Iet Radar Sonar and Navigation最新文献_第8页

Spectrally Compatible Deceptive Jamming Waveform Design for Synthetic Aperture Radar Countermeasures 合成孔径雷达对抗的频谱兼容欺骗干扰波形设计

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-01-11 DOI: 10.1049/rsn2.70110

Yuhao Chen, Bo Tang, Da Li, Yangjia Wang, Wenjun Wu

{"title":"Spectrally Compatible Deceptive Jamming Waveform Design for Synthetic Aperture Radar Countermeasures","authors":"Yuhao Chen, Bo Tang, Da Li, Yangjia Wang, Wenjun Wu","doi":"10.1049/rsn2.70110","DOIUrl":"https://doi.org/10.1049/rsn2.70110","url":null,"abstract":"This paper focuses on the design of synthetic aperture radar (SAR) deceptive jamming waveforms with spectral compatibility. We formulate an optimisation problem to minimise the matching error between the designed and the intercepted signal, under the constraint that the interference of the jammer to the nearby friendly radiators is controlled. Additionally, to enhance the effective radiated power of the jammers, we enforce a peak-to-average power ratio (PAPR) constraint on the waveforms. To deal with the formulated problem, we propose an iterative algorithm based on alternating direction multiplier method (ADMM). Numerical results demonstrate the fast convergence of the ADMM algorithm. Moreover, the jamming waveform designed by the algorithm exhibits high similarity <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mrow>\u0000 <mo>></mo>\u0000 <mn>0.95</mn>\u0000 </mrow>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation> $( > 0.95)$</annotation>\u0000 </semantics></math> to the waveform transmitted by the hostile radar. In addition, the waveform forms deep notches over the frequency band of the friendly radiator such that the interference is reduced. By using the synthesised jamming waveform, successful deception against the hostile SAR system is demonstrated by generating false targets with high fidelity in both range and azimuth.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145964061","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}

引用次数: 0

On pooling-based track fusion strategies: Harmonic mean density 基于池化的轨迹融合策略：谐波平均密度

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2026-01-09 Epub Date: 2025-07-03 DOI: 10.1049/rsn2.12681

Nikhil Sharma, Shovan Bhaumik, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan

{"title":"On pooling-based track fusion strategies: Harmonic mean density","authors":"Nikhil Sharma, Shovan Bhaumik, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan","doi":"10.1049/rsn2.12681","DOIUrl":"10.1049/rsn2.12681","url":null,"abstract":"In a distributed sensor fusion architecture, using standard Kalman filter (naive fusion) can lead to degraded results as track correlations are ignored and conservative fusion strategies are employed as a sub-optimal alternative to the problem. Since, Gaussian mixtures provide a flexible means of modelling any density, therefore fusion strategies suitable for use with Gaussian mixtures are needed. While the generalised covariance intersection (CI) provides a means to fuse Gaussian mixtures, the procedure is cumbersome and requires evaluating a non-integer power of the mixture density. In this paper, the authors develop a pooling-based fusion strategy using the harmonic mean density (HMD) interpolation of local densities and show that the proposed method can handle both Gaussian and mixture densities without much changes to the framework. Mathematical properties of the proposed fusion strategy are studied and simulated on two-dimensional (2D) and three-dimensional (3D) manoeuvering target tracking scenarios. The simulations suggest that the proposed HMD fusion performs better than other conservative strategies in terms of root-mean-squared error while being consistent.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145983588","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}

引用次数: 0

Radar Emitter Identification Based on Typical Parameter Sequence: HBNP Clustering, Hierarchical Denoising and LSTM Classification 基于典型参数序列的雷达辐射源识别：HBNP聚类、分层去噪和LSTM分类

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-12-30 DOI: 10.1049/rsn2.70105

Chen-Qian Zhao, Hong-Lei Qin, Rong-Ling Lang

{"title":"Radar Emitter Identification Based on Typical Parameter Sequence: HBNP Clustering, Hierarchical Denoising and LSTM Classification","authors":"Chen-Qian Zhao, Hong-Lei Qin, Rong-Ling Lang","doi":"10.1049/rsn2.70105","DOIUrl":"10.1049/rsn2.70105","url":null,"abstract":"To address the challenges of ‘high-density interference’ and ‘multi-mode parameter agility’ in Radar Emitter Identification (REI) under complex electromagnetic environments, as well as the limitations of existing models-pulse sequence models have weak anti-noise capability, whereas statistical feature models are prone to multi-mode parameter confusion-this paper proposes an REI method based on Typical Parameter Sequences (TPS). First, a three-level ‘operational mode-beam dwell-pulse group’ signal model is constructed to clarify the hierarchy of key radar features and lay a foundation for both the rationality of TPS and sliding windows design in TPS extraction. A Pulse Repetition Interval (PRI) probability model under pulse interference and loss is also established, providing a theoretical basis for noise suppression. Second, Hierarchical Bayesian Nonparametrics (HBNP) clustering and hierarchical denoising extract local typical parameters, which are concatenated into global TPS (retains temporal information, anti-noise, data compression) via sliding windows. Finally, Long Short-Term Memory (LSTM) realises emitter identification. Simulation experiments show that: In strong noise environments, the proposed model's accuracy is significantly higher than that of pulse sequence models after accumulating a limited number of beam dwells; in multi-mode switching scenarios, its accuracy is much higher than that of statistical feature models, helping alleviate multi-mode parameter confusion.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145891016","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}

引用次数: 0

Joint Time Synchronisation and Localisation Method by TDOA Observation in 5G Networks 5G网络TDOA观测联合时间同步与定位方法

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-12-28 DOI: 10.1049/rsn2.70109

Qiao Liu, Yutong Tian, Zhihang Qu, Yong Li

{"title":"Joint Time Synchronisation and Localisation Method by TDOA Observation in 5G Networks","authors":"Qiao Liu, Yutong Tian, Zhihang Qu, Yong Li","doi":"10.1049/rsn2.70109","DOIUrl":"https://doi.org/10.1049/rsn2.70109","url":null,"abstract":"With the continuous evolution of mobile communication technology and the booming development of the Internet of Things, positioning has become one of the important functions of 5G. Label positioning in wireless networks often adopts a time difference of arrival (TDOA)-based trilateral positioning scheme, whose accuracy is related to the time synchronisation information between base stations. Therefore, time synchronisation and localisation calculation are two key technologies for 5G wireless network localisation, but they are usually treated as two independent stages. This work proposes a unified framework model that utilises the TDOA measurements of all base stations in 5G wireless positioning networks, while estimating the clock deviation inside the base stations and the position of the tags to be located, in order to save system resources and improve positioning performance. Firstly, a TDOA ranging and positioning model based on clock offset is proposed. For the solution of this model, a joint time synchronisation and localisation algorithm based on alternating optimisation and alternating maximum likelihood is proposed. For the selection of initial iteration values, the maximum volume indexed ellipsoid solution is introduced to significantly improve convergence. The results show that this scheme achieves a positioning accuracy of 2 m and a synchronisation accuracy of 4 ns.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"20 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145891578","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}

引用次数: 0

Variational Bayesian Inference for Multiple Extended Targets or Unresolved Group Targets Tracking 多扩展目标或未解析群目标跟踪的变分贝叶斯推理

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-12-21 DOI: 10.1049/rsn2.70098

Yuanhao Cheng, Yunhe Cao, Tat-Soon Yeo, Yulin Zhang, Jie Fu

{"title":"Variational Bayesian Inference for Multiple Extended Targets or Unresolved Group Targets Tracking","authors":"Yuanhao Cheng, Yunhe Cao, Tat-Soon Yeo, Yulin Zhang, Jie Fu","doi":"10.1049/rsn2.70098","DOIUrl":"10.1049/rsn2.70098","url":null,"abstract":"In this work, we propose a method for tracking multiple extended targets or unresolvable group targets in a clutter environment. First, based on the random matrix model (RMM), each target's joint kinematic–extent state is modelled as a gamma Gaussian inverse Wishart (GGIW) distribution. Considering the uncertainty of measurement origin caused by the clutters, we adopt the idea of probabilistic data association and describe the joint association event as an unknown parameter in the joint prior distribution. Then, variational Bayesian inference (VBI) is used to approximate the intractable posterior distribution. To improve practicality, we propose two lightweight schemes to reduce computational complexity. The first is clustering-based and effectively prunes joint association events. The second simplifies the variational posterior by using marginal association probabilities. Finally, we demonstrate effectiveness on simulations and real-data experiments and show that the method outperforms state-of-the-art baselines in accuracy and adaptability.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145846028","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}

引用次数: 0

Rapid Target Search Method for Distributed Aperture Radar via Role Reversal 基于角色转换的分布式孔径雷达快速目标搜索方法

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-12-09 DOI: 10.1049/rsn2.70087

Jing Chen, Wen Fan, Qun Wan

{"title":"Rapid Target Search Method for Distributed Aperture Radar via Role Reversal","authors":"Jing Chen, Wen Fan, Qun Wan","doi":"10.1049/rsn2.70087","DOIUrl":"10.1049/rsn2.70087","url":null,"abstract":"Distributed aperture radar (DAR) systems encounter significant computational challenges in near-field target detection because of the large synthesised equivalent apertures which drastically increase scan cell dimensionality and processing complexity. To address this, we propose a novel near-field processing method based on ‘Virtual Array-Observer (VAO)’ role reciprocity. Our approach constructs a radar virtual array at the target location, which inversely observes the distributed radar deployment. Crucially, we design the equivalent aperture of this virtual array to satisfy far-field electromagnetic transmission conditions. This enables the derivation of a spatial steering vector model that compensates for and aligns range/azimuth units into regularly arranged array data. By leveraging a fast Fourier transform (FFT)-based rapid computation model for efficient steering vector calculation, we establish a universal computational framework for accelerated target detection. Simulations demonstrate that the proposed method achieves multi-node echo synthesis with energy loss under 8<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $%$</annotation>\u0000 </semantics></math> compared to theoretical values, while simultaneously reducing computational burden substantially. This framework provides an efficient solution for real-time, large-scale DAR implementations, effectively bridging the gap between coherent signal synthesis requirements and computational feasibility in near-field scenarios.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739547","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}

引用次数: 0

Convolutional Neural Network Based Joint Estimation of Direction of Arrival and Polarisation Parameters 基于卷积神经网络的到达方向和偏振参数联合估计

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-12-03 DOI: 10.1049/rsn2.70102

Pangzhe Li, Dexiu Hu, Chuang Zhao

{"title":"Convolutional Neural Network Based Joint Estimation of Direction of Arrival and Polarisation Parameters","authors":"Pangzhe Li, Dexiu Hu, Chuang Zhao","doi":"10.1049/rsn2.70102","DOIUrl":"10.1049/rsn2.70102","url":null,"abstract":"In this paper, a convolutional neural network (CNN) based algorithm for joint estimation of direction of arrival (DOA) and polarisation parameters is introduced. Previous algorithms for the joint estimation of DOA and polarisation parameters frequently exhibit performance degradation under low signal-to-noise ratio conditions and with a limited number of snapshots, which imposes significant constraints on their practical applicability. To address these limitations, this paper proposes a CNN-based algorithm for the joint estimation of DOA and polarisation parameters. This method splits the joint estimation of DOA and polarisation parameters into two steps. In the first step, the upper triangular array of array output covariance matrices is used as data input to CNN to realise DOA estimation from signal sources. The second step establishes the polarisation and spatial domain spectral functions, followed by a spectral peak search to estimate the polarisation parameters of the signal. Simulation results demonstrate that the proposed algorithm achieves a significantly higher estimation accuracy compared to classical estimation methods under the conditions of low signal-to-noise ratios and a limited number of snapshots. The analysis of the simulation results shows that the proposed algorithm leverages a CNN, which fully exploits the spatial characteristics of the array output covariance matrix and consequently reduces the computational complexity.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686189","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}

引用次数: 0

Direction Finding Techniques for Over-the-Horizon Radars 超视距雷达测向技术

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-12-03 DOI: 10.1049/rsn2.70103

M. Pacek, Z. Matousek, J. Puttera, J. Perdoch

{"title":"Direction Finding Techniques for Over-the-Horizon Radars","authors":"M. Pacek, Z. Matousek, J. Puttera, J. Perdoch","doi":"10.1049/rsn2.70103","DOIUrl":"https://doi.org/10.1049/rsn2.70103","url":null,"abstract":"There is no ultimate intelligence-gathering discipline. Intelligence products almost always require multiple sources for further analytical processing. However, signals intelligence represents a unique gathering discipline since information is derived from both communication and noncommunication electromagnetic emissions; it operates passively and is usually highly reliable. A subdivision of signals intelligence dedicated solely to the noncommunication emission is electronic intelligence. It focuses mainly on emitters such as radars, telemetry systems and radio navigation systems. Over-the-horizon radars are part of systems for long-range detection. This paper presents a comprehensive evaluation of three distinct direction-finding methods implemented within a unified three-channel coherent system for the bearing estimation of over-the-horizon (OTH) radar transmitters. The key contribution lies in the detailed simulation-based assessment of these methods under varying signal-to-noise ratio (SNR) conditions using realistic waveform parameters derived from measured real OTH radar signals. The results quantitatively characterise the operational strengths and limitations of each method, offering a practical foundation for algorithm selection in electronic intelligence applications and enabling future integration of machine learning models to enhance direction-finding performance in a dynamic operational environment.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695059","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}

引用次数: 0

Boundary-Aware Learning for Robust Automatic Modulation Recognition Based on Adaptive Pyramid Network 基于自适应金字塔网络的鲁棒自动调制识别的边界感知学习

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-11-28 DOI: 10.1049/rsn2.70097

Yuanhang Li, Mengyi Qi, Xiaofeng Tao, Wenbin Shao

{"title":"Boundary-Aware Learning for Robust Automatic Modulation Recognition Based on Adaptive Pyramid Network","authors":"Yuanhang Li, Mengyi Qi, Xiaofeng Tao, Wenbin Shao","doi":"10.1049/rsn2.70097","DOIUrl":"10.1049/rsn2.70097","url":null,"abstract":"Automatic modulation recognition (AMR) of radar signals plays a pivotal role in intelligent spectrum management for modern electronic warfare systems. Although deep learning has improved AMR accuracy, existing approaches encounter challenges when identifying signal types beyond those seen during training. To overcome this limitation, this work proposes a boundary-aware learning (BAL) framework with three innovations. First, an adaptive pyramid network automatically highlights important time-frequency information at multiple dimensions, producing richer and more discriminative signal representations. Second, a boundary-aware learning strategy shapes the embedding space so that samples of the same modulation are drawn tightly together, whereas different modulations are pushed farther apart. Third, a distance-based rejection mechanism measures how far a new signal lies from known clusters, enabling reliable detection of previously unseen modulations. Together, these components create a unified feature space that both sharpens class boundaries and isolates unknown signal types. Extensive experiments on both simulated and measured radar datasets show that the proposed framework outperforms conventional architectures in open-set signal recognition, confirming its superior robustness in realistic electromagnetic environments.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619218","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}

引用次数: 0

Adaptive Fast Track Association for Small Samples in Distributed Multi-Sensor Systems 分布式多传感器系统中小样本自适应快速通道关联

IF 1.5 4区管理学

Iet Radar Sonar and Navigation Pub Date : 2025-11-28 DOI: 10.1049/rsn2.70099

Xin Guan, Zhijun Huang, Xiao Yi, Haotian Yu

{"title":"Adaptive Fast Track Association for Small Samples in Distributed Multi-Sensor Systems","authors":"Xin Guan, Zhijun Huang, Xiao Yi, Haotian Yu","doi":"10.1049/rsn2.70099","DOIUrl":"10.1049/rsn2.70099","url":null,"abstract":"The track association problem aims to determine which tracks belong to the same real target, supporting subsequent processes such as target state estimation, target tracking and situation assessment. Existing algorithms are primarily based on statistical mathematics, fuzzy mathematics, grey system theory and neural networks. However, they suffer from several limitations, including overly idealised modelling, manually set thresholds, two-level traversal and poor association performance under few-shot conditions. In the light of the above problems, we propose a K-Point Input LSTM-KNN-DPC (KLKD) algorithm. Firstly, considering the rationality of feature selection, we introduce a similarity measurement method with an adaptive cutoff distance. Secondly, a method for cluster centre selection is presented. Finally, an association assignment strategy is provided. The proposed algorithm eliminates the need for timestamp alignment and exhaustive pairwise matching across different track sequences, thereby improving the efficiency of track association. Experimental results demonstrate that, in both typical manoeuvring and irregular manoeuvring scenarios, the KLKD algorithm achieves higher association accuracy and lower end-to-end latency compared to existing methods.","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619217","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}

引用次数: 0