Hardware-Accelerated SAR Simulation with NVIDIA-RTX Technology

Synthetic Aperture Radar (SAR) is a critical sensing technology that is notably independent of the sensor-to-target distance and has numerous cross-cutting applications, e.g., target recognition, mapping, surveillance, oceanography, geology, forestry (biomass, deforestation), disaster monitoring (volcano eruptions, oil spills, flooding), and infrastructure tracking (urban growth, structure mapping). SAR uses a high-power antenna to illuminate target locations with electromagnetic radiation, e.g., 10GHz radio waves, and illuminated surface backscatter is sensed by the antenna which is then used to generate images of structures. Real SAR data is difficult and costly to produce and, for research, lacks a reliable source ground truth. This article proposes a open source SAR simulator to compute phase histories for arbitrary 3D scenes using newly available ray-tracing hardware made available commercially through the NVIDIA's RTX graphics cards series. The OptiX GPU ray tracing library for NVIDIA GPUs is used to calculate SAR phase histories at unprecedented computational speeds. The simulation results are validated against existing SAR simulation code for spotlight SAR illumination of point targets. The computational performance of this approach provides orders of magnitude speed increases over CPU simulation. An additional order of magnitude of GPU acceleration when simulations are run on RTX GPUs which include hardware specifically to accelerate OptiX ray tracing. The article describes the OptiX simulator structure, processing framework and calculations that afford execution on massively parallel GPU computation device. The shortcoming of the OptiX library's restriction to single precision float representation is discussed and modifications of sensitive calculations are proposed to reduce truncation error thereby increasing the simulation accuracy under this constraint.Comment: 17 pages, 7 figures, Algorithms for Synthetic Aperture Radar Imagery XXVII, SPIE Defense + Commercial Sensing 202

Fast GO/PO RCS calculation: A GO/PO parallel algorithm implemented on GPU and accelerated using a BVH data structure and the Type 3 Non-Uniform FFT

The purpose of this PhD research was to develop and optimize a fast numeric algorithm able to compute monostatic and bistatic RCS predictions obtaining an accuracy comparable to what commercially available from well-known electromagnetic CADs, but requiring unprecedented computational times. This was realized employing asymptotic approximated methods to solve the scattering problem, namely the Geometrical Optics (GO) and the Physical Optics (PO) theories, and exploiting advanced algorithmical concepts and cutting-edge computing technology to drastically speed-up the computation. The First Chapter focuses on an historical and operational overview of the concept of Radar Cross Section (RCS), with specific reference to aeronautical and maritime platforms. How geometries and materials influence RCS is also described. The Second Chapter is dedicated to the first phase of the algorithm: the electromagnetic field transport phase, where the GO theory is applied to implement the “ray tracing”. In this Chapter the first advanced algorithmical concept which was adopted is described: the Bounding Volume Hierarchy (BVH) data structure. Two different BVH approaches and their combination are described and compared. The Third Chapter is dedicated to the second phase of the calculation: the radiation integral, based on the PO theory, and its numerical optimization. Firstly the Type-3 Non-Uniform Fast Fourier Transform (NUFFT) is presented as the second advanced algorithmical tool that was used and it was indeed the foundation of the calculation of the radiation integral. Then, to improve the performance but also to make the application of the approach feasible in case of electrically large objects, the NUFFT was further optimized using a “pruning” technique, which is a stratagem used to save memory and computational time by avoiding calculating points of the transformed domain that are not of interest. To validate the algorithm, a preliminary measurement campaign was held at the headquarter of the Ingegneria Dei Sistemi (IDS) Company, located in Pisa. The measurements, performed on canonical scatterers using a Synthetic Aperture Radar (SAR) imaging equipment set up on a planar scanner inside a semi-anechoic chamber, are discussed

Electromagnetic Scattering Characteristics of Composite Targets and Software Development Based on PO Algorithm

Physical optics (PO) algorithm is a high-frequency electromagnetic (EM) algorithm, which is widely used to solve the EM scattering problems of electrically large composite targets. Due to the PO algorithm only considers the induced current in the bright region irradiated by EM wave, the computational memory and time consumption are superior than other high-frequency algorithms, and the calculation accuracy is pretty fine. Based on the PO algorithm, this thesis focuses on the occlusion judgement of PO algorithm and its application in composite targets. The main contents of this thesis are as follows: 1. The occlusion judgement software system for PO algorithm is developed. The main function of this software is to judge the bright region of the target under the irradiation of EM wave. This software uses two judgement methods: ray tracing method based on CPU and Z-Buffer method based on CPU and GPU. Moreover, due to the compromise between patch size and patch number, both methods have errors at the edge of bright and shadow regions. This thesis discusses the error and reduces it. 2. Based on PO algorithm, the EM scattering characteristics of targets covered by plasma sheath are discussed. We simulate the plasma sheath flow field data of hypersonic vehicle by FASTRAN software, compare and analyze the plasma sheath electron number density at different flight heights and speeds. On this basis, the bistatic RCS of hypersonic vehicle head-on irradiation under different flight heights and speeds is calculated by using the PO algorithm of layered medium. 3. SAR image simulation of tree ground composite target is carried out based on PO algorithm and Non-Uniform Fast Fourier Transform (NUFFT) method. Firstly, we introduce the geometric modeling and EM parameter modeling of tree ground composite target, and the scattering characteristics of tree ground composite target are obtained by using PO algorithm. Finally, the scattering field of the target is processed by NUFFT method, and the SAR simulation images of multiple trees scene are obtained

SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation

SAR images are highly sensitive to observation configurations, and they exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance fields (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection pinciples, a set of SAR images is modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of 3D voxel SAR rendering equation and the sampling relationship between the 3D space voxels and the 2D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multi-view representation and generalization capabilities of SAR-NeRF. Additionally, it is found that SAR-NeRF augumented dataset can significantly improve SAR target classification performance under few-shot learning setup, where a 10-type classification accuracy of 91.6\% can be achieved by using only 12 images per class

SARCASTIC v2.0 - High-performance SAR simulation for next-generation ATR systems

Synthetic aperture radar has been a mainstay of the remote sensing field for many years, with a wide range of applications across both civilian and military contexts. However, the lack of openly available datasets of comparable size and quality to those available for optical imagery has severely hampered work on open problems such as automatic target recognition, image understanding and inverse modelling. This paper presents a simulation and analysis framework based on the upgraded SARCASTIC v2.0 engine, along with a selection of case studies demonstrating its application to well-known and novel problems. In particular, we demonstrate that SARCASTIC v2.0 is capable of supporting complex phase-dependent processing such as interferometric height extraction whilst maintaining near-realtime performance on complex scenes

Electromagnetic ray-tracing for the investigation of multipath and vibration signatures in radar imagery

Synthetic Aperture Radar (SAR) imagery has been used extensively within UK Defence and Intelligence for many years. Despite this, the exploitation of SAR imagery is still challenging to the inexperienced imagery analyst as the non-literal image provided for exploitation requires careful consideration of the imaging geometry, the target being imaged and the physics of radar interactions with objects. It is therefore not surprising to note that in 2017 the most useful tool available to a radar imagery analyst is a contextual optical image of the same area. This body of work presents a way to address this by adopting recent advances in radar signal processing and computational geometry to develop a SAR simulator called SARCASTIC (SAR Ray-Caster for the Intelligence Community) that can rapidly render a scene with the precise collection geometry of an image being exploited. The work provides a detailed derivation of the simulator from first principals. It is then validated against a range of real-world SAR collection systems. The work shows that such a simulator can provide an analyst with the necessary tools to extract intelligence from a collection that is unavailable to a conventional imaging system. The thesis then describes a new technique that allows a vibrating target to be detected within a SAR collection. The simulator is used to predict a unique scattering signature - described as a one-sided paired echo. Finally an experiment is described that was performed by Cranfield University to specifications determined by SARCASTIC which show that the unique radar signature can actually occur within a SAR collection

Investigating the effects of bistatic SAR phenomenology on feature extraction

Interest in bistatic radar has fluctuated since its first demonstration. Modern multistatic and MIMO radar systems have prompted a resurgence in the field, particularly where imaging radar and automatic target recognition are concerned. The lack of openly-available bistatic imagery and corresponding analysis of the unique artefacts which occur within it is a significant barrier to developing automatic target recognition methods for such systems. This paper looks to address these issues by presenting an appropriate simulation methodology for obtaining bistatic synthetic aperture radar imagery of ground vehicle targets and investigating the features that occur within this imagery. In this paper, a number of effects unique to the bistatic case are presented, and the performance degradation of a classifier at several bistatic angles is empirically demonstrated. A version of the final database will be publicly released to promote wider research into this challenge

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm