Iet Radar Sonar and Navigation最新文献

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.

Space-time adaptive processor (STAP) has been widely used in the GNSS antenna array anti-jamming receiver. The cost of STAP algorithms, including the minimum variance distortion-less response (MVDR) algorithm and power inversion (PI) algorithm, is introducing measurement errors. However, there is no systematic answer to the principle of error introduction, the magnitude of error and its influence on single point position (SPP) and real-time kinematic (RTK). We have conducted a systematic study on the above-mentioned issues. Firstly, the principle of error introduction was theoretically studied. Then, a large number of simulations were conducted to evaluate the magnitude of the error. Finally, simulated errors are introduced into the B1I and B3I real measurements to implement SPP and RTK to evaluate the influence of the STAP algorithms on SPP and RTK. Results show that for SPP, the influence of STAP algorithms on the B1I + B3I dual-frequency ionosphere-free combination SPP is larger than that on the B1I single-frequency SPP; for RTK, the influence of STAP algorithms on the B1I + B3I dual-frequency uncombined RTK is smaller than that on the B1I single-frequency RTK. In addition, the influences of the MVDR algorithm on SPP and RTK are smaller than those of the PI algorithm.

Multi-agent deep reinforcement learning (MARL) for self-driving vehicles aims to address the complex challenge of coordinating multiple autonomous agents in shared road environments. MARL creates a more stable system and improves vehicle performance in typical traffic scenarios compared to single-agent DRL systems. However, despite its sophisticated cooperative training, MARL remains vulnerable to unforeseen adversarial attacks. Perturbed observation states can lead one or more vehicles to make critical errors in decision-making, triggering chain reactions that often result in severe collisions and accidents. To ensure the safety and reliability of multi-agent autonomous driving systems, this paper proposes a robust constrained cooperative multi-agent reinforcement learning (R-CCMARL) algorithm for self-driving vehicles, enabling robust driving policy to handle strong and unpredictable adversarial attacks. Unlike most existing works, our R-CCMARL framework employs a universal policy for each agent, achieving a more practical, nontask-oriented driving agent for real-world applications. In this way, it enables us to integrate shared observations with Mean-Field theory to model interactions within the MARL system. A risk formulation and a risk estimation network are developed to minimise the defined long-term risks. To further enhance robustness, this risk estimator is then used to construct a constrained optimisation objective function with a regulariser to maximise long-term rewards in worst-case scenarios. Experiments conducted in the CARLA simulator in intersection scenarios demonstrate that our method remains robust against adversarial state perturbations while maintaining high performance, both with and without attacks.

The coordinated deployment of multi-sensor systems significantly enhances group target detection capabilities, yet persistent tracking remains challenging due to inherent limitations in single-sensor field of view (FoV) coverage. This paper proposes a novel labelled multi-Bernoulli (LMB) filter for resolvable group target (RGT) tracking under the centralised fusion (CF) framework, abbreviated as the CF-LMB-RGT filter. The proposed method introduces the virtual leader kinematic model to capture intra-group motion constraints and incorporates group structure undirected graph into the LMB recursion for interaction prediction. A key innovation lies in the Kullback–Leibler divergence minimised fusion rule that optimally integrates local posteriors within joint FoV regions while explicitly modelling common FoV overlaps, enabling complementary information fusion across nonoverlapping sensor FoVs. Simulation results demonstrate that our method achieves impressive tracking accuracy for RGTs by integrating information from all sensors.

The fast simultaneous localisation and mapping (FastSLAM), utilising the Rao-Blackwellised particle filter, provides a robust navigation solution in urban environments. Ensuring the integrity of FastSLAM is critical for the safety of autonomous driving applications. Our previous work proposed an empirical integrity risk evaluation method for nominal conditions and a probabilistic bound using PAC (probably approximately correct)–Bayesian theory. However, it was limited by overly conservative risk estimates and a lack of consideration for fault conditions. This study introduces a refined integrity evaluation framework with three main contributions. First, a modified weighting and resampling technique is proposed to reduce conservatism in empirical risk without compromising estimation accuracy. Second, a fault monitoring method is introduced to detect and isolate control input faults during the dynamic update step. Third, a conservative integrity risk evaluation approach is developed for FastSLAM to account for data association faults using probabilistic modelling. Simulation results show that the proposed methods significantly improve integrity performance under both nominal and faulted scenarios.