Iet Radar Sonar and Navigation最新文献

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.

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$��\%�$ 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.

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.

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.

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.

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.

Given rising UAV security concerns, this work addresses a critical counter-UAV limitation: the inability to rapidly discern UAV versus non-UAV targets (birds and balloons), causing response delays and false alarms. We establish this binary classification as a pivotal security decision node, directly activating response protocols to enhance system timeliness, reliability and efficacy. We propose an interpretable LightGBM framework integrating fused radar features (motion, RCS and track). Hyperparameters are optimised via grid search with performance validated through 5-fold cross-validation. SHAP values quantify feature contributions. Validation experiments show that the proposed framework achieves 92.55% overall accuracy in UAV and non-UAV classification based on low-altitude radar data, outperforming all comparative models including SVM, RF, BPNN, FT-Transformer, TabNet and LSTM, in both accuracy and inference speed. The SHAP-based interpretable framework simultaneously ensures high classification accuracy and reliable decision validation, thereby providing dual technical assurance in accuracy and interpretability for low-altitude security system deployment.

To address the limitations of existing UAV-mounted LiDAR systems, this paper proposes an innovative real-time transmission and rendering technology for massive point cloud data. The technology utilises an onboard LiDAR placed on the front of the UAV to collect 3D spatial point cloud data, which is transmitted to an onboard Intel NUC for critical processing steps such as data analysis and lossy compression. The compressed data are then transmitted via the UAV's video transmission link to a mobile control app, enabling real-time transmission and rendering of massive point cloud data on Android terminals. This approach effectively overcomes the portability drawbacks of traditional methods that rely on bulky computers, requiring only an Android mobile terminal. A well-designed lossy compression strategy significantly improves data transmission efficiency and reduces computational pressure for point cloud rendering on low-memory mobile devices. Integrated with the SLAM (simultaneous localisation and mapping) algorithm on a flight test platform composed of a DJI M350 RTK UAV, Velodyne VLP16 LiDAR, Intel NUC onboard computer and DJI RC Plus Android controller, the system achieves high-precision 3D point cloud real-time transmission and rendering, enabling real-time Beyond Visual Line of Sight (BVLOS) UAV control. Experimental results demonstrate that this technology can process millions of point cloud data per second on the UAV mobile controller, exhibiting excellent real-time data transmission and rendering performance under various environmental conditions, including low latency and high frame rates, meeting stringent requirements for high precision and real-time responsiveness.

Geosynchronous synthetic aperture radar (GEO SAR) provides extensive beam coverage and strong continuous observation capabilities, making it a research focus in the remote sensing. Based on the analysis of the GEO SAR illuminated scene characteristics, it is found that the nonplanar and undulating elevation of the ground surface leads to significant azimuth and range spatial variance in the echo signals. To attain precise, geometrically undistorted fast back-projection (BP) SAR images, we analyse the impact of elevation errors on the signal's quadratic phase. Then, a fast back-projection imaging method for GEO SAR geometric distortion calibration based on dynamic selection of local sub-aperture images using a coarse digital elevation model (DEM) is proposed. Firstly, the elevation information of the imaging grid is obtained through coarse DEM interpolation. Secondly, the coordinates of the dynamically selected local sub-aperture images can be approximated by a fully expanded quadratic coordinate polynomial. And the compression function of the two-stage spectrum compression method is modified by utilising this polynomial. Ultimately, the full-aperture SAR image is produced by conducting multi-level fusion operations and mosaic techniques. Additionally, a detailed flowchart is provided. The efficacy of the suggested approach is verified through simulated echo data.

Global navigation satellite system (GNSS) is widely recognised to be vulnerable to spoofing attacks. A sophisticated form of GNSS spoofing, termed distributed spoofing, transmits each spoofing signal through dedicated antennas. This technique poses significant implementation difficulty in practical scenarios, owing to challenges including diverse propagation paths, inter-node clock synchronisation and transmit-receive isolation. This article proposes a feedback node aided distributed spoofing (FNA-DS) system for executing GNSS time synchronisation attacks, enabling flexible implementation of distributed spoofing. Leveraging observations from a feedback node, the time biases and drifts of each spoofing node are estimated in real time and compensated during spoofing signal generation, ensuring false position, velocity and timing (PVT) solution accuracy and high pseudorange consistency. By dedicating the feedback node exclusively to signal reception and the spoofing nodes solely to signal transmission, the requirement for transmit-receive isolation is relaxed. To comprehensively characterise the distributed spoofing threat, a detailed performance analysis of the FNA-DS system is conducted, quantifying the impact of node position errors and time parameter estimation errors. Field experiments using a self-developed distributed spoofing prototype validate the FNA-DS system's effectiveness and expose limitations in existing direction of arrival (DoA) based anti-spoofing techniques. Collectively, this work expands the capability frontier of GNSS spoofing, advances understanding of distributed spoofing and underscores its significance as a practical GNSS security threat.