Iet Radar Sonar and Navigation最新文献

With the frequent occurrence of drone black flying in large stadiums and airports, the requirement of precise electromagnetic interference to achieve unmanned aerial vehicle (UAV) displacement and forced landing in these protected area became urgent. Since traditional suppression jamming will affect normal communication, and forwarding deceptive jamming has coverage hole, a smart forwarding deceptive jamming distribution optimal algorithm (SFDJDO) is proposed. It gives a mapping error scale factor and the optimal distribution of multistation forwarding equivalent mapping to solve the problem of distortion on the mapping scale caused by different distribution methods and reduces the influence of the mapping errors and the differences between the virtual and real point neighborhoods of the jamming source. A comparison of the proposed SFDJDO method to the existing jamming source distribution optimisation method is conducted in the aspect of area mapping and trajectory mapping. The findings reveal that when the GNSS receiver clock bias is within the capture range, SFDJDO demonstrates significant enhancements in mapping precision and jamming success rates.

A novel method is proposed for rail fastener detection based on millimetre-wave (mmWave) radar, mmWave radar fastener counter (MMW-FC), which can accurately detect and record the fasteners in real-time as the train traverses its route. Under circumstances where GNSS signals remain unavailable for prolonged durations, precise train localisation can be accomplished by correlating the number of fasteners derived from this method with the corresponding track map. Initially, MMW-FC utilises fast Fourier transform and adaptive beamforming to focus the energy reflected from fasteners. Subsequently, it applies an adaptive template-matching algorithm to detect each fastener. Furthermore, by leveraging known fastener spacing and the average time for trains to pass adjacent fasteners, the Kalman filter can execute precise speed tracking, used as a speed reference when adjusting the matching template adaptively. The experimental results indicate that the proposed method can precisely count the fasteners the train encounters in diverse road and speed conditions. The fastener counter maintains the Counting Error less than 0.067%, the speed error stays below 1.8 km/h, and the maximum values of the mean absolute error and root mean square error for speed are 0.7337 and 0.9584 km/h, respectively.

The author reveals that the power pattern for a particular selected rotating spherical electric current density profile exhibits the following two properties simultaneously: (a) fully omnidirectional in three dimensions (3-D) and (b) invariant with regards to radio frequency (RF). Specifically, most known antenna designs exhibit either nodal lines or planes for at least some RF frequencies. In contrast, the primary innovation of the subject rotating electric current sphere is that it generates a power pattern that is characterised by no nodal lines nor nodal planes for any RF frequency. In the present analysis, the electro-magnetic (EM) fields are calculated as an exact solution of Maxwell's equations for the subject electric current density that rotates azimuthally on a spherical surface. As expected, the spatial structure of the resulting EM fields also rotates azimuthally. More surprisingly, this rotating electric current density generates pure magnetic dipole radiation exactly, with the absence of any higher order multipole moments. This proposed antenna concept could offer utility in various applications, including communications beaconing and radar surveillance.

Noise impairs the performance of inverse synthetic aperture radar (ISAR) motion compensation, which induces severe defocusing under low signal-to-noise ratio environments. To overcome this issue, a novel similarity-oriented (SO) method with a two-domain denoising strategy is proposed. A PIxEl similarity-oriented (PIE-SO) denoising method designed for range-Doppler (RD) domain and a modified RAnge Profile Similarity-Oriented (RAP-SO) denoising method designed for high-resolution range profile (HRRP) matrix are included in the presented framework. Firstly, the PIE-SO method directly performs a two-dimensional fast Fourier transform on dechirp processed echo data to form a coarsely focusing ISAR image in the RD domain. Then the focusing image is separated from the noise background by virtue of pixel similarity, after which the noise is preliminarily removed. Subsequently, the coarsely denoised image is transformed into the HRRP matrix. Considering the range profile similarity impaired by noise is restored by the PIE-SO denoising, a Laplacian regularised-weighted nuclear norm proximal (LR-WNNP) operator is proposed. The proposed modified RAP-SO method, that is, the LR-WNNP operator, exploits the low-rank property of the HRRP matrix and the local similarity of adjacent HRRPs to reduce the residual noise. As a result, ISAR imaging quality is significantly improved. Comprehensive experiments illustrate the effectiveness and superiority of the presented method.

To enhance the capability of identifying unknown emitters in open spaces, an open-multiscale attention kernel (MSAK)-convolutional neural network-long short-term memory (CNN-LSTM) structure is proposed. To this end, first, a MSAK module and CNN-LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK-CNN-LSTM model is improved to obtain an open-MSAK-CNN-LSTM model with open-set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open-MSAK-CNN-LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal-to-noise ratio (SNR) scenarios.

In electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open-set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi-RNN). RBi-RNN utilises input reconstruction and employs a joint training strategy incorporating cross-entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter-class latent representation distances while minimising intra-class disparities. By incorporating an open-set recognition method based on extreme value theory, RBi-RNN adapts to open-set scenarios. Simulation results demonstrate the superiority of RBi-RNN over conventional models in both closed-set and open-set scenarios. In open-set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses.

Nowadays, sparse arrays have been a focus for direction-of-arrival (DOA). The existing arrays can achieve high degree of freedom (DOF) bigger than the number of sensors when using the spatial smoothing methods. However, the small inter-element spacing degrades the DOA estimation accuracy when facing severe mutual coupling. In order to alleviate mutual coupling, a coprime linear array using three uniform linear arrays (CLA-U3) is proposed. The expression of sensor locations is given, and the analysis of DOF is considered. The minimum subarray inter-element spacing in CLA-U3 is bigger than that of the existing arrays, which means that CLA-U3 can be less sensitive to mutual coupling. Moreover, the difference co-subarrays can fill each other's holes, so CLA-U3 and other sparse arrays composed of three subarrays have close DOF. Simulation experiments prove the favourable performance of DOA estimation.

Recent developments in Artificial Intelligence (AI) and the accessibility of cost-effective radar hardware have transformed various sectors, including e-healthcare, smart cities, and critical infrastructures. AI holds immense potential for enhancing radar technology. However, there are significant challenges hindering its adoption in this domain. These challenges encompass Radar Data Accessibility, which involves limited access to radar data for training AI models due to low sample availability. Data Labelling, requiring domain-specific expertise, and Data Pre-processing, aimed at selecting the best radar data representation for AI applications, are complex and vital steps. Additionally, integrating an AI framework into radar hardware, whether using pre-trained or custom models, presents a major obstacle. This special issue focuses on research, articles, and experiments that bridge the gap between radar hardware and AI frameworks, addressing these critical challenges.

The special issue has garnered significant interest, with a total of 13 paper submissions. After rigorous peer review, nine papers that met high publication standards were accepted. These papers collectively address crucial challenges in AI-assisted radar technology, offering innovative ideas, insightful analyses, and experimental results that bridge the gap between radar hardware and AI frameworks. Most notably, these papers include real-world validation and demonstrate innovative system designs and processing solutions. They advance current knowledge and pave the way for future innovations in the field.

Among the featured papers, Zhou et al. focus on the application of millimetre-wave radar, specifically 4D TDM MIMO FMCW radar, for health monitoring and human activity recognition [1]. Their comprehensive simulation model achieves an impressive 90% average classification accuracy, offering valuable insights for radar configuration and activity testing. Zhenghui Li et al. introduce an innovative approach to radar-based human activity recognition across six domains, with adaptive thresholding and holistic optimisation, significantly improving classification accuracy [2]. Li et al. propose a ground-breaking voice identification method using Ultra-Wideband technology, leveraging micro-Doppler shifts during speech production to achieve close to 90% accuracy in healthcare applications [3].

Yu et al. explore radar-based human activity recognition for elderly care health monitoring, addressing noisy radar signals. They introduce wavelet denoising and the Double Phase Cascaded Denoising and Classification Network, improving accuracy and robust activity monitoring [4]. Xiong et al. tackle track-to-track association (T2TA) challenges by using homography estimation to address radar bias, enhancing association credibility and reducing manual labelling [5]. Perďoch et al. utilise a s

It is our great pleasure to present you with this IET Radar, Sonar and Navigation special issue on the ‘Selected Papers from RADAR 2022—International Conference on Radar Systems (Edinburgh, UK)’.

RADAR 2022 took place at Murrayfield Stadium, Edinburgh, on 24–27 October 2022 as a prime opportunity for radar specialists at all career stages to update and enhance their knowledge on the latest developments in advanced radar systems. As such, RADAR 2022 was attended by over 250 delegates from 22 countries who joined the conference to explore the latest technologies in radar systems.

Key topics of RADAR 2022 included new radar trends and developments, bistatic and multistatic radar, target detection (with particular emphasis on drones), Constant False Alarm Rate algorithms, tracking before and after detection, low-frequency radar, waveform diversity, performance evaluation, virtual prototyping, and cognitive radar. Presentations covered the latest radar developments and were complimented by a set of outstanding tutorials given by world-leading radar experts on key elements of radar technology, a radar competition and keynote addresses from leading experts.

In total, 126 papers were published in the proceedings of RADAR 2022. Out of all these, the authors of the approximately 30 best papers, that scored the highest peer-review scores in the conference review selection, were invited to extend their conference papers into a journal article for this special issue.

This special issue contains 17 papers which are based on extended work presented at the conference on topics that include waveform design, estimation, passive radar, multistatic radar, Synthetic Aperture Radar (SAR), radar clutter and target signatures for detection and classification. The papers published in this special issue contain at least 40% new material compared to the work published in the RADAR 2022 conference proceedings and underwent a brand-new, rigorous, and robust peer-review process as set out by very high common IET and Wiley standards.

An analysis of the parameter estimation uncertainty for the target location and velocity achievable using a single-transmitter multiple-receiver multistatic radar system is presented in Ref. [1]. The paper proposes a framework for establishing multistatic radar parameter estimation uncertainties by an expansion of the bistatic radar performance. The proposed technique employs analytical methods based on the Cramér–Rao Lower Bound, and these are applied to scenarios in a two-dimensional physical space with a single target exhibiting Doppler characteristics and a bistatic angle-dependent radar cross-section. The results indicate that angular separation between the transmitter and the centre of the receiver distribution is of greater importance than the quantity of receivers, though a minimum of two receivers must be available. Results also show that increasing the total number of receivers reduces the pr

The current mature Simultaneous Localisation And Mapping (SLAM) algorithms, when applied to tunnel scenarios with point cloud degradation and poor lighting conditions, often lead to a sharp increase in the estimated attitude error of the unmanned aerial vehicle (UAV), or even prevent the UAV from moving autonomously due to severe feature degradation. To address the above problems, the authors propose a SLAM algorithm based on factor graph optimisation, Iterative Closest Point and Normal Distributions Transform algorithms. A front-end point cloud registration module and a back-end construction algorithm based on filtering and graph optimisation are designed. To verify the effectiveness of the proposed algorithm, experiments are conducted on KITTI dataset and real tunnel scenes, and compared with LiDAR Odometry and Mapping (LOAM) and lightweight and ground optimised (LeGO)-LOAM algorithms. The results show that the average processing time of the proposed method is about 75 ms, which can meet the real-time requirements of autonomous aerial vehicles. Compared with LOAM and LeGO-LOAM in the real tunnel experiment, the proposed method shows the tunnel 3D map construction.