Iet Radar Sonar and Navigation最新文献

Marine environmental pollution, particularly from oil spills, has garnered significant attention due to its irreversible damage to marine ecosystems. Ship-borne high-frequency surface wave radar (HFSWR) holds promise for long-distance, wide-area marine environment monitoring, enabling real-time surveillance of oil pollution on the sea surface. This paper utilises two sets of ship-borne HFSWR to swiftly deploy and monitor oil spill areas through optimal deployment planning, specifically tailored for addressing oil spill incidents in designated sea surface regions. First, this paper outlines the deployment model for two sets of ship-borne HFSWR, which is based on quadrilateral monitoring areas and circular deployment regions for transmitting and receiving stations. Then, this paper presents a traversal algorithm that operates under the minimum resource parameter limit, followed by a fast algorithm derived from geometric relationships with delineating the scope of application. Theoretical and experimental results demonstrate that the proposed algorithm significantly reduces the computational complexity of the traversal algorithm while maintaining high accuracy.

Synthetic Aperture Radar (SAR) image classification using deep neural networks (DNNs) has demonstrated vulnerability to adversarial attacks, particularly black-box attacks, which rely solely on model output scores to craft effective perturbations. Despite their practical threat, defences against such attacks in SAR tasks remain underexplored. To bridge this gap, we propose a novel defence mechanism that introduces a pointwise modulation layer to enforce gradient orthogonality, thereby disrupting the gradient estimation process employed in black-box attacks. This method preserves high accuracy on clean data by maintaining logit consistency while significantly reducing attack success rates. Furthermore, the approach is computationally efficient and can be easily integrated into existing models. Extensive experiments demonstrate the effectiveness of the proposed method in enhancing the robustness of SAR classifiers against a range of black-box attack scenarios, without compromising their performance on clean data. This work contributes to the development of secure and reliable SAR-based machine learning systems for critical applications.

With the U-space revolution, drones are going to reshape both the physical space and the cyberspace of the future urban environment, also with the support of autonomy and artificial intelligence (AI). However, this revolution comes with the cost of new multi-domain risks, which may be traced back to cyber and physical threats within drone-based new entrants. A proper assessment and treatment of these risks is essential to achieve the safety and security objectives of U-space for the drone ecosystem. This will entail further research, especially for the analysis of drone intruders and for the mitigation of the related U-space impacts. This work proposes a concept for improving the U-space resilience through a novel AI-centric service, named DARS (drone attack resilience service), focused on managing unauthorised operations of intruder drones in the physical and cyber domains. DARS-related threat scenarios and risk-assessment capabilities are discussed, resorting also to modelling specific drone cyber-physical attacks. A detailed analysis of DARS AI-centric functional architecture is provided, with a survey of the potential approaches for intruder trajectory prediction and intent recognition, to be used for the next design stages. Lastly, the work provides a preliminary analysis of how the neutralisation functions could be implemented in DARS.

The SAR-ATR (Synthetic Aperture Radar - Automatic Target Recognition) system based on deep learning technology has been proven to have a target recognition vulnerability—adversarial examples, which has attracted widespread attention. However, existing adversarial sample attacks focus primarily on the image domain, neglecting the unique characteristics of SAR imaging and the challenges of transferring attacks to the physical domain. In response, we propose a physically realisable adversarial attack method based on radar imaging principles and the Attribute Scattering Centre Model (ASCM), which aims to translate perturbations from the digital image domain to modifications of physical electromagnetic parameters of radar. The ASCM method consists of three key components: (1) reconstructing the backscattered signal to physical scattering centres using ASCM, (2) establishing a minimal perturbation optimisation model under $��{\ell }_0�$-norm constraints to restrict perturbations to scattering centres, and (3) applying the Monte Carlo Method (MCM) to determine optimal adjustment points and amounts for scattering centre amplitude parameters. Experimental results demonstrate that the proposed method achieves the highest success rate of 96.25% for nontargeted attacks and 88.89% for targeted attacks, with the potential for extension to the physical domain to generate high-success-rate adversarial attack effects.

Accurate classification of radar signals remains a key challenge in automatic modulation classification (AMC), particularly in scenarios with limited training data and complex signal variations. To address this, we propose a novel hybrid neural architecture and incorporate a magnitude rescaling method for data augmentation. Specifically, our hybrid neural structure integrates a bidirectional long short-term memory (Bi-LSTM) network, a dynamic feature extraction module, and a transformer encoder in a cascaded structure. It effectively processes one-dimensional signals enhanced via the proposed random magnitude rescaling method. Experimental results demonstrate our approach achieves a competitive classification accuracy of 94.18% on the RML2016a data set and exhibits strong performance on a hardware-in-the-loop simulation dataset. The implementation of our radar signal modulation classification method, along with the related datasets, is available at: https://github.com/stu-cjlu-sp/rsrc-for-pub/tree/main/ASEFEAMC.

In this work, we generalise the popular Kalman filter and Linear Quadratic Gaussian controller for use on multi-sensor and multi-agent/-target radar systems. The state-space representation for the dynamical evolution of targets and the sensor measurements is developed here using tensors in place of vectors and matrices, producing a multilinear dynamical system. In this dynamical framework, the tensor forms of the Kalman filter and the Linear Quadratic Gaussian controller are developed, allowing the simultaneous processing of (i) the inputs of all sensors, producing the estimation of the state of all agents/targets and (ii) the determination of the optimal control actions of all agents/targets. These tools are applied to implement optimal parallel waveform design and tracking control for a multi-radar system acting on multiple agents. In the study case, examined numerically, the radars can (i) estimate the state of the agents in terms of range, angular displacement, radial and angular velocities and (ii) jointly determine the agents control inputs and the radars transmitted waveforms to minimise the control cost action and the energy of the transmitted signals.

Mulitstatic SONAR networks (MSNs) have the potential to improve the imaging quality of underwater areas due to the increased degree of freedom. The additional signal processing required for such a network of distributed SONAR nodes compared to monostatic SONAR systems is presented. An equalisation technique for bistatic processing of coherent and incoherent setups as well as elliptical linear interpolation techniques are described to provide adequate imaging. Methods to merge the gathered data before detection and tracking algorithms are listed. A real-time modular SONAR imaging system is equipped with the presented algorithms. Simulations as well as measurements in a real harbour environment are performed, and the results show the capabilities as well as the advantages and drawbacks of such MSNs in contrast to conventional SONAR systems.

Interrupted sampling repeater jamming (ISRJ), as a typical intra-pulse coherent interference, seriously endangers the performance of radar. To effectively suppress ISRJ, the paper proposes an ISRJ suppression algorithm combines grey entropy with multidimensional filtering. Firstly, the grey entropy algorithm from image segmentation is introduced to extract interference-free segments enhancing the accuracy of intrapulse interference-free segments extraction. Secondly, a fractional-order domain filter is constructed using the interference-free segments to conduct the primary suppression of interference in the fractional-order domain. Then, a time-frequency filter is constructed based on dechirped interference-free segments to suppress the interference again in the frequency domain. Finally, leveraging the linear relationship between frequency and distance of target in dechirped radar received signals, and the pulse compression results after interference suppression are obtained. Simulation experiments validate the effectiveness and robustness of the proposed algorithm. Compared with the same type of algorithms, the proposed algorithm demonstrates improved interference suppression performance under low signal-to-noise ratio (SNR) or high jamming-to-signal ratio (JSR) conditions.

In the context of electronic warfare, cross-eye jamming is a powerful technique designed to induce angular error in radar systems. This study explores the potential of phase-conjugating (PC) arrays as novel and effective alternative to the traditional Van Atta (VA) arrays. An analytical framework for PC arrays is developed, extending the previous theoretical foundation of cross-eye jamming techniques, and is validated through simulations. A tolerance analysis of the system is provided by deriving analytical expressions for the acceptable phase and amplitude mismatches as a function of the angle deception capability measured by the angle factor. The impact of the skin return is examined with a refined metric based on the 95th percentile, which allows to assess the minimum JSR requirement for a reliable countermeasure. This shows again a superior performance for the PC implementation compared to the VA.

In this paper, we propose a three-element nonuniform linear array (NULA) design strategy for an OFDM-based radar system installed on a moving platform. The need to suppress Doppler-spread clutter induced by platform motion requires the use of space–time cancellation approaches, such as displaced phase centre antenna (DPCA) or space–time adaptive processing (STAP). In particular, the recently introduced thresholded-DPCA reciprocal filter (T-DPCA-RF) was demonstrated to be especially effective in detecting moving targets within a stationary scene using OFDM waveforms on transmit. This is mostly due to the T-DPCA-RF capability to process the received signals in batches of arbitrary length, independent of the OFDM framing structure. The three-element receiving NULA design strategy for multi-channel DPCA processing proposed in this work builds upon this feature of the T-DPCA-RF. Although previous research studies have demonstrated the advantages of NULA in multi-channel DPCA, the antenna positions were chosen based on a heuristic criterion. The NULA design strategy proposed in this paper provides guidance for antenna positioning by enforcing a constraint on the DPCA filter response that prevents blind velocity effects within a specified target velocity range of interest. The effectiveness of the multi-channel DPCA detector using the NULA designed with our strategy is validated through experimental data, extending the scope of previous studies in this area.