Deep learning technologies have significantly improved performance in the field of synthetic aperture radar (SAR) image target recognition compared to traditional methods. However, the inherent “black box" property of deep learning models leads to a lack of transparency in decision-making processes, making them difficult to be widespread applied in practice. This is especially true in SAR applications, where the credibility and reliability of model predictions are crucial. The complexity and insufficient explainability of deep networks have become a bottleneck for their application. To tackle this issue, this study proposes a physically explainable framework for complex-valued SAR image recognition, designed based on the physical process of microwave propagation. This framework utilizes complex-valued SAR data to explore the amplitude and phase information and its intrinsic physical properties. The network architecture is fully parameterized, with all learnable parameters endowed with clear physical meanings. Experiments on both the complex-valued MSTAR dataset and a self-built Qilu-1 complex-valued dataset were conducted to validate the effectiveness of framework. The de-overlapping capability of EMWaveNet enables accurate recognition of overlapping target categories, whereas other models are nearly incapable of performing such recognition. Against 0dB forest background noise, it boasts a 20% accuracy improvement over traditional neural networks. When targets are 60% occluded by noise, it still outperforms other models by 9%. An end-to-end complex-valued synthetic aperture radar automatic target recognition (SAR-ATR) algorithm is constructed to perform recognition tasks in interference SAR scenarios. The results demonstrate that the proposed method possesses a strong physical decision logic, high physical explainability and robustness, as well as excellent de-aliasing capabilities. Finally, a perspective on future applications is provided.

With the rapid development and widespread deployment of radar technology, the interpretation of the vast amount of synthetic aperture radar (SAR) imagery obtained daily has emerged as a hot topic. The complexity of the electromagnetic scattering mechanisms contained within radar images makes SAR image interpretation a challenging task. Current methodologies for SAR image interpretation primarily involve feature extraction-based techniques, categorized into image-domain and frequency-domain algorithms. However, these methods are faced with issues, such as rough segmentation in images, high-computational complexity, and poor robustness, presenting significant challenges in the field. In this article, a novel framework for SAR image interpretation is proposed, leveraging reinforcement learning (RL) for the extraction of electromagnetic scattering features and the inversion of parameters. Within this framework, a nonsparse reward function, combined with curriculum learning, is introduced as the supervisory information. It enables more efficient policy updates through a structured two-stage training approach. In addition, an algorithm that integrates a four-neighbor breadth-first search (BFS) with the watershed segmentation process is proposed, aiming to enhance the accuracy of scattering center analysis in SAR imagery. The attribute scattering center model (ASCM) is utilized as a prototype for conducting algorithmic research and experimentation. Experimental results on both simulation data and measured data have indicated that the proposed method significantly improves efficiency while ensuring accuracy, demonstrating its capability to extract parameters from measured data in most scenarios.

The simulation of high-resolution synthetic aperture radar (SAR) imagery in intricate environments remains a formidable challenge. Advancements in reversible microwave-domain surface scattering models are crucial, potentially revolutionizing the fidelity of SAR simulations and streamlining the extraction of target parameters. Drawing inspiration from computer graphics, this article proposes a novel differentiable ray tracing (DRT) approach for microwave rendering and fast SAR imaging. The rendering model utilizes coherent spatially varying (SV) bidirectional scattering distribution function (CSVBSDF) based on the Kirchhoff approximation (KA) and the small perturbation method (SPM), corresponding to specular and diffuse scattering contributions, respectively. SAR imaging is efficiently executed via a fusion of ray tracing (RT) and rapid mapping projection. The innovative DRT reversible engine enables swift estimation of SAR image parameter gradients for direct CSVBSDF surface scattering parameter optimization. The method’s validity is confirmed through comparative analysis with measured SAR images and other methods, demonstrating marked improvements in SAR simulation fidelity across diverse observational scenarios by learning surface scattering parameters.

Quadric surfaces are commonly seen geometries in man-made targets. In this paper, a unified bidirectional scattering distribution function (BSDF) is analytically derived for general convex quadric surfaces including both the doubly- and the singly-curved surfaces. Based on physical optics (PO) and stationary phase method (SPM), the BSDFs of the doubly- and singly-curved surfaces are first deduced separately. Then the unified form of the two BSDFs is formulated, which can smoothly degenerate to any specific type of canonical curved surfaces by taking the corresponding values of the geometric parameters. Comparison with numerical PO demonstrates the correctness and efficacy of the proposed model. This model can be used to continuously model the bistatic polarimetric scattering behavior of a localized quadric surface patch. It can be used as the prototype for a scattering dictionary for both forward and inverse problems of electromagnetic scattering, which is of great value to radar target recognition and radar image interpretation.

With the development of$5\mathrm{G}$, the location of base stations is related to the electromagnetic wave scattering with the surface of objects, especially the building walls in cities. In this study, the building wall is simply modeled as a two-dimensional plane surface. Since the roughness of the building wall is relatively small, the specular reflection is the main component of the scattering wave. The bidirectional reflectance distribution function (BRDF) of building walls is approximated as the Fresnel reflection coefficients. Then, a field measurement system, called FUSAR-Rail-S, is established to measure the scattering of the building wall. In the measurement, two different walls are used to analyze the roughness effect on the scattering coefficient. A comparison of the measurement result with the Fresnel reflection coefficients shows that they are in good agreement in most angles. Within a certain angle range, the reflection coefficients increase when the incident angle becomes larger. In addition, the reflection coefficients of the smaller roughness wall are larger.

High-resolution synthetic aperture radar (SAR) as an imaging device becomes more and more like a “camera” at the microwave frequency band. How different objects or object surfaces may visually appear in SAR images becomes an interesting research topic. Inspired by the bidirectional reflectance distribution function (BRDF) models employed in computer graphics (CGs), this article proposes the coherent spatially varying bidirectional scattering distribution function (CSVBSDF) for characterizing the electromagnetic scattering and SAR imaging behavior of surfaces. The CSVBSDF establishes a mapping function from observation parameters and surface local parameters to multidimensional measurements. In this article, CSVBSDF of the randomly rough surface is derived via adapting the integral equation method (IEM) to finite-size pixel cells under the plane wave and tapered wave incidence, respectively. It is then validated against the numerical beam simulation method (BSM) in the SAR image domain. A ground-based rail SAR and a 3-D laser scanner are used to measure the SAR image and the corresponding 3-D geometry of a real ground surface. Surface-local parameters, such as the local slope and roughness, are estimated from the measured 3-D geometry and then fed into the CSVBSDF model to produce a synthetic SAR image. Comparison against the real SAR image preliminarily demonstrates the efficacy of the proposed CSVBSDF model.