Iet Radar Sonar and Navigation最新文献

For the issue of configuration difficulty in maintaining linear formations based on the same orbital plane for distributed space-based coherent aperture radar (DSCAR), it is necessary to modify the linear formation model into an arc formation model. This article derives the steering vector and joint pattern expressions for DSCAR based on uniform arc formation, and designs a segmented inertial factor (IF) particle swarm optimization (PSO) to seek the optimal solution for non-uniform spacing and random yaw angle in non-periodic geometric distribution. Simulation analysis shows that the combination of non-uniform spacing and random yaw angle in non-periodic geometric formations can achieve lower peak side lobe level (PSLL) compared to single non-uniform spacing and single random yaw angle but with wider beamwidth spread. Additionally, the segmented IF PSO proposed in this article balances convergence more quickly in the early stage of the search process and improves convergence speed to approach the optimal value (OV) in later stage. Compared with other IF PSO, it has better convergence speed and accuracy.

This paper investigates a distributed shipborne high-frequency hybrid surface-surface wave radar (HFHSSWR) model that combines shared sky wave paths with distinct shipboard surface wave paths. This model improves target localisation accuracy and overcomes the limited aperture of a single shipboard array. A weighted least squares (WLS) positioning algorithm based on a Gaussian Markov random field (GMRF) is proposed for the model. The algorithm converts the geodetic coordinates of measurement stations to Cartesian coordinates, then estimates the initial target position using bistatic range (BR) and time difference of arrival (TDOA) measurements. An iterative refinement approach is employed to mitigate discrepancies between spherical and planar models, utilising ionospheric altitudes extrapolated through a GMRF for enhanced positioning accuracy. Finally, target coordinates are converted back to geodetic form. Simulations indicate that this approach achieves higher positioning accuracy than standard WLS positioning algorithm.

Doppler-tolerant waveforms are some of the most common radar waveforms used in practice. However, their deterministic and repetitive nature impedes control of mutual interference when multiple radars operate in close proximity. Noise radar technology may address this problem but is not Doppler tolerant. In this study, we design a machine learning radar detector capable of Doppler-tolerant performance with noise waveforms. The detector is implemented as a feedforward multilayer neural network. We show that the detector may be trained to operate with one-bit data. Further, to evaluate the proposed detector's performance, we derive a closed-form expression of the receiver operating characteristic (ROC) for one-bit detection of a Swerling 1 target using the square-law detector under the assumption of low signal-to-noise ratio (SNR). Numerical results demonstrate that the proposed machine learning detector, when suitably trained, is capable of operating with Doppler tolerance over a wide range of Doppler shifts.

To overcome the limitations of existing methods in processing continuous acoustic signals—particularly issues related to modal aliasing and the constraints of Pekeris waveguide applications—this study proposes a depth estimation approach for continuous acoustic targets using hydrophone linear arrays. A horizontal linear array, designed to meet the resolution requirements of the F–K transform, is deployed to receive continuous acoustic signals. Environmental parameters are incorporated to fit the sound speed profile, and modal time-delay differences are calculated based on normal mode propagation models. Temporal compensation is then applied to each modal component of the received signals across array elements. The corrected signal matrix undergoes a bidirectional F–K transform transformation into the frequency–wavenumber domain, allowing for clear separation of the normal modes of continuous signals. Frequency–wavenumber curves are characterised based on the sound speed profile, and binary mask filters are designed to extract modal energy. Finally, a depth estimation matching function is constructed to facilitate energy search and matching. Simulation results indicate that the proposed method achieves depth estimation errors of less than 5% for 10-s broadband acoustic signals under negative sound speed profiles and real shallow-sea waveguide conditions. The method demonstrates improved stability and applicability in variable sound speed environments, offering greater practical value for real-world shallow-sea scenarios.

Passive target motion analysis (TMA) is traditionally performed using angle-only measurements, which requires the own-ship to execute a manoeuvre to make the tracking system observable. These manoeuvres are burdensome for the naval community. In contrast, this work explores underwater TMA by incorporating time delay and Doppler frequency measurements along with angle data, eliminating the need for own-ship manoeuvre and improving estimation accuracy. Measurement noises are assumed to follow a non-Gaussian distribution, and maximum correntropy (MC)-based Bayesian filtering framework is adopted to solve the problem. Furthermore, the own-ship's position is inherently uncertain due to navigation errors, and this work addresses the uncertainty by modifying the measurement noise covariance matrix within the estimation framework. Simulation results demonstrate that the proposed methodology achieves improved tracking performance in terms of root mean square error (RMSE) and $��\%�$ track loss compared to existing state-of-the-art MC Kalman filtering approaches.

In the imaging process of SAR, the secondary phase mismatch can cause image defocusing. In this paper, a suppression jamming method against SAR based on two-dimensional (2D) phase mismatch is proposed through a designed jammer system. By extracting and resampling the intercepted radar signal through the designed jammer, the bandwidth of the linear frequency modulation (LFM) signal can be changed, which causes defocusing in the range dimension after matching filtering. Azimuth phase mismatch is achieved through velocity mismatch, which leads to azimuth defocusing after azimuth matching filtering. Efficient coverage of multiple dispersed important regions can be achieved by adjusting the parameters of jamming targets reasonably, such as modulation bandwidth, azimuth velocity, jamming positions and jamming power. Theoretical analysis is conducted on the implementation of the algorithm and its 2-D controllability in terms of jamming location and jamming area, as well as the required jamming power. The correctness of the theoretical model is verified by simulation results of spaceborne SAR. This method is quite simple to implement and can achieve efficient coverage of multiple dispersed targets, providing a basis for the implementation and application of SAR jamming in active radar responders.

Deep learning (DL) architectures, although being employed in widespread applications, often raise concerns about their trustworthiness due to their opacity in their decision-making processes. Explainable AI (XAI) emerges as a promising solution to mitigate these concerns by providing interpretable rationales for DL network outputs. In domains where risk tolerance is minimal, ensuring trustworthy predictions is essential. This study introduces expmax, a new classifier rooted in XAI principles, designed for multiclass classification problems using convolutional neural network (CNN) architectures. The key strength of expmax, compared to the conventional softmax, lies in its ability to evaluate the model's focus on salient features of targets rather than being distracted by unrelated patterns from the background. This characteristic allows expmax for increased resilience, especially in scenarios with adversarial samples, where conventional classifiers may fail to correctly recognise the target class. The methodology behind expmax is based on fitting a regressor with features that are extracted from the training dataset using the SHapley Additive exPlanations (SHAP) algorithm, along with a target mask area detection algorithm. By using the SHAP-based extracted features, expmax reduces vulnerabilities to perturbations introduced by adversarial inputs. The method is validated on the MTARSI dataset for aircraft recognition in remote sensing images.

The growing complexity of the electromagnetic environment makes accurate radar anti-jamming performance evaluation essential for assessing the effectiveness of electronic countermeasure systems. In jamming scenarios, smaller measurement errors in range indicate a radar's enhanced ability to counteract interference. This paper presents a novel approach to radar anti-jamming performance evaluation based on statistical models of radar cross-section (RCS) fluctuations. Assuming that the RCS follows one of several distributions—Swerling I–II, Swerling III–IV, or Rayleigh—we derive the corresponding distributions of radar parameter measurement errors. In our model, the measurement error is assumed to follow a conditional Gaussian distribution, with its standard deviation modelled as a random variable dependent on both the RCS and the signal-to-interference ratio (SIR). This formulation establishes a quantitative relationship between measurement error, SIR, and RCS, and enables derivation of the error's probability density function (PDF). Consequently, we obtain a novel expression for the radar anti-jamming rate. We compare this model to two conventional approaches: one that assumes a constant error variance across all target ranges and another that assumes a fixed variance that varies with target ranges but without incorporating distributional uncertainty. The proposed Error Distribution Estimation (EDE) model leverages the full probability distribution of measurement errors together with real-time parameter error data fusion. This integration provides a more continuous and nuanced evaluation of radar anti-jamming performance, potentially leading to more reliable assessments under a range of operating conditions.

The advancement of intelligent unmanned underwater systems demands enhanced multifunctionality, flexibility, and an open architecture for sonar equipment. To address the demands of underwater detection, this paper proposes a software-defined sonar (SDS) architecture featuring a terminal-plus-centre design tailored for unmanned underwater systems. This proposal draws on the foundational concepts of SDS and the architecture of software-defined radar. The performance parameters of integrated SDS have been preliminarily designed and analysed for underwater acoustic imaging, depth measurement, and velocity measurement. The feasibility of the proposed SDS architecture is validated through an instance analysis that combines centralised hardware with component-based software. In the future, SDS has the potential to substantially elevate the intelligence level of unmanned underwater systems.

Traditional adaptive beamforming techniques focus solely on optimising the excitation weights of array elements while neglecting the critical influence of element positioning on beamforming performance. To enhance array degrees of freedom and achieve superior beamforming capabilities, this paper proposes a novel joint optimisation method that simultaneously adjusts both element positions and excitation coefficients, targeting maximum output signal-to-interference-plus-noise ratio (MaxSINR). Under the minimum variance distortionless response (MVDR) framework, we derive and analyse the theoretical relationship between output SINR and array configuration. We reformulate the sparse array design as a binary integer optimisation problem by introducing a position selection vector. The solution is efficiently obtained through our enhanced hybrid algorithm, which combines improved binary grey wolf optimisation with genetic algorithm (IBGWO-GA). Compared with the traditional beamforming method, the proposed algorithm can effectively improve the degree of freedom of the array position and realise interference suppression under underdetermined conditions. The optimal design of sparse linear array and sparse planar array in simulation experiments verifies the effectiveness of the proposed method.