Video synthetic aperture radar (ViSAR) can produce continuous images with a high frame rate and contain the moving target’s shadow, which provides numerous advantages for detecting moving targets. In this letter, a novel moving target detection method based on convolutional neural network (CNN) is proposed. The proposed network has a 3D convolutional encoding path, a 2D convolutional decoding path, and a bridge path to efficiently capture the target’s shadow information and summarize the spatiotemporal features from raw continuous images to high-level semantics. Furthermore, based on coordinate attention (CA), a temporal tri-coordinate attention (TTCA) module is proposed to obtain key spatiotemporal features in ViSAR data. The validity of the proposed method has been confirmed through experiments with actual ViSAR data and shows superior performance in the suppression of false alarms and missing alarms.

In high intensity focused ultrasound (HIFU) treatment, it is crucial to accurately identify denatured and normal biological tissues. In this paper, a novel method based on compressed sensing (CS) and refined composite multi-scale fuzzy entropy (RCMFE) is proposed. First, CS is used to denoise the HIFU echo signals. Then the multi-scale fuzzy entropy (MFE) and RCMFE of the denoised HIFU echo signals are calculated. This study analyzed 90 cases of HIFU echo signals, including 45 cases in normal status and 45 cases in denatured status, and the results show that although both MFE and RCMFE can be used to identify denatured tissues, the intra-class distance of RCMFE on each scale factor is smaller than MFE, and the inter-class distance is larger than MFE. Compared with MFE, RCMFE can calculate the complexity of the signal more accurately and improve the stability, compactness, and separability. When RCMFE is selected as the characteristic parameter, the RCMFE difference between denatured and normal biological tissues is more evident than that of MFE, which helps doctors evaluate the treatment effect more accurately. When the scale factor is selected as 16, the best distinguishing effect can be obtained.

Video synthetic aperture radar (ViSAR) attracts much attention in the field of moving target detection due to its high frame rate and high-resolution imaging capability. In ViSAR, the moving target's shadow does not offset and defocus, which is widely used as a feature for moving target detection. To address the shortcomings of high false and missing alarms in existing detection algorithms, this study proposes a high-precision moving target shadow detection algorithm for ViSAR based on information geometry. Firstly, the shadow enhancement and background suppression algorithm is used to enhance the saliency of moving targets' shadows in ViSAR images to reduce the false and missing alarms caused by complex background. Then, based on the information geometry theory, the enhanced ViSAR image is mapped to a Gaussian statistical manifold, and the Jensen–Shannon (JS) divergence matrix with pixel context information is extracted from this manifold. And the JS divergence matrix and the gray-scale information from the enhanced ViSAR image are used to construct the joint probability distribution function (JPDF). The 2D minimum cross-entropy principle is used to calculate the threshold to extract the regions of interest (ROIs). Finally, the final detection results are obtained by applying single-frame and multiple-frame false alarms suppression. The experimental results, based on real ViSAR dataset published by Sandia National Laboratories (SNL), verify the effectiveness of our proposed algorithm. Specifically, the Recall rate is 85.97%, the Precision is 96.22%, and the F1 score achieves 0.91.

Based on the all-weather imaging capability of synthetic aperture radar (SAR), video synthetic aperture radar (ViSAR) enables dynamic ground target monitoring through high-frame-rate continuous observation. Since moving targets' shadows remain stable and unaffected by defocusing in ViSAR images, this property is widely leveraged for ViSAR moving target detection. However, ViSAR moving target detection faces challenges including low contrast in moving targets' shadows, complex background clutter interference, and the difficulty of simultaneously detecting small and medium-sized shadow targets. Existing deep learning-based detection methods for ViSAR struggle to address these challenges effectively. To overcome these limitations, we propose a multiscale CNN–Transformer hybrid detection framework, which consists of three key components. Firstly, in the pre-processing stage of ViSAR images, an improved low-rank representation algorithm is used to suppress background clutter and stationary targets' shadows, and enhance the contrast of moving targets' shadows. Secondly, the CNN's feature representation capability for small and medium-sized shadow targets is enhanced by improving the backbone and designing a novel feature pyramid network structure. This addresses the challenge of detecting small and medium-sized shadow targets simultaneously. Finally, within the Transformer architecture, a novel proposal generation module and a contrastive denoising training strategy are integrated into the Deformable Transformer to mitigate ambiguous semantic encoding and accelerate convergence in DETR- like detectors. Experimental results on the real-world ViSAR dataset demonstrate that the proposed framework achieves state-of-the-art performance.

For accurate evaluation of high intensity focused ultrasound (HIFU) treatment effect, it is of great importance to effectively judge whether the sampled signal is the HIFU echo signal or the noise signal. In this paper, a judgment method based on an auto-regressive (AR) model and spectrum information entropy is proposed. In total, 188 groups of data are obtained while the HIFU source is on or off through experiments, and these sampled signals are judged by this method. The judgment results of this method are compared with empirical judgments. It is found that when the segment number for the power spectrum estimated by AR model is 14 to 17, the judgment results of this method have a higher consistency with empirical judgments, and Accuracy, Sensitivity and Specificity all have good values. Moreover, after comparing and analyzing this method with the classic power spectrum estimation method, it is found that the recognition rate of the two sampled signals of this method is higher than that of the classic power spectrum estimation method. Therefore, this method can effectively judge the different types of sampled signals.

Clutter suppression remains a fundamental challenge in multi-channel SAR moving target detection. Among various approaches, Space-Time Adaptive Processing (STAP) has been widely employed due to its excellent clutter suppression capabilities. However, STAP intrinsically assumes complex Gaussian clutter, which often significantly deviates from actual heterogeneous clutter environments encountered in practical scenarios. Such deviations can markedly impair the clutter suppression performance of STAP. To overcome this issue, this paper proposes a Kullback-Leibler Divergence (KLD)-based Hermitian Positive Definite (HPD) manifold Dimensionality Reduction (DR) technique, aimed at enhancing the feature discrimination of moving target on HPD manifold. Initially, the airborne multi-channel SAR image domain data are mapped into the HPD manifold to form a feature manifold. Next, an optimal dimensionality reduction mapping is constructed by formulating an optimization problem on the HPD manifold that maximizes the KLD, and an analytical solution to this problem is derived. Based on this solution, an optimal reduction matrix is designed, projecting the data into a low-dimensional HPD manifold. Finally, on the reduced HPD manifold, a Matrix Information Geometry (MIG) detector based on KLD is employed to achieve enhanced detection of moving targets. The proposed multi-channel SAR moving target detection framework incorporating KLD-based dimensionality reduction does not rely on specific parametric statistical model of the clutter background, only utilizes geometric properties derived from second-order statistic. This effectively addresses the performance degradation caused by statistical distribution model mismatch in complex environments. Moreover, deriving an analytical solution to the optimization problem avoids the high computational costs associated with Riemannian conjugate gradient algorithms, thus reducing complexity substantially and facilitating practical implementation in SAR systems. Experimental results, including simulations and measurements with real clutter backgrounds, demonstrate the superior effectiveness of the proposed approach.