Iet Radar Sonar and Navigation最新文献

{"title":"An optimised scattering power decomposition method oriented to ship detection in polarimetric synthetic aperture radar imagery","authors":"Lu Fang,&nbsp;Wenxing Mu,&nbsp;Ning Wang,&nbsp;Tao Liu","doi":"10.1049/rsn2.12665","DOIUrl":"https://doi.org/10.1049/rsn2.12665","url":null,"abstract":"<p>This paper describes an optimised scattering power decomposition method suitable for ship detection in polarimetric synthetic aperture radar imagery. Based on the principal component analysis technique for dimension reduction and the scattering power decompositions with strong physical interpretation, a novel optimised scattering power decomposition model is proposed which comprises surface scattering, double-bounce scattering, ±45° oriented dipole, and volume scattering components. Wherein, the oriented dipole component can be used to characterise the compounded scattering of the target which is composed of even-bounce and odd-bounce reflectors. Furthermore, pocket algorithm and support vector machine are adopted to solve linear non-separable problems under complex experimental conditions in this study. Extensive experiments carried out on RADARSAT-2 data and NASA/JPL AIRSAR data show that the oriented dipole component significantly contributes to the optimised four-component decomposition method and can play a vital role in ship detection. Compared to other scattering power decompositions, the optimised decomposition method not only achieves better performance but is more in accordance with the real scattering mechanisms, which is more applicable to ship detection.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2642-2656"},"PeriodicalIF":1.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12665","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252544","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":"Target detection, ISAR imaging and tracking capabilities of a passive radar net utilising barrage jamming signals","authors":"Roman Mularzuk,&nbsp;Maciej Soszka,&nbsp;Piotr Szymański,&nbsp;Mariusz Zych,&nbsp;Michał Bartoszewski,&nbsp;Marcin Bączyk,&nbsp;Krzysztof Kulpa,&nbsp;Łukasz Maślikowski,&nbsp;Maciej Wielgo,&nbsp;Maria-Pilar Jarabo-Amores,&nbsp;Nerea Rey-Maestre,&nbsp;David Mata-Moya,&nbsp;Idar Norheim-Næss","doi":"10.1049/rsn2.12641","DOIUrl":"https://doi.org/10.1049/rsn2.12641","url":null,"abstract":"<p>Jammers aim to interfere with active radars during military operations. Still, they can strengthen our capabilities on the battlefield if there are proper methods to use them. This paper demonstrates the possibility of utilising a ground-based barrage jammer (to jam radars or communications) as an illumination source for passive radar detection, tracking and inverse synthetic aperture radar (ISAR) imaging. It shows the feasibility of using a jammer for cooperative (friendly jammer) and non-cooperative (hostile jammer) operations. The results come from the European Defence Agency project no B 1516 IAP2 GP entitled jammer-based passive radar. This article will present some of the outcomes of field trials conducted in June 2022 in Poland within the international electronic warfare exercises. Designed jammer demonstrators, with a maximum bandwidth of 115 MHz, were used as illuminators of opportunity and made it possible to test the operation of passive radars with five signal types (within two bands—L-band and S-band) for military targets (i.e. fighters, transport planes, helicopters). In the first part of the article, detection, ISAR imaging and tracking issues will be addressed. The second part of the article will focus on the results obtained during the project's joint field trials.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2144-2154"},"PeriodicalIF":1.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12641","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762382","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":"Guest Editorial: Synthetic aperture in sonar and radar","authors":"Marco Martorella,&nbsp;Gary Heald,&nbsp;Anthony Lyons,&nbsp;Michail Antoniou","doi":"10.1049/rsn2.12649","DOIUrl":"https://doi.org/10.1049/rsn2.12649","url":null,"abstract":"&lt;p&gt;Traditional sonar and radar both emerged during the early parts of the 20th century revolutionising the way we perceive and interpret the world around us, both above and under water. Particularly, synthetic aperture technologies have provided the tool for obtaining high-resolution images, namely synthetic aperture sonar (SAS) and synthetic aperture radar (SAR), which have opened the gates for a wide array of applications, ranging from military surveillance and environmental monitoring to archaeological exploration and disaster management.&lt;/p&gt;&lt;p&gt;Since the beginning of radar and sonar, it has been recognised that there are many areas of common interest. These include detection, classification, localisation and tracking of targets against a background of reverberation, noise or clutter, using either acoustic or electromagnetic energy. Over the past few decades there have been significant advances in both domains in the use of synthetic aperture imaging techniques—in radar for high resolution imaging from aircraft and satellites, defence surveillance purposes, geophysical and oceanographic remote sensing and environmental monitoring. In sonar it has been applied in high resolution imaging of objects on the seabed (including clutter) for the offshore industry and maritime mine countermeasures. Despite these common goals there has been very little cross-fertilization between the two scientific communities. This special issue is aimed at collecting scientific papers from both communities with the objective of contributing to increasing the exchange of knowledge between the two fields.&lt;/p&gt;&lt;p&gt;For this special issue, we received 10 papers, which underwent peer review. Papers were accepted or rejected based on the quality and fit with the special issue theme. Three of the five accepted papers focus on SAS, whereas the remaining two concern SAR.&lt;/p&gt;&lt;p&gt;The paper by &lt;i&gt;Hansen and Sæbø&lt;/i&gt; presents a novel method for optimising the collection geometry for long-range synthetic aperture sonar interferometry (InSAS). As InSAS performance strongly depends on the collection geometry, the authors focus on determining the performance metrics and their dependence on geometrical parameters and then define a model and a procedure for optimising the overall performance. The theoretical work produced in this paper is well supported with evidence provided by real data.&lt;/p&gt;&lt;p&gt;The paper by &lt;i&gt;Lane&lt;/i&gt; et al. shows how to implement target recognition and classification in SAR images with low-SWAP processing hardware. The authors utilise three different machine learning (ML)-based approaches to implement target detection and classification applied to SAR images, namely the RetinaNet, EfficientNet and Yolov5. The ML-based algorithms are trained by using a powerful cloud-based server, but they run on very low-SWAP devices, emulating their use in small and low-powered platforms. The authors make use of diverse types of SAR images to explore the algorithm effectiveness across va","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 11","pages":"2017-2019"},"PeriodicalIF":1.4,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762337","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":"Investigating the effects of destructive factors on pulse repetition interval modulation type recognition using deep convolutional neural networks based on transfer learning","authors":"Mahshid Khodabandeh,&nbsp;Azar Mahmoodzadeh,&nbsp;Hamed Agahi","doi":"10.1049/rsn2.12660","DOIUrl":"https://doi.org/10.1049/rsn2.12660","url":null,"abstract":"<p>Automation and self-sufficiency in the complex environment of modern electronic warfare (EW) are critical and necessary issues in electronic intelligence and support systems to detect real-time and accurate threat radars. The task of these systems is to search, discover, analyse, and identify the parameters of radar signals. However, recognition pulse repetition interval (PRI) modulation is challenging in natural environments due to destructive factors, including missing pulses (MP), spurious pulses (SP), and large outliers (LO) (caused by antenna scanning), which lead to noisy sequences of PRI variation patterns. The current article examines the effects of destructive factors on recognising PRI modulation in radar signals using deep convolutional neural networks (DCNNs). The article uses simulations based on the actual environment to generate data and consider destructive factors with different percentages. The number of images obtained by applying the sum of destructive factors for each range of destructive factors (with different percentages) considered is 30,000. It is common for six types of modulation. Then, the DCNN models, including VGG16, ResNet50V2, InceptionV3, Xception, and MobileNetV2, are trained using the transfer learning method. The simulation results show that the accuracy of training and testing the models decreases significantly with the increase in the percentage of destructive factors. Also, the effects of the model type on the performance of the models have been investigated, and the results have shown that some models are more resistant to destruction and retain more accuracy. Finally, this analysis shows that to improve the performance of deep neural network (DNN) techniques in the face of changes caused by destructive factors, it is necessary to pay attention to these factors and apply appropriate strategies.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2581-2607"},"PeriodicalIF":1.4,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12660","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248875","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":"Characterisation and synchronisation of a netted software defined radio radar","authors":"Ferran Valdes Crespi,&nbsp;Angel Slavov,&nbsp;Matthias Weiß,&nbsp;Peter Knott,&nbsp;Daniel O’Hagan","doi":"10.1049/rsn2.12652","DOIUrl":"https://doi.org/10.1049/rsn2.12652","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;The present work intends to characterise the combination of two netted software defined radios (SDR) and different radio frequency (RF) front-ends installed in them, together with a two-stratum time dissemination and synchronisation system. A modified implementation of the Network Time Protocol is used as coarse event synchronisation for either SDR back-end. The White rabbit light embedded node implementation of the commonly known white rabbit synchronisation system (IEEE 1588 PTP-2019) or arbitrary wave form generators are used as fine time dissemination system for either SDR. The resulting netted transceiver system is intended to compose a demonstrator for proof-of-concept experiments. The findings presented in this article show that the chosen combination of hardware and software is suitable for radar applications operating within L-, S- and C-bands. A channel coherency throughout the network with a relative modified Allan deviation of less than 80 fs for averaging intervals of 1 s, a phase noise better than −118 dBc/Hz at 10 Hz frequency offset and a fractional frequency lower than &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mn&gt;2.5&lt;/mn&gt;\u0000 &lt;mo&gt;⋅&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;13&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $pm 2.5cdot 1{0}^{-13}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; was measured. Within a single transceiver node, a fractional frequency lower than &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;±&lt;/mo&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;mo&gt;⋅&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;13&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $pm 2cdot 1{0}^{-13}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and phase noise of −124 dBc/Hz at 10 Hz frequency offset were measured as well. Multistatic radar systems exploit wide spatial diversity to enhance target detection and tracking, albeit with increased complexity when compared to a monostatic configuration. To exploit these benefits, all participating transceiver nodes within the netted radar need to synchronise to a common time base &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 ","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2427-2446"},"PeriodicalIF":1.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248373","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":"Phase coded waveforms for integrated sensing and communication systems","authors":"Sevda Şahin,&nbsp;Tolga Girici","doi":"10.1049/rsn2.12661","DOIUrl":"https://doi.org/10.1049/rsn2.12661","url":null,"abstract":"<p>Nowadays, especially with the developments in the automotive industry, it has become a highly popular topic for radar and communication systems to operate on the same platform and perform joint tasks to increase situational awareness. Sharing the hardware of the radar and communication systems and using a joint waveform for both functions eliminate interference problems between the systems, increasing the effectiveness of the joint task. In studies where radar signals are utilised for communication functions, continuous wave frequency modulation or intra-pulse frequency modulation is generally employed to transmit communication signals. There are a few studies in the literature on joint radar and communication waveforms using phase modulation. In these studies, phase modulation is not employed directly for both functions. As a contribution to the literature, this study evaluates the usage of joint radar and communications waveforms, such as Barker sequences and Zadoff–Chu sequences as opposed to linear frequency modulation. We compare these waveforms terms of range-speed detection and symbol error rate.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2608-2616"},"PeriodicalIF":1.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143247458","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":"Hyperparameter free sparse estimation for wideband multiple-input-multiple-output radar direction finding without secondary data","authors":"Jiong Xiao,&nbsp;Bo Tang","doi":"10.1049/rsn2.12658","DOIUrl":"https://doi.org/10.1049/rsn2.12658","url":null,"abstract":"<p>The estimation of target directions of arrival (DOA) for wideband multiple-input-multiple-output (MIMO) radar is investigated in this article. First, the authors establish a signal model for wideband MIMO radar systems. Then, an algorithm is proposed to estimate the target angles without secondary data (i.e. the training data from slow-time is not required).The proposed algorithm unitises the spatial sparsity of target signals and it is derived under the Bayesian framework. It can estimate the spatial pseudo-spectra of the targets through cyclic optimisation. Additionally, it is hyperparameter-free and guarantees convergence. To analyse the performance of the proposed algorithm, the Cramér-Rao bound (CRB) is derived for DOA estimation with wideband MIMO radar. Numerical examples demonstrate that even without secondary data, the proposed algorithm can accurately estimate the DOA of the targets.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2552-2563"},"PeriodicalIF":1.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12658","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253610","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":"Subspace-based distributed target detection method with small training data samples","authors":"Guangfen Wei,&nbsp;Zhan Zhou,&nbsp;Yuan Luo,&nbsp;Tao Jian,&nbsp;Xiaoming Tang","doi":"10.1049/rsn2.12655","DOIUrl":"https://doi.org/10.1049/rsn2.12655","url":null,"abstract":"<p>Detecting distributed targets precisely in homogeneous environments has been a hot topic in radar signal processing. Generally, distributed targets are often modelled with subspace models of unknown coordinates, and clutter is modelled as the complex Gaussian distribution with zero mean and unknown covariance matrix, while covariance matrix is estimated with a set of training data without the target signal. However, in practice, the complexity of the external environment makes the training data that satisfy the condition of independent homogeneous distribution less available. Therefore, it is assumed that the covariance matrix of the clutter is persymmetric structure and the approach of dimensionality reduction using subspace transformations is introduced, two detectors based upon generalised likelihood ratio test criterion and Wald test criterion in homogeneous environments are proposed. Theoretical analyses indicate the constant false alarm rate characteristics of the two proposed detectors for unknown clutter covariance matrices. Simulation analyses indicate that the proposed detector works well even with fewer training data samples, and its detection performance outperforms that of existing contrast detectors.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2541-2551"},"PeriodicalIF":1.4,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253413","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":"Bistatic multi-polarimetric synthetic aperture radar coherence investigation using spatially variant incoherence trimming","authors":"Alexander Hagelberg,&nbsp;Daniel Andre,&nbsp;Mark Finnis","doi":"10.1049/rsn2.12659","DOIUrl":"https://doi.org/10.1049/rsn2.12659","url":null,"abstract":"<p>Synthetic Aperture Radar (SAR) Coherent Change Detection allows for the detection of very small scene changes. This is particularly useful for Intelligence, Surveillance and Reconnaissance as small changes such as vehicle tracks can be identified. Rapidly collecting repeat pass SAR imagery is important in these applications. For space-borne platforms, such repeat passes may however have significant differences, or baselines. Coherent Change Detection products are reliant on high coherence for good interpretability. This work investigates the sources and levels of incoherence associated with bistatic SAR imagery for a variety of baselines using simulations and measured laboratory data for two ground types. Additionally, spatially variant incoherence trimming is implemented. The paper shows the importance of angle-dependant backscatter on the coherence of sub-resolution cell scatterers.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2564-2580"},"PeriodicalIF":1.4,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12659","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253412","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":"Enhancing underwater target detection: Fusion of spatio-temporal incompletely-aligned AIS and sonar information via DTW and multi-head attention mechanism","authors":"Wenbo Zhao,&nbsp;Xinghua Cheng,&nbsp;Dezhi Wang,&nbsp;Xiaodan Xiong,&nbsp;Xiaoshuang Zhang","doi":"10.1049/rsn2.12653","DOIUrl":"https://doi.org/10.1049/rsn2.12653","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>In the field of underwater target detection, the passive sonar is an important means of long-distance target detection. The sonar detection information typically includes both surface and underwater targets, whereas it is a great challenge on effectively distinguishing between surface and underwater targets solely based on sonar information. Effective fusion of sonar and AIS (Automatic Identification System) data can leverage their complementary nature to compensate for the limitation of sonar information. However, the sonar information and AIS information are acquired based on different detection principles and systems, which are essentially multi-source heterogeneous information with obvious spatio-temporal misalignment in nature. Existing fusion methods normally struggle to effectively align sonar and AIS data in both time and space subject to the complexity of the problem. In this study, the Dynamic Time Warping (DTW) algorithm is applied to align sonar and AIS data in the time domain. In addition, a deep learning algorithm with multi-head attention mechanism is proposed to achieve the spatial alignment of sonar and AIS data, where the matching between the surface targets in AIS data and the same surface targets in sonar data can also be successfully achieved. It provides a priori knowledge to enhance the underwater target detection of the passive sonar by eliminating the interference of the surface targets. Based on the attention mechanism, the abstract features extracted from the intermediate-layer of the neural networks are found to be effective to represent the typical features of the target motion trajectories, which also demonstrates the effectiveness of the attention mechanism. The experiment results show that the proposed method can successfully achieve a MatchingSucccessRate of over 95% between the AIS targets and sonar detection targets.</p>\u0000 </section>\u0000 </div>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"18 12","pages":"2521-2540"},"PeriodicalIF":1.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253007","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}