Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years. It provides high-resolution, day-and-night and weather-independent images for a multitude of applications ranging from geoscience and climate change research, environmental and Earth system monitoring, 2-D and 3-D mapping, change detection, 4-D mapping (space and time), security-related applications up to planetary exploration. With the advances in radar technology and geo/bio-physical parameter inversion modeling in the 90s, using data from several airborne and spaceborne systems, a paradigm shift occurred from the development driven by the technology push to the user demand pull. Today, more than 15 spaceborne SAR systems are being operated for innumerous applications. This paper provides first a tutorial about the SAR principles and theory, followed by an overview of established techniques like polarimetry, interferometry and differential interferometry as well as of emerging techniques (e.g., polarimetric SAR interferometry, tomography and holographic tomography). Several application examples including the associated parameter inversion modeling are provided for each case. The paper also describes innovative technologies and concepts like digital beamforming, Multiple-Input Multiple-Output (MIMO) and bi- and multi-static configurations which are suitable means to fulfill the increasing user requirements. The paper concludes with a vision for SAR remote sensing.

/pdf/a-tutorial-on-synthetic-aperture-radar-1v8k9pqokm.pdf

A tutorial on synthetic aperture radar

Synthetic aperture radar (SAR) is a coherent active microwave imaging method. In remote sensing it is used for mapping the scattering properties of the Earth's surface in the respective wavelength domain. Many physical and geometric parameters of the imaged scene contribute to the grey value of a SAR image pixel. Scene inversion suffers from this high ambiguity and requires SAR data taken at different wavelength, polarization, time, incidence angle, etc. Interferometric SAR (InSAR) exploits the phase differences of at least two complex-valued SAR images acquired from different orbit positions and/or at different times. The information derived from these interferometric data sets can be used to measure several geophysical quantities, such as topography, deformations (volcanoes, earthquakes, ice fields), glacier flows, ocean currents, vegetation properties, etc. This paper reviews the technology and the signal theoretical aspects of InSAR. Emphasis is given to mathematical imaging models and the statistical properties of the involved quantities. Coherence is shown to be a useful concept for system description and for interferogram quality assessment. As a key step in InSAR signal processing two-dimensional phase unwrapping is discussed in detail. Several interferometric configurations are described and illustrated by real-world examples. A compilation of past, current and future InSAR systems concludes the paper.

https://iopscience.iop.org/article/10.1088/0266-5611/14/4/001/pdf

Synthetic aperture radar interferometry

This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., Wi-Fi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require line-of-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We implement a proof-of-concept prototype of WiSee using USRP-N210s and evaluate it in both an office environment and a two- bedroom apartment. Our results show that WiSee can identify and classify a set of nine gestures with an average accuracy of 94%.

/pdf/whole-home-gesture-recognition-using-wireless-signals-4tosup7gjr.pdf

Whole-home gesture recognition using wireless signals

We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification when the learning includes a class separability criteria in the objective function. The benefits of dictionary learning clearly show that a proper understanding of causes underlying the sensed world is key to task-specific representation of relevant information in high-dimensional data sets.

Dictionary Learning

Exact synthetic aperture radar (SAR) inversion for a linear aperture may be obtained using fast transform techniques. Alternatively, back-projection integration in time domain can also be used. This technique has the benefit of handling a general aperture geometry. In the past, however, back-projection has seldom been used due to heavy computational burden. We show that the back-projection integral can be recursively partitioned and an effective algorithm constructed based on aperture factorization. By representing images in local polar coordinates it is shown that the number of operations is drastically reduced and can be made to approach that of fast transform algorithms. The algorithm is applied to data from the airborne ultra-wideband CARABAS SAR and shown to give a reduction in processing time of two to three orders of magnitude.

Synthetic-aperture radar processing using fast factorized back-projection

Part 1 Introduction: spotlight SAR SAR modes importance of spotlight SAR early SAR chronology. Part 2 Synthetic aperture radar fundamentals: SAR system overview imaging considerations pulse compression and range resolution synthetic aperture technique for Azimuth resolution SAR coherence requirements signal phase equation inverse SAR (ISAR) SAR sensor parametric design. Part 3 Spotlight SAR and polar format algorithm: scope of processing task polar format overview polar data storage as a two-dimensional signal correction for non-planar motion polar format algorithm limitations Taylor series expansion procedures phase of image pixels image geometric distortion image focus error equations displacements and absolute positioning. Part 4 Digital polar format processing: sampling rate conversion polyphase filters polar interpolation image scale factors image distortion correction signal history projections stabilized scene polar interpolation subpatch processing and mosaicking. Part 5 Phase errors: classification of phase error management of phase error magnitude of phase error requirements on a practical SAR motion sensor moving target effects. Part 6 Autofocus techniques: mapdrift multiple aperture mapdrift phase difference phase gradient prominent point processing considerations for space-variant refocus. Part 7 Processor design examples: the common UNIX SAR processor the ground to air imaging radar processor. Part 8 SAR system performance: image quality metrics system performance budgeting requirements on system impulse response requirements on system noise geometric distortion secondary image quality metrics test arrays. Part 9 Spotlight processing applications: spotlight processing of scan and stripmap SAR data interferometric SAR forward look SAR vibrating target detection. Part 10 Range migration algorithm: model algorithm overview analytical development discussion efficient algorithms for range migration processing. Part 11 Chirp scaling algorithm: non-dechirped signal model algorithm overview analytical development discussion. Part 12 Comparison of image formation algorithms: image formation algorithm models computational complexity memory requirements other considerations.

Ronald M. Majewski

Papers

Spotlight synthetic aperture radar : signal processing algorithms