Synthetic Aperture Radar image object detection holds significant application value in both military and civilian domains. However, existing deep learning-based methods suffer from excessive model parameters and high computational costs, making them impractical for real-time deployment on edge computing platforms. To address these challenges, this paper proposes a lightweight SAR object detection method optimized for edge devices. First, we design an efficient backbone network based on inverted residual blocks and the information bottleneck principle, achieving an optimal balance between feature extraction capability and computational resource consumption. Then, a Fast Feature Pyramid Network is constructed to enable efficient multi-scale feature fusion. Finally, we propose a decoupled network-in-network Head, which significantly reduces the computational overhead while maintaining detection accuracy. Experimental results demonstrate that the proposed method achieves comparable detection performance to state-of-the-art YOLO variants while drastically reducing computational complexity (4.4 GFLOP) and parameter count (1.9 M). On edge platforms (Jetson TX2 and Huawei Atlas DK 310), the model achieves real-time inference speeds of 34.2 FPS and 30.7 FPS, respectively, proving its suitability for resource-constrained, real-time SAR object detection scenarios.

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 order to achieve miniaturization, short-range radar (SRR) generally adopts millimeter-wave (MMW) radar with a frequency-modulated continuous-wave (FMCW) system, which may make the stop–go–stop assumption in traditional synthetic aperture radar (SAR) imaging algorithms invalid. In addition, in order to observe a large enough area, SRR often needs a wide radar beam, which may cause serious range–azimuth coupling when using SRR for SAR imaging. The above two problems may make the traditional SAR imaging algorithm invalid in SRR SAR imaging. Taking the SRR SAR imaging application into account, traditional frequency domain SAR imaging algorithms are analyzed and improved in this paper. Firstly, the intra-pulse motion (IPM) caused by the FMCW system and the two-dimensional coupling (TDC) in the case of a wide beam are analyzed. Subsequently, the applicability of the range Doppler algorithm (RDA), the frequency scaling algorithm (FSA) and the range migration algorithm (RMA) for SRR SAR is analyzed. Then, improvement measures are put forward to address the aliasing and folding phenomena caused by the wide-beam problem in the FSA and RMA, respectively. Finally, the effectiveness of the proposed algorithm is verified using simulation data and real measured data collected using an MMW radar fixed on a slide rail.

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.

The cognitive radar framework presents a closed-loop adaptive processing paradigm that ensures the efficient acquisition of target information while exploring the environment and enhancing overall sensing performance. In this study, instead of mutual information, we employed the squared Pearson correlation coefficient (SPCC) to measure the target information in observations specifically considering only linear dependency. A waveform design method is proposed that simultaneously maximizes target information and minimizes the integrated sidelobe level (ISL) under the constant modulus constraint (CMC). To enhance computational efficiency, we reformulated the constrained problem as an unconstrained optimization problem by leveraging the inherent geometric property of CMC. Additionally, we present two conditional equivalences associated with waveform design in relation to target information. The simulation results validate the feasibility and effectiveness of the proposed method.

Heterogeneous clutter suppression is an important challenge for multi-channel synthetic aperture radar (SAR) moving target detection. The knowledge-aided space time adaptive processing (STAP) method is widely used due to its superior clutter suppression performance. In this process, it is necessary to use independent and identically distributed (IID) training data to estimate the clutter covariance matrix (CCM) of the cell under test (CUT). However, heterogeneous environments such as clutter power fluctuations and moving target contamination in training samples will significantly reduce the estimation accuracy of CCM, which is not conducive to subsequent clutter suppression processing. To solve this problem, this paper introduces the covariance matrix estimation method based on the geometric mean of Hermitian positive-definite (HPD) manifold into the multi-channel SAR moving target detection. Firstly, the airborne multi-channel SAR echo is characterized on the HPD manifold, which better reveals the nonlinear geometric structure of the multi-channel SAR echo data than in Euclidean space. Based on the echo characterization on the HPD manifold, the CCM estimation problem is transformed into the geometric mean estimation problem on the HPD manifold. At the same time, the generalized inner product (GIP) criterion is used in training sample selection to reduce the negative impact of contaminated samples. Finally, the CCM estimation based on geometric mean is applied to the knowledge-aided SAR-STAP, which effectively improves the clutter suppression performance. Given the large volume of multi-channel SAR echo data, this paper comprehensively evaluates algorithm performance and computational complexity, and selects the geometric metric criterion for multi-channel SAR moving target detection. Based on this criterion, a multi-channel SAR moving target detection framework driven by HPD manifold is proposed. The detection performance of the proposed framework is verified on simulated and measured clutter backgrounds.