Iet Radar Sonar and Navigation最新文献

A conditional generative adversarial network (CGAN) framework is proposed to address the issue of incomplete circular synthetic aperture radar (CSAR) azimuthal information due to motion errors. Specifically, the authors propose a novel CGAN architecture that can control the azimuth angle for arbitrary angle generation, capable of complementing missing CSAR sub-aperture information. The network incorporates angular labels for various scenarios and integrates a dynamic region-aware convolution (DRconv) module. Additionally, to counteract the common challenge of mode collapse in GAN training, a mode seeking regularisation technique is innovativrly introduced into the authors’ loss function. The efficacy of the proposed network is rigorously tested using both the MSTAR dataset and an X-band SAR dataset. The results demonstrate that the authors’ network can generate high-fidelity SAR images with controllable azimuths, closely resembling authentic images. Furthermore, the proposed method excels in complementing missing CSAR sub-aperture information, effectively supplying the lost angular information due to motion errors. A new technical approach for SAR image generation is not only offered but it also has the potential to significantly expand SAR datasets. This advancement is expected to enhance the quality and utility of SAR imagery in applications such as surveillance, reconnaissance, and environmental monitoring.

The classification and recognition of underwater targets by an active sonar system remain challenging and complex. Traditional methods have limited classification performance in time and spatially varying ocean channels. An active sonar target recognition method is proposed based on multi-domain transformations and an attention-based fusion network. Initially, the active target echo undergoes time-frequency analysis, auditory signal processing, and matched filtering to represent target attributes in joint spatial-time-frequency domains. Subsequently, multiple attention-based fusion models fuse the multi-domain transformations either early or late in the processing stages. An attention module further enhances significant feature channels through adaptive weight assignment. Experiment results demonstrate that the recognition accuracy of active sonar echoes using multi-domain transformations improves significantly compared to that of single-domain methods, with an increase of up to 10.5%. The incorporation of multiple transformation domains provides complementary information about the target, thereby enhancing the network's representation ability, especially with limited data samples. Furthermore, the findings indicate that feature fusion of multiple transformations in a high-level feature space yields more informative and effective results for active sonar echoes compared to low-level feature spaces.

Leveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data-driven approach to improve accuracy in radar target localization post adaptive radar detection. To this end, we generate a large number of radar returns by randomly placing targets of variable strengths in a predefined area, using RFView^®, a high-fidelity, site-specific, RF modeling & simulation tool. We produce heatmap tensors from the radar returns, in range, azimuth [and Doppler], of the normalized adaptive matched filter (NAMF) test statistic. We then train a regression convolutional neural network (CNN) to estimate target locations from these heatmap tensors, and we compare the target localization accuracy of this approach with that of peak-finding and local search methods. This empirical study shows that our regression CNN achieves a considerable improvement in target location estimation accuracy. The regression CNN offers significant gains and reasonable accuracy even at signal-to-clutter-plus-noise ratio (SCNR) regimes that are close to the breakdown threshold SCNR of the NAMF. We also study the robustness of our trained CNN to mismatches in the radar data, where the CNN is tested on heatmap tensors collected from areas that it was not trained on. We show that our CNN can be made robust to mismatches in the radar data through few-shot learning, using a relatively small number of new training samples.

Effective extraction of target features has always been a key issue in target recognition technology in the field of signal processing. Traditional deep learning algorithms often require extensive data for pre-training models to ensure the accuracy of feature extraction. Moreover, it is challenging to completely remove noise due to the complexity of the underwater environment. A Time-Delay Autoencoder (TDAE) is employed to extract ship-radiated noise characteristics by leveraging the strong coherent properties of line spectrum. This approach eliminates the need for previous data to adaptively develop a nonlinear model for line spectrum extraction. The test data was processed using three distinct approaches, and plots of recognition accuracy curves at various signal-to-noise ratios were made. On the dataset utilised in the research, experimental results show that the proposed approach achieves over 75% recognition accuracy, even at a signal-to-noise ratio of −15 dB.

Attributed scattering problems have been found to be helpful in inverse synthetic aperture radar (ISAR) imaging and target recognition problems. In this model, the scattering centres are divided into two categories: localised and distributed. Localised scattering centres are those that are concentrated in a small area, while distributed scattering centres are spread out over a larger area. Several methods have been proposed to estimate the scattering centres which aim to accurately identify the location and characteristics of the scattering centres. However, detecting a distributed scattering centre remains a challenging task. A novel technique is proposed based on sparse signals to improve the detection of distributed scattering centres from localised ones. This technique takes advantage of the sparsity of the signals to accurately identify the location of the distributed scattering centres. Experimental results demonstrate the superiority of algorithm in detecting distributed scattering centres. This improved detection capability has significant implications for ISAR imaging and target recognition problems.

Specific emitter identification (SEI) is a technique for identifying emitters based on the principle that the hardware chain is not ideal, causing the emitted signal to contain emitter-specific information. However, the receiver is also non-ideal, which affects recognition accuracy and introduces receiver-specific information that makes SEI difficult to generalise across receiving systems. In this work, a new multi-receiver receiving and processing system (MR-SEI) scheme is proposed to mitigate the influence of receivers based on the analysis of receiver distortion models. After receiving and processing in a specific manner, recognition performance can be enhanced. Therefore, extracted features can be shared among different receivers and platforms, and can even be applied to newly added receivers. The concept of common waveform (CW) is first defined, referring to the received signal without receiver distortions. Different receiving devices are working synchronously, and the CW is estimated using multiple copies of the signal obtained from multiple receivers through the iterative reweighted least squares (IRLS) method. For each receiver, a maximum linear correlation algorithm is proposed to calculate the received signal without being affected by distortions. Experimental results show that the proposed scheme can enhance identification performance. With the increase in the number of receivers, the improvement is more noticeable. Using 10 distorted receivers operating under an SNR of 25 dB, the proposed algorithm can significantly improve the identification performance, achieving over 95% and approaching the ideal scenario of no receiver distortion. Meanwhile, influences caused by receiver distortions can be effectively eliminated, and the database can be shared with new receivers, overperforming other SEI methods that eliminate the receiver.

Accurate work state recognition of multifunction radar (MFR) is crucial in electronic warfare, as it helps understand the enemy's intention and evaluate potential threats. A pulse-level work state recognition method of MFR based on the residual block with spatial attention connected gated recurrent unit by features using metric coding and correlative embedding (MC-RSG) is proposed. Metric coding is designed to generate the distance vector with time of arrival, and the correlative embedding is performed on the distance vector and raw data features to increase the feature information by extracting feature information associated with the previous and subsequent pulses in each feature sequence, respectively. Besides, we make use of the model called RSG containing the residual block with spatial attention connected gated recurrent unit to learn the features of pulse sequences and identify the radar work state label of each pulse. The experimental work shows that the method is robust and has achieved up to 97% recognition accuracy on the test dataset under ideal observation conditions and 5% higher than the comparison network in high noise observation conditions.

In quantitative analysis of seafloor scattering measurements, it is common to model the single-point probability density function of the scattered intensity or amplitude. For more complex seafloors, the pixel amplitude distribution has previously been modelled with a mixture model consisting of two K distributions, but the environment may have more identifiable scattering mechanisms. Choosing the number of components of a mixture model is a decision that must be made, using a priori information, or using a data driven approach. Several common model selection techniques from the statistics literature are explored (the Akaike, Bayesian, deviance, and Watanabe-Akaike information criteria) and compared to the authors' choice. Examples are given for synthetic aperture sonar data collected by an autonomous underwater vehicle in a rocky environment off the coast of Bergen, Norway, using the HISAS-1032 synthetic aperture sonar system. The Bayesian information criterion aligned most closely with the interpretation of both the acoustic images and the plots of the probability of false alarm.

Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)-based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI-based algorithms for NRT and provide results for its use.