Iet Radar Sonar and Navigation最新文献

This paper presents a comprehensive survey of Direction of Arrival (DoA) estimation techniques that utilise one-bit and low-resolution data. We delve into various approaches, including direct application of quantised data to existing DoA estimators and reconstruction-based methods, such as covariance matrix reconstruction via the arcsine law and recovery of noiseless unquantised measurements. Low-resolution quantisation is increasingly essential in modern communication systems, especially massive MIMO systems, due to its benefits in terms of power consumption, cost and system complexity. One-bit quantisation, in particular, has gained significant attention in wireless communication and cellular and sensor networks. We conduct a thorough evaluation of different methods and algorithms under various scenarios to identify optimal techniques for different conditions. Our analysis includes comparisons of different performance metrics and computational complexity. We also investigate the effect of increasing the number of quantiser output levels on DoA estimation performance. Our findings demonstrate that the Lloyd-Max quantiser consistently outperforms the maximum entropy quantiser for a higher number of quantisation levels. Additionally, we compare the performance of direct use of quantised data with quantised measurement recovery approach at higher quantisation levels. Our results suggest that direct use of quantised data is generally a more efficient and effective approach in such scenarios.

Passive reconnaissance solutions receive increased interest as unjammable fibre-optic drones represent a large number of UAVs in recent military conflicts. In order to equip critical infrastructure with an early warning system against drone attacks, it seems obvious to use the local communication infrastructure as illuminator for a passive radar. In Germany and other European countries, a blackout resistant LTE network in the 450 MHz band for critical infrastructure sites is currently rolled outed or already planned. In this contribution, we provide a proof of concept and present experimental results with a LTE450-based single- and multichannel passive radar for drone detection. To ease the signal processing while achieving a clearer ambiguity-function, only the reference elements contained in the OFDM symbols are used. For removing the dominant direct path contribution from the illuminator, an ad hoc approach is used which exploits the space-time structure of the received OFDM reference elements.

In electronic warfare (EW) systems, accurate time-of-arrival (TOA) prediction for radar signals is critical for effective jamming. TOA depends on both pulse repetition interval (PRI) and radar scan patterns, which are increasingly complex due to technological advancements. Unlike prior research focusing solely on one factor, this paper proposes a machine-learning model that leverages both PRI and scan patterns to predict subsequent radar pulse TOA. The system demonstrates superior prediction accuracy and robust performance in noisy environments and under varying probabilities of detection (POD). This is achieved by separating the PRI sequence and the radar scan interval, an approach that can be applied to different system designs. The proposed method applies a filtering algorithm that separates PRI and scan sequences, feeding them into distinct LSTM models, with a splitting technique addressing missing pulses. Importantly, the model integrates the radar antenna main lobe and side lobe information to enhance jamming effectiveness. Simulation results also demonstrate that the main design concept—considering both PRI and scan type—can be used for different techniques, such as a decision tree. This approach significantly improves TOA estimation, handles diverse radar patterns and represents a valuable contribution to radar technology for improved situational awareness and operational efficiency.

This study addresses positioning errors in high-frequency (HF) over-the-horizon (OTH) localisation that arise from inaccuracies in ionospheric virtual height measurements. We propose a direct localisation algorithm based on the direct position determination (DPD) model in which the initial search range of the target is estimated using HF single-station direction-finding (SSDF). To enhance accuracy, the International Reference Ionosphere (IRI) model is combined with ionosonde data to provide priors on ionospheric virtual heights. These priors are incorporated into a single-layer mirror reflection model of the ionosphere to establish a more accurate signal propagation path, thereby mitigating errors caused by variations in virtual heights across different transmission paths. The algorithm leverages the global search capability of particle swarm optimisation (PSO) to generate high-quality initial solutions, followed by localised refinement through the Gauss–Newton method to further improve positioning accuracy. Experimental results show that, compared with traditional direct localisation methods that assume fixed virtual heights, the proposed approach reduces positioning errors by 5–25 km in typical scenarios and increases computational efficiency by more than 40% compared to the conventional exhaustive grid search method (measured in terms of computational complexity). Overall, the method provides a balanced solution for HF OTH localisation systems, effectively improving both accuracy and efficiency.

Due to the stable propagation of magnetic signals in ocean and air, magnetic detection technology has become an effective means for nonacoustic detection. The magnetic anomaly signal and shaft-rate magnetic signal radiated by underwater vehicles are currently the most effective magnetic detection signals. Existing magnetic detection methods primarily focus on studying either magnetic anomaly signal or shaft-rate magnetic signal. However, since a target can generate both of these magnetic signals simultaneously, detecting one type may lead to the neglect of the other, reducing detection accuracy. To overcome the limitations of existing technologies, this paper presents a combined detection method for magnetic anomaly and shaft-rate magnetic signals. The detection process is divided into magnetic anomaly signal detection based on orthogonal basis function (OBF) and shaft-rate magnetic signal detection based on adaptive line spectrum enhancement (ALE). Especially for the detection of magnetic anomaly signal, this paper proposes a preprocessing method based on the LOESS smoothing technique, utilising noise characteristics, and combines it with the CFAR criterion for decision-making. This approach significantly improves the detection accuracy of the magnetic anomaly signal. Finally, the simulation and experimental results show that combining magnetic anomaly and shaft-rate magnetic signals for combined detection can effectively improve the detection accuracy.

Sea clutter suppression is a hot topic for airborne radar. Space-time adaptive processing (STAP) is a useful approach to address this issue. Currently, range ambiguity is a problem to restrict conventional STAP performance. The conventional STAP cannot differentiate the signals originating from distinct ambiguous areas due to the lack of range-associated degrees-of-freedom (DoFs). Frequency diverse array (FDA) can provide the DoFs via introducing a frequency shifting between adjacent transmit elements, then FDA-STAP is developed. According to the Reed, Mallet and Brennan (RMB) rule, FDA-STAP requires more training samples in comparison with conventional STAP. However, the number of training samples is limited practically, and FDA-STAP will suffer from severe performance deterioration. To address this issue, this paper introduces a sparsity recovery algorithm, atomic norm minimisation (ANM), into FDA-STAP for clutter profile recovery, that is, ANM-FDA-STAP, thereby reducing the requirement on training samples. Numerical results verify that the ANM-FDA-STAP algorithm exhibit outstanding performance.

This work addresses the problem of multiplatform motion error calibration in an asynchronous airborne radar network with the employment of ground strong scatterers (SSs) at inaccurate locations. The multiradar time-frequency synchronisation errors as well as motion errors, including position and velocity errors, would significantly impair signal coherence of airborne radar networks, provided that a potential target can share the same complex scattering coefficient to all airborne radars distributed within hundreds of yards. Hence, the network configuration calibration under time and frequency asynchronisation is required prior to coherent beamforming. Starting from the nonlinear time of arrival (TOA) and frequency of arrival (FOA) measurement equations, we develop an iterative reweighted least squares (IRLS) algorithm to obtain the deviations in positions, velocities, instants and frequencies of multiple radars during each iteration. By adding the deviations obtained from all iterations, a final error estimation is achieved and the evaluation of multiradar parameters is more refined. During algorithm development, we apply Taylor series expansion to eliminate nuisance parameters, followed by reweighted iterations to manage the remaining nonlinearity. This approach allows us to form linear equations for estimating multiradar parameter errors. Besides, we conduct the performance analysis of our method in comparison with the theoretical Cramér-Rao lower bound (CRLB). Both theoretical derivations and simulation results confirm the effectiveness of our algorithm.

This study analyses sonar performance for underwater object detection in four regions of the Marmara Sea, using oceanographic data from the Turkish Naval Forces and open source datasets. Simulations were conducted with LYBIN acoustic modelling software across four seasons (January, May, July and October), evaluating variable-depth sonar (VDS) and hull-mounted sonar (HMS) systems for coverage and detection performance. Results identified optimal sonar coverage zones, highlighting seasonal impacts on propagation, with temperature and salinity fluctuations directly influencing performance. Seasonal stratification in the Marmara Sea generates surface ducts and shadow zones that strongly constrain HMS performance, while VDS consistently mitigates these effects. Simulations demonstrate that VDS reduces shadowed areas by 25% across all seasons and regions, extending reliable detection ranges compared with HMS. The study provides a foundation for designing efficient underwater surveillance systems in the Marmara Sea, offering insights for optimising operational strategies. Future research should explore diverse marine conditions and sonar configurations to enhance detection capabilities.

Mechanically generated sounds, common in industrial process control and surveillance, often exhibit narrowband harmonic features that manifest as line spectra in the time–frequency domain. While convolutional neural networks (CNNs) have been employed for line spectrum extraction, their performance is often hindered by the scarcity of high-quality supervised data. To address this limitation, we explore graph neural networks (GNNs), which explicitly model feature relationships. Among GNNs, graph convolutional networks (GCNs) stand out due to their computational efficiency. In this study, we introduce a GCN model enhanced with a weight tensor to effectively extract line spectral features from graph representations of mechanical sounds. Our approach is tailored for weakly supervised scenarios, where time–frequency masks are noisy and interfere with supervision. By leveraging a tensor product operation, the model projects input graphs into a multi-dimensional embedding space, facilitating the learning of diverse and discriminative representations with minimal computational overhead. Experimental results on audio and underwater acoustic datasets reveal that our method outperforms fully supervised baselines while significantly reducing computational requirements. These results underscore the efficiency and practicality of our framework for real-world acoustic processing applications.

The resonance parameters of late-time returns (LTRs) can be used as features in the identification of radar targets. However, reliable estimation of the complex frequency of each resonance is notoriously difficult. This is a result of the short duration of the LTR, its low effective signal-to-noise ratio (SNR) and the inherent sensitivity of the estimation problem. These issues are exacerbated when the radar background includes resonating clutter. We develop an effective technique for estimation the complex frequencies of a target's resonances for scenarios in which the radar can obtain multiple measurement shots of the background (clutter) alone and multiple measurement shots of the target in the presence of the background. The proposed method exploits the fact that the maximum likelihood estimator for measurements in Gaussian noise can be decomposed to estimate the complex frequencies of the resonances separately from their complex amplitudes. This enables us to decouple the estimation of the complex frequencies of the target from those of the background because the background's complex frequencies remain largely unchanged when the target is introduced. We investigate the performance of the proposed method using a radar that operates in the band of 0.5–5 GHz and employs equivalent sampling at a rate of 20 GSa/s. Proof-of-concept experiments on brass rods of known length validate the overall approach, and experiments on more complex targets in clutter demonstrate its potential for practical applications.