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

This paper addresses a novel SAR imaging scheme based on 1-bit sampling assisted with a single-frequency threshold. The 1-bit sampling approach is able to considerably reduce the quantization cost. However, when the sampling technique simplifies the SAR system by reducing the word length of each sample to only 1 bit, amplitude information of the SAR echo is lost and high-order harmonics are introduced, degrading the SAR imaging quality. The strategy of single-frequency threshold is able to linearly maintain the amplitude information and shifts the spectra of the harmonics away from the imaging component caused by the intermodulation. Hence, the imaging quality using 1-bit sampled data can be guaranteed. By selecting different SAR parameter groups according to a comprehensive consideration on spectrum aliasing, filter mismatching, and radio frequency interfering, a good tradeoff can be achieved between imaging quality and system simplification. Examples of ideal scatterers and a real measured scene are provided for quantitative analysis. Real measured RADARSAT-1 data are also imaged using the proposed scheme to validate its effectiveness.

One-Bit SAR Imaging Based on Single-Frequency Thresholds

For the multireceiver synthetic aperture sonar (SAS), the point target reference spectrum (PTRS) in the two-dimensional (2D) frequency domain and azimuth modulation in the range Doppler domain were first deduced based on a numerical evaluation method and accurate time delay. Then, the difference between the PTRS and azimuth modulation generated the coupling term in the 2D frequency domain. Compared with traditional methods, the PTRS, azimuth modulation and coupling term was better at avoiding approximations. Based on three functions, an imaging algorithm is presented in this paper. Considering the fact that the coupling term is characterized by range variance, the range-dependent sub-block processing method was exploited to perform the decoupling. Simulation results showed that the presented method improved the imaging performance across the whole swath in comparison with existing multireceiver SAS processor. Furthermore, real data was used to validate the presented method.

https://www.mdpi.com/2072-4292/11/6/672/pdf

An Imaging Algorithm for Multireceiver Synthetic Aperture Sonar

Wide-beam SAR autofocus based on blind resampling

At present, the resolution of the most advanced airborne microwave photonic synthetic aperture radar (SAR) can reach the order of centimeters or even millimeters, so the two-dimensional spatial variation and two-dimensional coupling characteristics of motion error will become more serious. In this paper, based on the advantages of nonlinear chirp scaling (NCS) and resampling (RS) processing, a microwave photonic SAR full-aperture autofocus algorithm based on a cascaded NCS-RS is proposed. Firstly, the proposed algorithm combines the typical two-step MoCo and chirp-Z transform (CZT) to correct the range spatial variant characteristics of motion error. Then, a cascaded NCS-RS processing is used to correct the azimuth spatial variant characteristics of motion error, in which NCS processing is introduced before range cell migration correction (RCMC) and RS processing is introduced after RCMC. Finally, the RS in cascaded NCS-RS processing is modified to change with range to correct the range-azimuth coupling characteristic of motion error. The three steps of the algorithm belong to the full-aperture processing, which avoids the problems of grating lobes and image stitching caused by the sub-aperture algorithm. The estimation of the parameters in NCS-RS processing is modeled as a high-dimensional optimization problem. Before solving this optimization problem, it is converted to multiple one-dimensional optimization problems. The results of processing simulated and measured data verify the effectiveness of the proposed algorithm. 

Full-Aperture Processing of Airborne Microwave Photonic SAR Raw Data

Aperture-dependent motion compensation (MOCO) is crucial for high-resolution airborne synthetic aperture radar (SAR) imaging, which can suppress image degradation especially for SAR systems working at a high-frequency waveband, such as millimetre-wave (MMW) SAR. The current precise topography and aperture (PTA) dependent MOCO algorithm aims to compensate the azimuth-dependent motion error by using a modified azimuth matched filtering. However, the matched filter is deduced with the principle of stationary phase without considering the effects from the azimuth-dependent motion errors, which limits its precision in focusing MMW SAR data involving severe motion errors. This study proposes a refined PTA algorithm with a precise post matched filter established by the method of series reversion. The range-variant characteristic of residual motion errors is also investigated for an improved precision. Simulated and real MMW SAR data sets are used to validate the effectiveness of the proposal.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rsn.2016.0195

Precise aperture-dependent motion compensation for high-resolution synthetic aperture radar imaging

Conventional two-step motion compensation (MOCO) method is widely adapted for airborne synthetic aperture radar (SAR) imaging due to its conciseness combining with the SAR focusing procedure. For two-step MOCO, range-independent compensation is processed before range cell migration correction (RCMC), and the range-dependent phase correction is implemented after RCMC. However, the accuracy of RCMC would be seriously decreased by the residual range-dependent phase, which is a fatal problem for high-resolution millimeter-wave (MMW) SAR imaging. In this paper, an extensive investigation on the RCMC accuracy is provided by establishing an accurate formula expression between the range cell migration error and the residual range-dependent phase error. One-step MOCO-based SAR imaging algorithm is investigated by compensating the range-dependent motion error before RCMC, so the presence of range cell migration error would be significantly suppressed. What is more, a modified azimuth match filtering (AMF) function is given by precise topography and aperture-dependent motion compensation (PTA) method to overcome the residual azimuth-dependent phase error in the azimuth compression stage. Both simulated and real-measured MMW SAR data sets are used to validate the analysis for high-resolution airborne SAR imaging.

https://link.springer.com/content/pdf/10.1186%2Fs13634-016-0416-1.pdf

Range cell migration correction analysis of one-step and two-step motion compensation for millimeter-wave airborne SAR imaging

Study on SAR image segmentation based on K-means clustering. Analyzes and refined the adaptive moving K-means clustering algorithm by refined the adaptation degree function computation method which dividing the raw adaptation degree function by a direct ratio function of the sample number in clustering and presenting a new sample point separating rule on the clustering area which has the largest adaptation degree function. Millimeter SAR image segment results verify that the refined algorithm have better quality than K-means clustering algorithms in paper for city, road and bridge. Refined K-means clustering algorithm are more efficiency than the adaptive moving K-means clustering algorithm.

Refined SAR image segmentation algorithm based on K-means clustering

Doppler beam sharpening (DBS) is an effective working mode for quickly scanning and imaging to a large-scale scene. In this mode, the antenna beam direction is changing so that images from different slant angles can be spliced into a wide scale of image. In practical applications, the aircraft attitude is always affected by the unstable atmosphere, so the beam pointing has deviation compared with the ideal value, which introduces errors to image stitching and geometric correction. For this problem, a robust image stitching and geometric correction method is proposed in this paper, it has better performance when the platform is unstable, which is uncomplicated and suitable for real-time processing. The effectiveness of the proposed method is verified by real measured data experiments.

A Robust Image Stitching and Geometric Correction Method for Doppler Beam Sharpening Imaging

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Papers

Precise aperture-dependent motion compensation for high-resolution synthetic aperture radar imaging

Range cell migration correction analysis of one-step and two-step motion compensation for millimeter-wave airborne SAR imaging

Refined SAR image segmentation algorithm based on K-means clustering

A Robust Image Stitching and Geometric Correction Method for Doppler Beam Sharpening Imaging