"Xiang Li,&nbsp;Ke Zhang,&nbsp;Xuesong Li,&nbsp;Qilin Wu,&nbsp;Yunfeng Tang,&nbsp;Congcong Ma,&nbsp;Hanhao Zhu","doi":"10.1049/rsn2.70238","DOIUrl":"https://doi.org/10.1049/rsn2.70238","url":null,"abstract":"<p>Acoustic Doppler current profilers (ADCPs) are important acoustic remote sensing instruments for measuring ocean current velocity profiles in marine observation systems. This paper addresses the degradation of real-time velocity measurement performance in broadband incoherent ADCPs used for ocean current profiling under strong interference. The pulse-pair method is analytically interpreted as a first-order spectral moment estimator of the observed Doppler spectrum. A point-scattering-based volume echo model is established, and the impact of strong interference on Doppler spectral structure is analysed, revealing the generation of a bimodal spectrum. To mitigate this issue, a method based on spectral integration is proposed to identify interference components and extract valid spectral regions for Doppler frequency estimation. Based on the filtered spectrum, an interference-mitigation Doppler frequency estimation algorithm is developed. Simulation results show that, compared with the conventional pulse-pair method, which suffers from severe bias and up to 200% variance degradation under strong interference, the proposed method effectively mitigates interference effects, maintains near-unbiased mean performance and limits variance degradation to approximately 30%. These improvements enhance the robustness and reliability of Doppler-based ocean current measurements and demonstrate the potential of the proposed method for acoustic remote sensing applications in challenging marine environments.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149259211","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 Forgetting Factor Probabilistic Data Association Filtering for Radar Target Tracking in Cluttered Environments With Unknown and Varying Measurement Noise Covariance","authors":"Shaoying Su,&nbsp;Mingfeng Shi,&nbsp;Meilin He,&nbsp;Zhirui Hu,&nbsp;Wei Feng","doi":"10.1049/rsn2.70250","DOIUrl":"https://doi.org/10.1049/rsn2.70250","url":null,"abstract":"<p>An adaptive forgetting-factor probabilistic data association filter, termed AR-<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>λ</mi>\u0000 </mrow>\u0000 <annotation> $lambda $</annotation>\u0000 </semantics></math>PDAF, is proposed for radar target tracking in cluttered environments with unknown and varying measurement noise covariance. In conventional PDAF, inaccurate measurement noise covariance may lead to mismatched validation gating, association probability calculation and state correction. To address this problem, an exponentially weighted moving average estimator is embedded into the PDAF recursion to estimate the measurement noise covariance online from the combined innovation sequence. Moreover, the forgetting factor is adaptively regulated according to the Mahalanobis distance of the combined innovation. When a significant statistical deviation is detected, a smaller forgetting factor is assigned to improve the response to current measurements; otherwise, a larger forgetting factor is used to retain historical information and suppress clutter-induced fluctuations. The design criterion of the adaptive forgetting factor is established, and the approximate steady-state unbiasedness of the proposed estimator is analysed. Monte Carlo simulations under constant velocity and coordinated turn models show that, in the present experiments, AR-<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>λ</mi>\u0000 </mrow>\u0000 <annotation> $lambda $</annotation>\u0000 </semantics></math>PDAF generally achieves better tracking accuracy than AR-PDAF and fixed-forgetting-factor EWMA-PDAF, with more pronounced advantages under abrupt covariance changes.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205448","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":"AI-Driven Intelligent and Explainable SONAR for Underwater Acoustic Sensing","authors":"Shumaila Javaid,&nbsp;Naveed Khan,&nbsp;Akhlaqur Rahman,&nbsp;Nasir Saeed","doi":"10.1049/rsn2.70249","DOIUrl":"https://doi.org/10.1049/rsn2.70249","url":null,"abstract":"<p>Underwater acoustic signals are central to underwater sensing and communication, offering superior long-range propagation compared to electromagnetic or optical waves. Sound navigation and ranging (SONAR) systems exploit these signals for target detection, classification, localisation and tracking, with low-frequency analysis and recording (LOFAR) spectra serving as a primary tool for analysing tonal and broadband underwater signatures. However, the complex marine environment introduces significant challenges, such as noise, clutter, multipath propagation and interference that degrade LOFAR spectral quality and limit detection reliability. This review provides a comprehensive examination of signal processing and AI-based techniques used to enhance LOFAR spectrum analysis, including sparse representations, convolutional neural networks and emerging transformer architectures. In addition, the paper examines recent progress towards explainable and intelligent SONAR systems, emphasising the potential of large language models (LLMs) as high-level reasoning engines for semantic interpretation, operator assistance, federated multi-autonomous underwater vehicles (AUV) collaboration and automated dataset documentation. By integrating developments across underwater acoustics, machine learning and language-centric intelligence, this work synthesises current capabilities, outlines persistent limitations and highlights future research opportunities for building robust, interpretable and autonomous underwater acoustic detection systems.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146021","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":"Sky–Ground Wave Time Delay Estimation in eLoran Based on Two-Stage MS-CNN","authors":"Lili Zhou,&nbsp;Jinjing Wang,&nbsp;Zhonglin Mu,&nbsp;Xinyue Hu,&nbsp;Junyou Li","doi":"10.1049/rsn2.70237","DOIUrl":"https://doi.org/10.1049/rsn2.70237","url":null,"abstract":"<p>The working accuracy of the eLoran system is susceptible to the interference sky–ground wave multipath aliasing. Therefore, the accurate estimation of the sky–ground wave relative delay plays an important role in suppressing skywave interference. To address the performance degradation of existing methods under low-SNR or small-delay-interval conditions, this paper constructs a delay estimation method that combines IFFT preprocessing with two-stage MS-CNN. The IFFT preprocessing is used to enhance the time-domain features related to delay. The two-stage MS-CNN achieves accurate estimation of the groundwave, skywave and their relative delay through multi-scale convolution, channel attention and local residual correction. Simulation results show that the proposed algorithm can accurately estimate the delay over an SNR range of −5 to 20 dB, and the estimation error can be reduced to as low as 0.021 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>μ</mi>\u0000 <mi>s</mi>\u0000 </mrow>\u0000 <annotation> $mu s$</annotation>\u0000 </semantics></math> under high-SNR conditions. The results of multiple comparative experiments show that the proposed method maintains high delay estimation accuracy under different SNR conditions, and exhibits better stability especially in low-SNR and small-delay-interval scenarios, further verifying its applicability and robustness under complex conditions.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146815","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":"Joint Localisation of Line-of-Sight and Non-Line-of-Sight Targets via Deep Learning-Assisted Millimetre-Wave Radar","authors":"Shuohan Su,&nbsp;Pengcheng Gong,&nbsp;Qixing Li,&nbsp;Jian Liu,&nbsp;Yuanjun Zhao,&nbsp;Xiangjin Zeng,&nbsp;Yuntao Wu","doi":"10.1049/rsn2.70242","DOIUrl":"https://doi.org/10.1049/rsn2.70242","url":null,"abstract":"<p>Joint localisation of line-of-sight (LOS) and nonline-of-sight (NLOS) targets using millimetre-wave radar is challenging in urban occluded environments due to complex multipath propagation and the large energy imbalance between strong LOS echoes and weak NLOS returns. This paper proposes a joint LOS/NLOS localisation framework for typical urban T-shaped scenes by combining multidimensional parameter estimation, region-aware sparse reconstruction and deep-unfolded iterative optimisation. The scene is first geometrically modelled and partitioned into LOS and NLOS regions. Target localisation is then formulated as a sparse recovery problem in the range-angle-Doppler domain, where a region-weighted strategy is introduced to enhance weak NLOS target responses. Based on this formulation, two network-enhanced iterative methods are developed to learn adaptive regularisation and parameter update strategies from data. Both methods retain the interpretability of conventional iterative reconstruction whilst improving robustness under low-SNR and mixed LOS/NLOS conditions. Simulation and real-data experiments show that the proposed methods outperform conventional iterative algorithms in reconstruction accuracy, false-alarm suppression and sidelobe/artefact reduction, achieving robust super-resolution localisation in complex urban occluded scenes.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149047935","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":"Multi-Hypothesis Grid-Based Detection Network for Small and Weak Targets in Range-Doppler Spectrum Images","authors":"Mingjie Qiu,&nbsp;Jianming Wang,&nbsp;Guangxin Wu","doi":"10.1049/rsn2.70220","DOIUrl":"https://doi.org/10.1049/rsn2.70220","url":null,"abstract":"<p>To address the limitations of traditional constant false alarm rate (CFAR) algorithms in detecting small and weak unmanned aerial vehicle (UAV) targets under low signal-to-noise ratio (SNR) conditions, as well as the poor real-time performance of existing deep learning-based sliding window detection algorithms, this paper proposes a multi-hypothesis grid-based detection network (MHGDNet). The proposed network adopts a global grid-based detection framework, incorporating a dual-branch multi-scale feature extraction module and nine parallel multi-grid detection heads. A composite loss function is constructed, along with a clutter-aware training augmentation strategy and a tailored non-maximum suppression (NMS) method. Experimental results on both simulated and measured data demonstrate that the proposed method achieves a detection probability of 95.5% at SNR = −20 dB in simulated data, and 87.7% in real-world scenarios, with a processing time of only 39 ms per detection. The comprehensive performance significantly outperforms traditional CFAR and mainstream YOLO-based algorithms, offering an effective solution for real-time high-precision detection of low-altitude, slow-speed, small targets in radar applications.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942743","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":"Dual-Polar Reconstruction From Single-Polarisation Weather Radar Using Generative Autoencoders","authors":"Mohsen Firoozmand,&nbsp;Farbod Razzazi,&nbsp;Maryam Rajabzadeh Asaar","doi":"10.1049/rsn2.70129","DOIUrl":"https://doi.org/10.1049/rsn2.70129","url":null,"abstract":"&lt;p&gt;Single-polarisation weather radars rely exclusively on reflectivity (&lt;i&gt;Z&lt;/i&gt;), which allows for precipitation detection and intensity estimation but lacks the capability for reliable hydrometeor classification. Key polarimetric parameters—such as differential reflectivity &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Z&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $left({Z}_{DR}right)$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and co-polar correlation coefficient &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;ρ&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;H&lt;/mi&gt;\u0000 &lt;mi&gt;V&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $left({rho }_{HV}right)$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;—are inherently unmeasurable because of hardware constraints. Although polarimetric radars offer superior atmospheric insights, their high cost and complexity have limited their operational deployment. This study addresses a critical gap in radar meteorology by proposing a deep learning framework that emulates dual-polarisation features from single-polarisation data. Specifically, a generative autoencoder based on a U-Net architecture is designed to synthesise &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Z&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${Z}_{DR}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;ρ&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;H&lt;/mi&gt;\u0000 &lt;mi&gt;V&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${rho }_{HV}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; from &lt;i&gt;Z&lt;/i&gt;-only inputs. Unlike prior approaches focused solely on classification tasks, this model enables synthetic reconstruction of polarimetric fields, bridging the diagnostic divide between low-cost and advanced radar systems. Experimental results demonstrate substantial improvements in precipitation type classification, hydrometeor siz","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848642","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":"VFH Obstacle Avoidance Using Single-Beam Acoustic Ranging for a Multi-Thruster AUV in Unknown Environments","authors":"Xizhao Du,&nbsp;Xinyu An,&nbsp;Congcong Ma,&nbsp;Xiaochuan Qian,&nbsp;Na Liao,&nbsp;Xiyuan Wei,&nbsp;Baoquan Hu","doi":"10.1049/rsn2.70214","DOIUrl":"https://doi.org/10.1049/rsn2.70214","url":null,"abstract":"<p>Deep-sea operations impose demanding requirements on the flexibility and autonomy of underwater vehicles. Unlike traditional torpedo-shaped AUVs, the disk-shaped, multi-thruster Autonomous Underwater Vehicle (AUV) demonstrates enhanced agility, which is ideal for navigating uncharted and complex underwater terrains. This paper proposes a dedicated obstacle avoidance strategy tailored to the multi-thruster AUV's exceptional horizontal manoeuverability and fixed-point multi-directional distance measurement capability. Upon obstacle detection, the vehicle adjusts the thrust difference of its horizontal thrusters to achieve in-place rotation across different headings, enabling continuous collection of obstacle distance data via single-beam acoustic ranging. The collected data is then input into an optimised Vector Field Histogram (VFH) algorithm to generate the optimal real-time heading for obstacle avoidance. This integration of fixed-point multi-directional sensing and algorithm-adaptive decision-making enhances the vehicle's obstacle avoidance robustness and adaptability. Comprehensive simulations are conducted in MATLAB and on the MOOS-IvP autonomous marine vehicle platform under diverse uncharted underwater environments to validate the strategy. Simulation results confirm the feasibility and reliability of the proposed obstacle avoidance strategy and algorithm.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752997","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":"Long-Time Coherent Integration Enhancement of Moving Target Line Spectrum Based on Matched Frequency-Trajectory Estimation","authors":"Yushu Zhang,&nbsp;Zhenxing Zhao,&nbsp;Lin Zhai,&nbsp;Bo Gao,&nbsp;Dazhi Gao,&nbsp;Wei Gao","doi":"10.1049/rsn2.70216","DOIUrl":"https://doi.org/10.1049/rsn2.70216","url":null,"abstract":"<p>Line spectra radiated by moving targets are important features for passive sonar detection and classification. Relative motion between a target and the receiver induces time-varying Doppler shifts in the received line spectra, causing phase misalignment during long-time Fourier integration and resulting in spectral broadening, energy dispersion and coherent integration loss. To address this problem, this paper proposes a matched frequency-trajectory estimation method for long-time coherent integration enhancement (MFT-LTCIE). The method transforms the received signal into its analytic representation, extracts the narrowband component around the target line spectrum and partitions the signal into short-time segments. For each segment, matching responses are computed over a candidate frequency grid, and a maximum-matching criterion is used to estimate the segment-wise frequency trajectory. The estimated trajectory is interpolated onto the sample-time axis and used to construct a phase-compensation function relative to a reference frequency. Trajectory-guided phase compensation restores phase coherence, and a full-duration FFT of the compensated signal yields an enhanced coherent integration spectrum. Simulations show that MFT-LTCIE suppresses Doppler-induced spectral broadening and maintains sharp spectral peaks under different signal-to-noise ratios and target speeds. Experimental ship-radiated noise data further demonstrate its ability to track line-spectrum variations and refocus dispersed energy.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752483","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}