Air- and spaceborne synthetic aperture radar (SAR) can provide a large number of high-resolution images for microwave remote sensing applications, such as geoscience and climate change research, environmental Earth system monitoring, and precision agriculture. Unlike spaceborne SAR, the flight trajectory of airborne SAR may deviate from its ideal straight course due to the influence of atmospheric turbulence, resulting in significant motion errors in radar raw data. If a standard imaging algorithm is directly used to process the data, the image focusing quality will be significantly degraded. Therefore, to improve image focusing quality, one typically needs to integrate standard imaging algorithms with an effective motion compensation (MoCo)/autofocus technique. In this article, we present a comprehensive overview of MoCo/autofocus methods for airborne SAR to articulate related research and promote further development. 

Motion Compensation/Autofocus in Airborne Synthetic Aperture Radar: A Review

The nonlinear trajectory and bistatic characteristics of general bistatic synthetic aperture radar (SAR) can cause severe two-dimensional space-variance in the echo signal, and therefore it is difficult to focus the echo signal directly using the traditional frequency-domain imaging algorithm based on the assumption of azimuth translational invariance. At present, the state-of-the-art nonlinear trajectory imaging algorithm is based on singular value decomposition (SVD), which has the problem that SVD may be not controlled, and thus may lead to a high imaging complexity or low imaging accuracy. Therefore, this article proposes a nonlinear trajectory SAR imaging algorithm based on controlled SVD (CSVD). First, the chirp scaling algorithm is used to correct the range space-variance, and then SVD is used to decompose the remaining azimuth space-variant phase, and the first two feature components after SVD are integrated to make them be represented by a new feature component. Finally, the new feature component is used for interpolation to correct the azimuth space-variance. The simulation results show that the proposed CSVD can further improve the image quality compared with SVD-Stolt.

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Processing of Bistatic SAR Data With Nonlinear Trajectory Using a Controlled-SVD Algorithm

Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear inverse problems, and the resolution is limited by the data bandwidth for traditional imaging techniques via matched filter (MF). The sparse SAR imaging technology using compressed sensing (CS) has been developed for enhanced performance, such as superresolution, feature enhancement, etc. More recently, sparse SAR imaging from machine learning (ML), including deep learning (DL), has been further studied, showing great potential in the imaging area. However, there are still gaps between the two groups of methods for sparse SAR imaging, and their connections have not been established. 

Sparse Synthetic Aperture Radar Imaging From Compressed Sensing and Machine Learning: Theories, applications, and trends

Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear inverse problems, and the resolution is limited by the data bandwidth for traditional imaging techniques via matched filter (MF). The sparse SAR imaging technology using compressed sensing (CS) has been developed for enhanced performance, such as superresolution, feature enhancement, etc. More recently, sparse SAR imaging from machine learning (ML), including deep learning (DL), has been further studied, showing great potential in the imaging area. However, there are still gaps between the two groups of methods for sparse SAR imaging, and their connections have not been established.

Obtaining information (e.g., position, respiration, and heartbeat rates) on humans located behind opaque and non-metallic obstacles (e.g., walls and wood) has prompted the development of non-invasive remote sensing technologies. Due to its excellent features like high penetration ability, short blind area, fine-range resolution, high environment adoption capabilities, low cost and power consumption, and simple hardware design, impulse radio ultra-wideband (IR-UWB) through-wall radar has become the mainstream primary application radar used for the non-invasive remote sensing. IR-UWB through-wall radar has been developed for nearly 40 years, and various hardware compositions, deployment methods, and signal processing algorithms have been introduced by many scholars. The purpose of these proposed approaches is to obtain human information more accurately and quickly. In this paper, we focus on IR-UWB through-wall radar and introduce the key advances in system design and deployment, human detection theory, and signal processing algorithms, such as human vital sign signal measurement methods and moving human localization. Meanwhile, we discuss the engineering pre-processing methods of IR-UWB through-wall radar. The lasts research progress in the field is also presented. Based on this progress, the conclusions and the development directions of the IR-UWB through-wall radar in the future are also preliminarily forecasted.

https://www.mdpi.com/1424-8220/21/2/402/pdf

The Overview of Human Localization and Vital Sign Signal Measurement Using Handheld IR-UWB Through-Wall Radar.

Spaceborne synthetic aperture radar (SAR) real-time imaging is especially important for disaster emergencies and real-time monitoring applications with highly desired real-time requirements. Therefore, the continuous improvement of real-time imaging efficiency is an important development trend. At present, traditional real-time imaging algorithms based on constant pulse repetition frequency (PRF) have low accuracy when processing spaceborne SAR data with nonlinear trajectory. For this problem, the existing methods usually introduce some complex signal processing steps, such as scaling or interpolation processing, to improve the accuracy of the real-time imaging, but this will reduce its efficiency. Therefore, this article proposes a new real-time imaging algorithm based on variable PRF for nonlinear trajectory spaceborne SAR. By introducing the variable PRF, the proposed algorithm is equivalent to complete the complex signal processing steps in the radar signal transmission stage, which can greatly improve the efficiency of real-time imaging. Simulation experiments verify the effectiveness of the algorithm.

Real-Time Processing of Spaceborne SAR Data With Nonlinear Trajectory Based on Variable PRF

The real-time imaging research of squint spaceborne synthetic aperture radar (SAR) with high resolution has significant value in both military and civil fields, which makes it a hot issue in SAR research. It is necessary to solve the contradictory problems of nonlinear trajectory and efficient imaging at the same time in order to achieve the two goals, high-resolution and real-time imaging. A large number of complex operations are required in the accurate correction algorithms for nonlinear trajectory, which will reduce the imaging efficiency, and this problem becomes more prominent with the improvement of resolution. To solve the above problems, this paper proposes a new real-time imaging processing of squint high-resolution SAR, which eliminates the velocity–azimuth variation caused by nonlinear trajectory in the data acquisition stage through nonuniform pulse repetition interval (PRI) design. The imaging efficiency has been greatly improved because the new method avoids the complex azimuth resampling operation. Simulation experiments verify the effectiveness of the method.

https://www.mdpi.com/2072-4292/14/15/3725/pdf?version=1659601581

Real-Time Imaging Processing of Squint Spaceborne SAR with High-Resolution Based on Nonuniform PRI Design

As a non-contact life detecting equipment of smart campus, the ultra-wide band (UWB) radar life detector is widely used in finding victims of post-disaster search and rescue. However, due to the complex detection environment and the weak life characteristics of victims, the detecting time consuming by current detecting methods is long meanwhile the sensitivity is low, as a result, the process of search and rescue is slow and the probability of missed detection is high. To solve this problem, three works have been done in this research: first, a mathematical model of the echo of UWB radar is established; second, a concept, the critical speed of fast and slow objects, is put forward as a reference value of signal processing; and third, a new accelerated algorithm of which utilizes block data analysis to detect micro-moving objects is proposed in this paper. It has been verified both by simulation and experiment that the new algorithm can improve the detecting sensitivity and the speed of detection.

An Accelerated Algorithm for Detecting Micro-Moving Objects of Radar Life Detector of Smart Campus Based on Block Data Analysis

The invention discloses a multi-channel error self-correction method for an impulse type through-wall radar. The method comprises the following steps of (S1) detecting the position of a direct coupling wave in each channel, (S2) determining a reference channel and a reference moment in the reference channel, (S3) calculating a time delay error of each channel, (S4) performing adaptive compensationon the error obtained in the step S3 through a zero filling or truncation method, and (S5) performing consistency error compensation on all channels and compensating a direct coupled wave propagationerror. The method has the advantages that the debugging efficiency can be improved and the application range is wide.

Yanghao Jin

Papers

Real-Time Processing of Spaceborne SAR Data With Nonlinear Trajectory Based on Variable PRF

Processing of Bistatic SAR Data With Nonlinear Trajectory Using a Controlled-SVD Algorithm

Real-Time Imaging Processing of Squint Spaceborne SAR with High-Resolution Based on Nonuniform PRI Design

An Accelerated Algorithm for Detecting Micro-Moving Objects of Radar Life Detector of Smart Campus Based on Block Data Analysis

Multi-channel error self-correction method for impulse type through-wall radar