Iet Radar Sonar and Navigation最新文献

Current radar signal deinterleaving methods primarily utilise direction of arrival, radio frequency and pulse repetition interval to separate signals from different radars. However, their effectiveness becomes limited in scenarios with highly overlapping radar parameters. Amplitude can provide supplementary discrimination for many deinterleaving problems, especially for mechanically scanning radars. The amplitude of signals intercepted from such radars exhibits a continuous parabolic-like variation characteristic. Leveraging this, we construct a function-adapted Gaussian mixture model to characterise the joint distribution of pulse time-of-arrival and amplitude for interleaved pulse trains, thereby transforming radar signal deinterleaving into a parameter estimation and clustering problem. Furthermore, we employ active function cross-entropy clustering (afCEC) to solve the problem and innovatively embeds the sequential andom sampling consensus within the afCEC framework to mitigate its sensitivity to initial values and avoid local optima. This achieves preliminary clustering of the time-amplitude data, effectively decomposing the originally pulse point cloud into multiple subclusters conforming to mixture model components. Building upon this over-segmentation result, we design a merging strategy based on pulse cluster continuity, enabling automatic deinterleaving without prior knowledge of radar quantity. Simulation results demonstrate that the proposed method achieves superior deinterleaving performance in complex electromagnetic scenarios, outperforming state-of-the-art approaches.

This paper proposes a dual-backbone feature fusion approach to address the few-shot class imbalance problem in specific emitter identification. First, employ the Weighted Random Sampler algorithm to dynamically calculate sampling weights for data preprocessing; Subsequently, by fusing the two single-backbone networks of ResNet50 and ConvNeXt-Tiny, we overcome the hierarchical limitations of their independent feature capture, thereby achieving few-shot multi-scale and multi-level feature extraction while enhancing fine-grained features; Furthermore, we embed Efficient Channel Attention into the dual-backbone networks to achieve dynamic modelling of inter-channel correlations. This method enhances feature attention on ‘minority class’ samples while suppressing redundant information, thereby improving the accuracy, stability and robustness of specific emitter identification under imbalanced data conditions. Experimental results validated on a public Bluetooth dataset demonstrate that the proposed method achieves at least a 6% improvement in recognition rate compared to other commonly used algorithms.

The line spectrum from ship-radiated noise is a critical feature for passive sonar to detect underwater acoustic targets. However, due to weak target strength as well as severe propagation attenuation and oceanic ambient noise, the signals received by passive sonars generally manifest low signal-to-noise ratio (SNR), strong nonstationarity and overwhelmed Doppler-shifted line spectrum. These challenges deteriorate the performance of conventional Cohen class time frequency distribution (CCTFD) methods in capturing the fine spectral feature of such signals. To overcome these difficulties, this research proposes an improved Cohen-class method, termed ambiguity function-instantaneous autocorrelation function joint filtering Wigner–Ville distribution (AIJF-WVD). First, this study analyses how standard CCTFD's cross-term suppression mechanism degrades time-frequency resolution/concentration in time-frequency distribution (TFD) when processing multicomponent Doppler-shifted signals. Departing from conventional framework of cross-term suppression via two-dimensional low-pass filtering along both frequency-shift dimension and time-delay dimension in ambiguity function (AF) domain, AIJF-WVD presents a novel joint filtering approach that consists of designing one-dimensional finite impulse response (FIR) filter solely along frequency-shift dimension in AF domain (while maintaining time-delay dimension unchanged) as well as subsequent one-dimensional low-pass filtering along time dimension in instantaneous autocorrelation function (IAF) domain based on the designed filter. Therefore, this novel method enhances TFD performance of cross-term suppression and frequency resolution simultaneously while maintaining low computational complexity. Then, the performances of various CCTFD methods are quantitatively assessed using mean structural similarity (MSSIM), normalised Rényi entropy (NRE), half-power bandwidth (HBW) and mean runtime. Finally, the global spectral estimation accuracy of Doppler-shifted tonals is evaluated through tracking deviation analysis. Compared to conventional CCTFDs, AIJF-WVD exhibits superior robustness and adaptability in low-SNR background noise as evidenced by processing both simulated signals and ship-radiated noise from sea trials. Furthermore, the refined approach is also validated to significantly improve cross-term suppression, time-frequency concentration and computational efficiency characteristics while preserving frequency resolution and superior tonal trajectory tracking capability for passive sonar.

This paper presents a novel approach to synthesising radar echoes for unmanned aerial vehicle (UAV) anticollision systems, specifically focusing on the challenges posed by high-rise architectural landmarks in urban environments. We employ a Monte Carlo method to generate synthetic radar data that accurately reflects the statistical properties of real-world radar echoes, derived from data collected using a custom-designed X-band radar. Our methodology involves the probabilistic modelling of radar echoes for three distinct classes: large-scale arena building, sky-scraping slender spire and background noise, using kernel density estimation (KDE). This approach allows for the creation of a large database of synthetic radar signatures essential for training and validating machine learning algorithms intended for use in UAV collision avoidance systems. The results demonstrate the efficacy of our method in preserving the statistical characteristics of real radar data, enabling the generation of high-fidelity synthetic echoes that can significantly enhance the development and testing of UAV navigation and obstacle avoidance systems.

This paper presents a comprehensive review of advancements in road surface classification technology utilising automotive microwave sensors, covering both active radar and passive radiometry, along with data analysis techniques. Accurate knowledge of road surface type and condition is crucial for improving driving safety, especially in the pursuit of fully autonomous driving. The paper begins with a comparative analysis of different sensing technologies, including microwave, optical, LIDAR and sonar sensors. It subsequently highlights the distinct advantages of microwave sensors, particularly in scenarios with low visibility, where other sensing methods are not sufficiently effective. The analysis of road surface classification methods using radar or radiometer data includes both technical aspects (signal parameters, sensor type, position and number of antennas, signal polarisation, etc.) and classification algorithms. These include analysing backscattered or emitted signal parameters based on specific criteria and making decisions based on this analysis or using statistical classification methods (e.g., k-nearest neighbours, support vector machines, neural networks). The paper also discusses the current state of the field and explores future directions and potential advancements in surface classification technology.

This paper presents a scalable solution for the coordinated control of swarms of UAVs operating in complex three-dimensional environments with no-fly zones and obstacles. The proposed approach is based on a priority-driven behaviour structure implemented using the null-space behavioural (NSB) technique. Each UAV dynamically adapts its behaviour according to a predefined task hierarchy including collision avoidance, obstacle-avoidance, formation maintenance and target achievement. By projecting lower priority control actions into the null space of higher priority tasks, the method ensures conflict-free execution of tasks with respect to the fulfilment of the overall mission. The control architecture has a fully decentralised structure and is designed to maintain performance and scalability as the number of UAVs increases. The results of several experimental tests have demonstrated the effectiveness of the proposed method in maintaining formation and achieving mission objectives in constrained environments.

Radar signal deinterleaving is challenging due to dense pulse interleaving and diverse PRI modulations. This work reframes it as a multi-class classification problem, treating each emitter as a distinct class. Existing methods suffer from error accumulation in sequential processing or fail to integrate parallel classifier outputs effectively. To address these flaws, we propose OvR-C-MC, a complete one-vs.-rest (OvR) decomposition framework. Key innovations include (1) true multi-class decomposition: parallel binary classifiers maintain the OvR paradigm's theoretical guarantees, avoiding error propagation in sequential binary classifiers. We integrate classifier outputs with a prioritisation mechanism to resolve conflicts, ensuring a more robust and accurate classification process than existing methods. (2) HMC-based OvR classifier: hidden Markov chains (HMCs) form the basis of each binary classifier, enabling support for any regularised PRI modulation types through state transition property and providing a more comprehensive solution. Experimental results demonstrate that the proposed method significantly outperformed existing approaches, particularly in dense interleaved scenarios, whereas maintaining compatibility with diverse PRI modulation types. Thus, the proposed systematic perspective for radar signal deinterleaving provides robust support for radar signal reconnaissance in complex electromagnetic environments.

Large-scale sensor scheduling for multisource localisation is a critical technology in wireless communication control and navigation systems. Most existing heuristic algorithms face challenges in adapting to large-scale sensor systems. To overcome this limitation, we utilise the self-learning capabilities of deep reinforcement learning (DRL) to enable multisource localisation. This paper proposes a large-scale sensor scheduling algorithm based on the multiagent proximal policy optimisation (LSS-MAPPO) framework. We develop a multisource localisation model based on time difference of arrival (TDOA) and design a reward function grounded in the Cramér–Rao lower bound (CRLB). Our approach integrates multihead attention layers into MAPPO to improve the performance of the algorithm. In large-scale sensor scheduling systems, multihead attention mechanisms can effectively handle the high-dimensional state space associated with multisource localisation in multiagent environments. Experimental results under different environments show that LSS-MAPPO improves localisation accuracy compared to the baseline in large-scale sensor scheduling. Notably, it maintains robust performance under partial observability, addressing critical gaps in large-scale dynamic sensor scheduling.

This paper addresses the measurement quality optimisation problem in multiple resolvable group target tracking (MRGTT), proposing an improved MRGTT algorithm based on rigid-body similarity model and optimal measurement fusion. Firstly, a unified framework for group target motion and measurement description is established by introducing the rigid-body similarity model. Secondly, an optimal measurement fusion scheme derived from the minimum variance criterion is proposed, which achieves 2.5–2.8 times faster convergence speed compared to traditional equal-weight methods. Furthermore, a complete algorithm flowchart integrating group structure construction, measurement optimisation and intensity update is designed. The proposed method demonstrated exceptional adaptability across different random finite set (RFS) filtering frameworks, including Gaussian mixture probability hypothesis density (GM-PHD) and Poisson multi-Bernoulli mixture (PMBM). Simulation results show that the proposed method achieves significant improvements in OSPA distance over traditional algorithms, with 45% improvement in the GM-PHD implementation and robust performance across diverse scenario complexities in the PMBM framework, including large-scale manoeuvring scenarios. This framework-agnostic approach provides a versatile solution for resolvable group target tracking in complex scenarios such as group splitting, merging and high-clutter environments.

The utilisation of staggered pulse repetition intervals (PRIs) in pulse-Doppler (PD) radar systems is instrumental in expanding the unambiguous Doppler interval, which is traditionally confined by the constraints of uniform PRIs, and in enhancing the electronic countermeasures capabilities of the radar. However, the presence of high Doppler sidelobes emerges as a principal impediment to the practical deployment of such systems. In this paper, we introduce a novel approach to accurately estimate the Doppler frequencies of targets in PRI-staggered radar systems, which integrates gradient descent and orthogonal matching pursuit algorithms to effectively reconstruct the Doppler profiles of targets with arbitrary velocities. The simulation results demonstrate the method’s effectiveness and robustness, indicating its promising suitability for practical application within PD radar systems.