We have recently introduced a modification of the multiple signal
classification (MUSIC) method for synthetic aperture radar. This
method depends on a tunable, user-defined parameter,
$ε$, that allows for quantitative high-resolution imaging. It
requires however, relative large single-to-noise ratios (SNR) to
work effectively. Here, we first identify the fundamental mechanism
in that method that produces high-resolution images. Then we
introduce a modification to Kirchhoff Migration (KM) that uses the same
mechanism to produces tunable, high-resolution images. This modified
KM method can be applied to low SNR measurements. We show simulation
results that demonstrate the features of this method.
In this paper, we present 3D high resolution radar imaging process at millimeter-Wave (mmWave) frequencies by creating the effect of a large aperture synthetically. We use a low cost fully integrated Frequency Modulated Continuous Wave (FMCW) radar operating at $\rm 79\;GHz$ and then perform Synthetic Aperture Radar (SAR) imaging in the near-filed zone. At the end, we conduct a real experiment and present the reconstructed image.
We present a framework for simulating realistic inverse synthetic aperture radar images of automotive targets at millimeter wave frequencies. The model incorporates radar scattering phenomenology of commonly found vehicles along with range-Doppler based clutter and receiver noise. These images provide insights into the physical dimensions of the target, the number of wheels and the trajectory undertaken by the target. The model is experimentally validated with measurement data gathered from an automotive radar. The images from the simulation database are subsequently classified using both trad…
Due to the rapid increase in 76 GHz automotive spectrum use in recent years, wireless interference is becoming a legitimate area of concern. However, the recent rise in interest of automated vehicles (AVs) has also spurred new growth and adoption of low frequency vehicle-to-everything (V2X) communications in and around the 5.8 GHz unlicensed bands, opening the possibility for communications spectrum reuse in the form of joint radar-communications (JRC). In this work, we present a low frequency 5.9 GHz side-looking polarimetric synthetic aperture radar (SAR) for automotive use, utilizing a rang…
Automotive targets undergoing turns in road junctions offer large synthetic apertures over short dwell times to automotive radars that can be exploited for obtaining fine cross-range resolution. Likewise, the wide bandwidths of the automotive radar signal yield high-range resolution profiles. Together, they are exploited for generating inverse synthetic aperture radar (ISAR) images that offer rich information regarding the target vehicle's size, shape, and trajectory which is useful for object recognition and classification. However, a key requirement for ISAR is translation motion compensatio…
The stretch processing architecture is commonly used for frequency modulated continuous wave (FMCW) radar due to its inexpensive hardware, low sampling rate, and simple architecture. However, the stretch processing architecture is not able to achieve optimal Signal to Noise ratio (SNR) in comparison to the matched-filter architecture. In this paper, we aim to propose a method whereby stretch processing can achieve optimal SNR. Hence, we develop a novel processing method to enable applying a matched filter to the output of the stretch processing. The proposed architecture achieves optimal SNR w…
In this paper, we study the trajectory and resource optimization for lightweight rotary-wing unmanned aerial vehicles (UAVs) equipped with a synthetic aperture radar (SAR) system. The UAV's mission is to perform SAR imaging of a given area of interest (AoI). In this setup, real-time communication with a base station (BS) is required to facilitate live mission planning for the drone. For this purpose, a non-convex mixed-integer non-linear program (MINLP) is formulated such that the UAV resources and three-dimensional (3D) trajectory are jointly optimized for maximization of the drone radar grou…
A problem with synthetic aperture radar (SAR) is that due to the poor penetrating action of electromagnetic waves within solid bodies, the ability to observe through distributed targets is precluded. In this context, indeed, imaging is only possible on targets distribute on the scene surface. This work describes an imaging method based on the analysis of micro-motions present on volcanoes and generated by the underground Earth's heat. Processing the coherent vibrational information embedded on the single SAR image, in the single-look-complex configuration, the sound information is exploited, p…
A problem with synthetic aperture radar (SAR) is that, due to the poor penetrating action of electromagnetic waves inside solid bodies, the capability to observe inside distributed targets is precluded. Under these conditions, imaging action is provided only on the surface of distributed targets. The present work describes an imaging method based on the analysis of micro-movements on the Khnum-Khufu Pyramid, which are usually generated by background seismic waves. The results obtained prove to be very promising, as high-resolution full 3D tomographic imaging of the pyramid's interior and subsu…
Ground penetrating radar mounted on micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optica…
Automatic Target Recognition (ATR) for military applications is one of the core processes towards enhancing intelligencer and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this paper surveys and assesses current SAR ATR architectures that employ the most popular dataset for the SAR domain, namely the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Based on the current methodology trends, we propose a taxonomy for the SAR ATR architectures, along wi…
Motivated by applications in unmanned aerial based ground penetrating radar for detecting buried landmines, we consider the problem of imaging small point like scatterers situated in a lossy medium below a random rough surface. Both the random rough surface and the absorption in the lossy medium significantly impede the target detection and imaging process. Using principal component analysis we effectively remove the reflection from the air-soil interface. We then use a modification of the classical synthetic aperture radar imaging functional to image the targets. This imaging method introduce…
Synthetic aperture radar (SAR) is widely used for ground surface classification since it utilizes information on vegetation and soil unavailable in optical observation. Image classification often employs convolutional neural networks. However, they have serious problems such as long learning time and resolution degradation in their convolution and pooling processes. In this paper, we propose complex-valued reservoir computing (CVRC) to deal with complex-valued images in interferometric SAR (InSAR). We classify InSAR image data by using CVRC successfully with a higher resolution and a lower com…
Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR …
NASA Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室，加州理工学院）
California's Central Valley is the national agricultural center, producing 1/4 of the nation's food. However, land in the Central Valley is sinking at a rapid rate (as much as 20 cm per year) due to continued groundwater pumping. Land subsidence has a significant impact on infrastructure resilience and groundwater sustainability. In this study, we aim to identify specific regions with different temporal dynamics of land displacement and find relationships with underlying geological composition. Then, we aim to remotely estimate geologic composition using interferometric synthetic aperture rada…
We present a novel approach to the problem of dual-functional radar and communication (DFRC) waveform design with adjustable peak-to-average power ratio (PAPR), while minimizing the multi-user communication interference and maintaining a similarity constraint towards a radar chirp signal. The approach is applicable to generic radar chirp signals and for different constellation sizes. We formulate the waveform design problem as a non convex optimization problem. As a solution, we adopt the alternating direction method of multipliers (ADMM), hence iterating towards a stable waveform for both rad…
University of Victoria（维多利亚大学）；QB Information Technologies (1QBit)（1QB信息技术公司（1QBit））
The focus of this work is to explore the use of quantum annealing solvers for the problem of phase unwrapping of synthetic aperture radar (SAR) images. Although solutions to this problem exist based on network programming, these techniques do not scale well to larger-sized images. Our approach involves formulating the problem as a quadratic unconstrained binary optimization (QUBO) problem, which can be solved using a quantum annealer. Given that present embodiments of quantum annealers remain limited in the number of qubits they possess, we decompose the problem into a set of subproblems that …
Fast Matrix Inversion and Determinant Computation for Polarimetric Synthetic Aperture Radar
Graduate Program of Electrical and Computer Engineering Department, University of Calgary（卡尔加里大学电气与计算机工程研究生项目）；Signal Processing Group, Departamento de Estatística, Universidade Federal de Pernambuco（伯南布哥联邦大学统计系信号处理组）；Department of Electrical and Computer Engineering, University of Calgary（卡尔加里大学电气与计算机工程系）；Laboratório de Computação Científica e Análise Numérica, Universidade Federal de Alagoas（阿拉戈阿斯联邦大学科学计算与数值分析实验室）
By a symmetry argument, a synethic aperture radar collection along a linear path does not collect three-dimensional information about the scene. However, it is known that vertical curvature can be used to derive some vertical position information. This paper approaches the problem from a monopulse perspective, resulting in a non-iterative computation that commutes with efficient image formation algorithms.
Millimeter-wave (mmW) radar is widely applied to advanced autopilot assistance systems. However, its small antenna aperture causes a low imaging resolution. In this paper, a new distributed mmW radar system is designed to solve this problem. It forms a large sparse virtual planar array to enlarge the aperture, using multiple-input and multiple-output (MIMO) processing. However, in this system, traditional imaging methods cannot apply to the sparse array. Therefore, we also propose a 3D super-resolution imaging method specifically for this system in this paper. The proposed method consists of t…
University of California Berkeley（加州大学伯克利分校）；The Ohio State University（俄亥俄州立大学）；German Aerospace Center DLR（德国航空航天中心）；University of Valencia（瓦伦西亚大学）；National Observatory of Athens（雅典国家天文台）；United Nations Satellite Centre（联合国卫星中心）；University of Oxford（牛津大学）；NVIDIA（英伟达公司）；Universidad de Antioquia（安蒂奥基亚大学）
With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that can provide measurements of affected areas independent of weather or lighting conditions. Usage of SAR, however, is hindered by domain knowledge that is necessary for the pre-processing steps and its interpretation requires expert knowledge. We provide simplified, pre-processed, machine-learning ready SAR datacubes for four globally located landslid…
Automotive synthetic aperture radar (SAR) can achieve a significant angular resolution enhancement for detecting static objects, which is essential for automated driving. Obtaining high resolution SAR images requires precise ego vehicle velocity estimation. A small velocity estimation error can result in a focused SAR image with objects at offset angles. In this paper, we consider an automotive SAR system that produces SAR images of static objects based on ego vehicle velocity estimation from the radar return signal without the overhead in complexity and cost of using an auxiliary global navig…
Automotive synthetic aperture radar (SAR) systems are rapidly emerging as a candidate technological solution to enable a high-resolution environment mapping for autonomous driving. Compared to lidars and cameras, automotive-legacy radars can work in any weather condition and without an external source of illumination, but are limited in either range or angular resolution. SARs offer a relevant increase in angular resolution, provided that the ego-motion of the radar platform is known along the synthetic aperture. In this paper, we present the results of an experimental campaign aimed at assess…
