Iet Radar Sonar and Navigation最新文献

Transionospheric SAR autofocus is a variational algorithm designed to circumvent the deficiencies of conventional autofocus techniques in correcting the distortions of spaceborne SAR images due to ionospheric turbulence. It has demonstrated superior performance in a variety of computer-simulated imaging scenarios. In the current work, we conduct a systematic statistical analysis of transionospheric SAR autofocus aimed at corroborating its robustness and identifying limitations and sensitivities across a broad range of factors that affect the autofocus performance. We employ the range-compressed domain representation where the target reflectivity, antenna signal, and the phase screen depend only on the azimuthal coordinate. The three main factors included in the study are the levels of turbulent perturbations, clutter, and noise. We use the normalised cross correlation (NCC), integrated sidelobe ratio (ISLR), and peak desynchronisation (PD) as a-posteriori performance metrics. A key objective of the current analysis, beyond assessing the autofocus performance, is to identify the directions of how to further improve the algorithm, in terms of both the quality of focusing and associated computational cost.

In response to the complex scenario where multiple jammers navigate through a netted radar system (NRS), this study presents an optimised allocation algorithm for cooperative jamming resources, namely the Multi-Agent Jamming Resource Allocation (MJCJRA) algorithm, which is based on multi-agent deep reinforcement learning. Initially, the research develops a target fusion detection probability function and a global performance index optimisation function, which are tailored to the specific jamming and radar detection models of the scenario. Subsequently, the multiple jammers are mapped into a multi-agent system with a greedy strategy employed to generate targeted rewards for the jamming agents, enhancing their learning efficiency and performance. The study culminates in the design of evaluation and mixed-strategy networks for the jamming agents. It utilises an exponential mean shift method for soft updates of the target network, adopts priority experience replay and importance sampling methods, and incorporates reward centring into the loss function for network updates. Experimental findings demonstrate that MJCJRA algorithm significantly surpasses the baseline method, the particle swarm optimisation (PSO), the snow ablation optimiser (SAO), the multi-agent deep deterministic policy gradient (MADDPG) and multi-agent proximal policy optimisation (MAPPO), effectively diminishing the detection capability of NRS.

In the experimental test of the radar system, it is extremely important to build a realistic experimental environment for electromagnetic target testing, which is often realised by the radar target simulation technology. Corner reflectors often simulate radar RCS features by the spatial arrangement; however, their electromagnetic characteristics are solidified and the RCS features differ from those of real targets. This paper proposes a target RCS simulation method based on AFSS echo power modulation. The core idea is to use AFSS reflection modulation to dynamically regulate the target power information to achieve flexible and fast control of the target radar RCS characteristics. Based on the AFSS echo power modulation model, the theoretical relationship between the modulation parameters and the RCS value is deduced, and the duty cycle of the scattering state control signal is used as an adjustable variable to realise the simulation of the dynamic RCS sequence of the mid-range target. The RCS simulation experiment is carried out based on the target measured data, and the simulation effect is analysed in terms of statistical characteristics and similarity coefficients. The simulation results show that the statistical characteristics of the simulated RCS sequence and the target RCS sequence are very close to each other with the mean value and standard deviation within 1 dBsm and the extreme value and extreme deviation within 3 dBsm. The method is of great significance in the field of radar system tests and electronic protection.

Low-correlation sidelobes are critical for spectrally compatible waveforms in multiple-input multiple-output (MIMO) radar systems. This study presents a novel algorithm for designing spectrally compatible waveforms for MIMO radar with low auto- and cross-correlation sidelobes to enhance weak target detection capability. We adopt the minimum auto- and cross-correlation-weighted integrated sidelobe level (ACWISL) as the objective function. Under spectral and constant modulus constraints, we formulate a nondeterministic polynomial time (NP)-hard problem. To solve this problem, we combine the block successive upper-bound minimisation (BSUM) and majorisation-minimisation (MM) algorithms to develop the BSUM-MM algorithm. The original problem is decomposed into several independent subproblems, which are iteratively solved using the MM algorithm. We also employ the fast Fourier transform (FFT) to significantly accelerate the calculation. Simulation results demonstrate that the proposed algorithm is superior in terms of computational efficiency and sidelobe performance.

The existing radar emitter modulation recognition methods typically assume that the data distribution across different types is balanced. But in reality, the number of signals of various kinds often follows a long-tail distribution, leading to model overfitting for the head classes and underfitting for the tail classes. As a result, the overall recognition performance of models under such data imbalances is suboptimal. A long-tail distribution automatic modulation recognition method based on decoupled training is proposed to address this issue. Based on the ResNeXt network, the proposed method decouples the model training process into two stages: a feature extraction phase under the imbalanced dataset and the classifier learning stage under a balanced dataset. The classifier boundary is fine-tuned by $��\tau �$-normalization method. Compared to existing radar emitter modulation recognition frameworks, the proposed method achieves an overall recognition accuracy of 86.8% when the data imbalance factor is 0.01, surpassing the baseline model by 5%, and improves the performance of radar emitters modulation recognition in the real environment.

For radar target recognition in high-resolution range profiles (HRRP) under low signal-to-noise ratio (SNR) conditions, traditional methods typically involve denoising followed by recognition. However, these methods struggle with complex noise. To enhance HRRP information extraction, this paper proposes an integrated approach combining noise reduction and recognition. First, the short-time Fourier transform (STFT) is improved with a complex Gaussian window to enhance time-frequency resolution. Then, multi-scale analysis is applied by introducing scale values to better capture detailed target features. Differential operations are used to highlight scattering points and edges, improving recognition accuracy. A convolutional neural network (CNN) is employed to extract multi-level features for target recognition. Experimental results on a simulated HRRP dataset from the U.S. Air Force Research Laboratory (AFRL) demonstrate the proposed method's superior performance. It outperforms traditional methods in both accuracy and robustness, offering stronger noise resistance and better utilisation of HRRP's rich features, providing an effective solution for radar target recognition tasks.

A joint power and waveform resource allocation algorithm is proposed for netted radar integrated search and tracking tasks with low probability of intercept. For the search and tracking performance, the detection probability and the posterior Cramér-Rao lower bound of the target are adopted separately. The optimization problem of joint resource allocation is solved by controlling the radar node selection, power allocation, waveform selection, and pulse duration, to minimise the total power of the netted radar while meeting the search and tracking performance for a given target. The intelligent optimization methods are used to solve the problem, and the effectiveness of the proposed method is verified by simulation.

Ionospheric scintillations, arising from variations in phase/amplitude of radio signals traversing the ionosphere, pose significant challenges to Global Navigation Satellite System (GNSS) positioning, particularly in low-latitude regions. This paper proposes a Rao-Blackwellized Particle Filter (RBPF) integrated with a Markov chain model to comprehensively characterise and mitigate the impact of ionospheric scintillation on GNSS positioning. Unlike traditional methods, the Markov-RBPF framework offers enhanced versatility in assessing scintillation dynamics both spatially and temporally, allowing for precise modelling of scintillation evolution over varying nighttime hours and months of the year. Through simulations, the authors demonstrate the superior performance of the proposed Markov-RBPF compared to conventional Extended Kalman Filters (EKF), with position root-mean-square errors below 2 m in a scenario of strong scintillation events in October 2014. This showcases its robustness and versatility in improving GNSS positioning accuracy amidst challenging ionospheric conditions.

There is significant interest in multistatic SAR image formation, due to the increased development of satellite constellations and UAV swarms for remote sensing applications. The exploitation of the finer resolution and wider coverage of these geometries has been shown to reduce the often-impractical data collection requirements of 3D SAR imagery; this offers advantages such as improved target identification and the removal of layover artefacts. This paper presents a novel polarimetric generalisation of the SSARVI algorithm, which was previously developed to exploit sparse aperture multistatic collections for 3D SAR image formation. The new algorithm presented here, named the PolSSARVI algorithm, combines polarimetrically weighted interferograms for determining the 3D scatterer locations from sparse aperture polarimetric collections. The bistatic generalised Huynen fork polarimetric parameters are then determined for the multistatic PolSSARVI 3D SAR renderings. This new approach was tested on both simulated and experimental data. Experimental imagery was formed using measurements from the Cranfield GBSAR laboratory.