scholarly journals Optimization of Weighting Window Functions for SAR Imaging via QCQP Approach

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 419
Author(s):  
Jin Liu ◽  
Wei Wang ◽  
Hongjun Song
Keyword(s):  
Synthetic Aperture Radar ◽  
Signal To Noise Ratio ◽  
Window Function ◽  
Synthetic Aperture ◽  
Quadratic Program ◽  
Window Functions ◽  
Sar Imaging ◽  
Imaging Simulation ◽  
Weighting Window ◽  
Aperture Radar

Weighting window functions are commonly used in Synthetic Aperture Radar (SAR) imaging to suppress the high Peak SideLobe Ratio (PSLR) at the price of probable Signal-to-Noise Ratio (SNR) loss and mainlobe widening. In this paper, based on the method of designing a mismatched filter, we have proposed a Quadratically Constrained Quadratic Program (QCQP) approach, which is a convex that can be solved efficiently, to optimize the weighting window function with both amplitude and phase, expecting to offer better imaging performance, especially on PSLR, SNR loss, and mainlobe width. According to this approach and its modified form, we are able to design window functions to optimize the PSLR or the SNR loss under different kinds of flexible and practical constraints. Compared to the ordinary real-valued and symmetric window functions, like the Taylor window, the designed window functions are complex-valued and can be asymmetric. By using Synthetic Aperture Radar (SAR) point target imaging simulation, we show that the optimized weighting window function can clearly show the weak target hidden in the sidelobes of the strong target.

scholarly journals A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging

2016 ◽  
Vol 3 (11) ◽  
pp. 446-462 ◽  
Author(s):  
H. Vickers ◽  
M. Eckerstorfer ◽  
E. Malnes ◽  
Y. Larsen ◽  
H. Hindberg
Keyword(s):  
Synthetic Aperture Radar ◽  
Snow Avalanche ◽  
Synthetic Aperture ◽  
Sar Imaging ◽  
Aperture Radar

Synthetic aperture radar (SAR) imaging based on low-cost FMCW radar for mini-UAVs

2016 ◽  
Author(s):  
Yarleque Medina ◽  
Manuel Augusto ◽  
Alvarez Navarro ◽  
Sthefany Martinez Odiaga ◽  
Hansel Joussef ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Low Cost ◽  
Synthetic Aperture ◽  
Fmcw Radar ◽  
Sar Imaging ◽  
Aperture Radar

scholarly journals Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4981
Author(s):  
Raghu G. Raj ◽  
Maxine R. Fox ◽  
Ram M. Narayanan
Keyword(s):  
Neural Networks ◽  
Synthetic Aperture Radar ◽  
Signal To Noise Ratio ◽  
Classification Performance ◽  
Synthetic Aperture ◽  
Ultrasound Images ◽  
Target Classification ◽  
Radar Images ◽  
High Resolution Imagery ◽  
Aperture Radar

The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images. Convolutional neural networks (CNNs) trained using extensive datasets are being investigated recently. However, large CNNs and wavelet scattering networks (WSNs), which share similar properties, have extensive memory requirements and are not readily extendable to other datasets and architectures—and especially in the context of adaptive and online learning. In this paper, we quantitatively study several quantization schemes on WSNs designed for target classification using X-band synthetic aperture radar (SAR) data and investigate their robustness to low signal-to-noise ratio (SNR) levels. A detailed study was conducted on the tradeoffs involved between the various quantization schemes and the means of maximizing classification performance for each case. Thus, the WSN-based quantization studies performed in this investigation provide a good benchmark and important guidance for the design of quantized neural networks architectures for target classification.

scholarly journals Focusing Bistatic Forward-looking Synthetic Aperture Radar Based on An Improved Hyperbolic Range Model and A Modified Omega-K algorithm

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3792
Author(s):  
Chenchen Wang ◽  
Weimin Su ◽  
Hong Gu ◽  
Jianchao Yang
Keyword(s):  
Synthetic Aperture Radar ◽  
High Order ◽  
Synthetic Aperture ◽  
Approximation Procedure ◽  
Square Root ◽  
Slant Range ◽  
Receiver System ◽  
Sar Imaging ◽  
Aperture Radar ◽  
Forward Looking

For parallel bistatic forward-looking synthetic aperture radar (SAR) imaging, the instantaneous slant range is a double-square-root expression due to the separate transmitter-receiver system form. The hyperbolic approximation provides a feasible solution to convert the dual square-root expression into a single-square-root expression. However, some high-order terms of the range Taylor expansion have not been considered during the slant range approximation procedure in existing methods, and therefore, inaccurate phase compensation occurs. To obtain a more accurate compensation result, an improved hyperbolic approximation range form with high-order terms is proposed. Then, a modified omega-K algorithm based on the new slant range form is adopted for parallel bistatic forward-looking SAR imaging. Several simulation results validate the effectiveness of the proposed imaging algorithm.

scholarly journals 3D SAR (SYNTHETIC APERTURE RADAR) IMAGING SIMULATION USING JAVA

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Erfansyah Ali ◽  
Andriyan B Suksmono
Keyword(s):  
Embedded System ◽  
Synthetic Aperture Radar ◽  
Radar Imaging ◽  
Synthetic Aperture ◽  
Sar Image ◽  
C Programming Language ◽  
C Programming ◽  
Imaging Simulation ◽  
Radar Resolution ◽  
Aperture Radar

One of methods in remote sensing is Synthetic Aperture Radar (SAR). When combined with Range DopplerAlgorithm (RDA) can produce smaller radar resolution only by using normal sized antenna placed atplatform. RDA is able to generate much wider aperture �synthetic� antenna, resulting very narrow beamwidthwhen reach earth's ground. By using already established 2D SAR methods in accuracy andprocessing speed this 3D SAR simulation was developed. Simulated on 15 x 15 pixels grayscale targets atdifferent heights, 3D SAR developed on this research can detect object's height accurately. Thissimulation was developed using JAVA as steppingstone in implementing SAR image processing in smallsystem like embedded system or micro computing which normally using C programming language.

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