scholarly journals Image Formation of Azimuth Periodically Gapped SAR Raw Data with Complex Deconvolution

2019 ◽  
Vol 11 (22) ◽  
pp. 2698 ◽  
Author(s):  
Yulei Qian ◽  
Daiyin Zhu
Keyword(s):  
Complete Data ◽  
Synthetic Aperture ◽  
Phase Compensation ◽  
Raw Data ◽  
New Approach ◽  
Imaging Algorithm ◽  
Robust Implementation ◽  
Sar Imaging ◽  
Novel Method ◽  
Sar Data

The phenomenon of periodical gapping in Synthetic Aperture Radar (SAR), which is induced in various ways, creates challenges in focusing raw SAR data. To handle this problem, a novel method is proposed in this paper. Complex deconvolution is utilized to restore the azimuth spectrum of complete data from the gapped raw data in the proposed method. In other words, a new approach is provided by the proposed method to cope with periodically gapped raw SAR data via complex deconvolution. The proposed method provides a robust implementation of deconvolution for processing azimuth gapped raw data. The proposed method mainly consists of phase compensation and recovering the azimuth spectrum of raw data with complex deconvolution. The gapped data become sparser in the range of the Doppler domain after phase compensation. Then, it is feasible to recover the azimuth spectrum of the complete data from gapped raw data via complex deconvolution in the Doppler domain. Afterwards, the traditional SAR imaging algorithm is capable of focusing the reconstructed raw data in this paper. The effectiveness of the proposed method was validated via point target simulation and surface target simulation. Moreover, real SAR data were utilized to further demonstrate the validity of the proposed method.

scholarly journals High-Resolution Squinted SAR Imaging Algorithm Applied in the Near-Field Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shijia Wang ◽  
Shibo Wang ◽  
Wanli Liu
Keyword(s):  
Cell Migration ◽  
Near Field ◽  
Synthetic Aperture ◽  
Distortion Compensation ◽  
Imaging Algorithm ◽  
Field Environment ◽  
Imaging Result ◽  
Range Cell ◽  
Sar Imaging ◽  
Range Cell Migration

In the squinted synthetic aperture radar (SAR) imaging of the near-field environment, range-dependent characteristic of squint angle cannot be ignored, which causes azimuth-dependent range cell migration (RCM) after linear range walk correction (LRWC). In this study, an efficient SAR imaging algorithm applied in the near-field environment is proposed. In the processing of the range focusing, LRWC is firstly used to remove the linear RCM. Then, the residual LRCM is expanded into azimuth-invariant and azimuth-variant terms in consideration of the residual LRCM of azimuth-dependent. Range cell migration azimuth scaling (RCMAS) is designed to remove the azimuth-variant term before secondary range compression (SRC) and range compression (RC). In the azimuth focusing, azimuth distortion compensation (ADC) is performed to compensate the azimuth distortion, following which azimuth nonlinear chirp scaling (ANCS) is applied to equalize the frequency modulation (FM) rate for azimuth compression (AC). The simulated results show that more accurate and improved imaging result can be obtained with the proposed algorithm.

A Compression Algorithm for SAR Data after Range Focusing

2013 ◽  
Vol 694-697 ◽  
pp. 2877-2880
Author(s):  
Shang Chun Zeng ◽  
Xian Lin Deng ◽  
Yi Xian Chen ◽  
Yun Xia Xie ◽  
Zhao Da Zhu
Keyword(s):  
Synthetic Aperture Radar ◽  
Linear Prediction ◽  
Compression Algorithm ◽  
Synthetic Aperture ◽  
Bit Rate ◽  
Compression Method ◽  
Raw Data ◽  
Adaptive Quantization ◽  
Difference Series ◽  
Sar Data

It is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method after range focusing advised in corresponding reference.

scholarly journals CNN-Based Vehicle Target Recognition with Residual Compensation for Circular SAR Imaging

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 555
Author(s):  
Rongchun Hu ◽  
Zhenming Peng ◽  
Juan Ma ◽  
Wei Li
Keyword(s):  
Recognition Accuracy ◽  
Target Recognition ◽  
Synthetic Aperture ◽  
Speckle Reduction ◽  
Reduction Algorithm ◽  
Target Image ◽  
Thinning Algorithm ◽  
Imaging Algorithm ◽  
Residual Image ◽  
Sar Imaging

The contour thinning algorithm is an imaging algorithm for circular synthetic aperture radar (SAR) that can obtain clear target contours and has been successfully used for circular SAR (CSAR) target recognition. However, the contour thinning imaging algorithm loses some details when thinning the contour, which needs to be improved. This paper presents an improved contour thinning imaging algorithm based on residual compensation. In this algorithm, the residual image is obtained by subtracting the contour thinning image from the traditional backprojection image. Then, the compensation information is extracted from the residual image by repeatedly using the gravitation-based speckle reduction algorithm. Finally, the extracted compensation image is superimposed on the contour thinning image to obtain a compensated contour thinning image. The proposed algorithm is demonstrated on the Gotcha dataset. The convolutional neural network (CNN) is used to recognize the target image. The experimental results show that the image after compensation has a higher target recognition accuracy than the image before compensation.

scholarly journals Extended Subaperture Imaging Method for Airborne Low Frequency Ultrawideband SAR Data

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4516
Author(s):  
Daoxiang An ◽  
Wu Wang ◽  
Leping Chen
Keyword(s):  
Low Frequency ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Theory Analysis ◽  
Range Cell ◽  
Imaging Results ◽  
Sar Imaging ◽  
Sar Data ◽  
Range Cell Migration

The subaperture processing is one of the essential strategies for low frequency ultrawideband synthetic aperture radar (LF UWB SAR) imaging, especially for the real-time LF UWB SAR imaging because it can improve the parallelization of the imaging algorithm. However, due to the longer synthetic aperture of LF UWB SAR, the traditional subaperture imaging encounters an azimuth ambiguities problem, which severely degrades the focused quality of the imaging results. In this paper, the reason for the presence of azimuth ambiguities in the LF UWB SAR subaperture imaging and its influence on image quality is first analyzed in theory. Then, an extended subaperture imaging method based on the extension of subaperture length before Range Cell Migration Correction (RCMC) was proposed. By lengthening the subaperture length, the azimuth ambiguities are effectively eliminated. Finally, the extended part of subaperture is wiped off before the azimuth compression (AC), and the LF UWB SAR image of high focused quality is obtained. The correctness of the theory analysis and the effectiveness of the proposed method have been validated through simulated and real LF UWB SAR data.

A Study of Space-Borne Sliding Spotlight SAR Imaging Algorithm

2012 ◽  
Vol 429 ◽  
pp. 128-131
Author(s):  
Ying Ying Chen ◽  
Xin Jia
Keyword(s):  
Synthetic Aperture Radar ◽  
Operating Mode ◽  
Synthetic Aperture ◽  
Imaging Algorithm ◽  
Sar Imaging ◽  
Simulation Results ◽  
Azimuth Resolution ◽  
Aperture Radar

In recent years, a new operating mode for Synthetic Aperture Radar (SAR) system, that is sliding spotlight mode. This mode has many advantages, such as, it has better azimuth resolution than stripmap mode. At the same time, the sliding spotlight mode has better coverage than the spotlight mode. In this paper, we present the sliding spotlight mode algorithm and analyze its azimuth resolution and coverage. The simulation results prove it.

scholarly journals LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2263
Author(s):  
Haileleol Tibebu ◽  
Jamie Roche ◽  
Varuna De Silva ◽  
Ahmet Kondoz
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Test Results ◽  
New Approach ◽  
Occupancy Grid ◽  
Laser Scanners ◽  
Novel Method ◽  
Occupancy Grid Mapping ◽  
Cartesian Coordinate

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

Sign in / Sign up

Export Citation Format

Share Document