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A Generalized omega-k algorithm to Process Translationally Variant Bistatic-SAR Data Based on Two-Dimensional Stolt Mapping

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2014年第10期52卷 6597-6614页

作者： Wu, Junjie Li, Zhongyu Huang, Yulin Yang, Jianyu Liu, Qing Huo Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Peoples R China Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

In translationally variant (TV) bistatic synthetic aperture radar (BSAR), 2-D spatial variation is a major problem to be tackled. In this paper, a generalized omega-k imaging algorithm to deal with this problem is proposed. The method utilizes a point target reference spectrum of the generalized Loffeld's bistatic formula (LBF) (GLBF). Without the bistatic-deformation term, GLBF is the latest development of LBF. Similar to the monostatic case, it has a much simpler form than other point target reference spectra. Based on the spatial linearization of GLBF, the Stolt mapping relationship is derived. Different from the traditional omega-k algorithms for monostatic SAR and translationally invariant BSAR, this approach uses a 2-D Stolt frequency transformation. Through this transformation, the method can deal with the 2-D spatial variation. It can also consider the linear spatial variation of Doppler parameters, which is usually not considered in the previous publications on bistatic omega-k algorithms. This method can handle the cases of TV-BSAR with different trajectories, different velocities, high squint angles, and large bistatic angles. In addition, a compensation method for the phase error caused by the linearization is discussed. Numerical simulations and experimental data processing verify the effectiveness of the proposed method.

关键词： Bistatic synthetic aperture radar (BSAR) generalized Loffeld's bistatic formula (LBF) (GLBF) omega-k algorithm 2-D spatial variation 2-D Stolt mapping

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An omega-k algorithm for Highly Squinted Missile-Borne SAR With Constant Acceleration

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2014年第9期11卷 1569-1573页

作者： Tang, Shiyang Zhang, Linrang Guo, Ping Zhao, Yonghong Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

This letter proposes an omega-k algorithm for missile-borne synthetic aperture radar (SAR) diving with constant acceleration. The essence of the novel method is to derive a reference function multiplication from the equivalent range history. The true range history is divided into two parts: range independent and range dependent. After compensating the range-independent part with considering all the terms, we can regard the range-dependent part as an equivalent range history with a form similar to that of conventional SAR. Then, an analytical 2-D spectrum can be used for the omega-k algorithm. The proposed algorithm can handle general missile configurations with wide swaths and high squint angles. Simulation results are presented to validate the proposed method.

关键词： Acceleration highly squinted missile-borne omega-k algorithm reference function multiplication (RFM) synthetic aperture radar (SAR)

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An Array-Radar-Based Frequency-Modulated Continuous-Wave Synthetic Aperture Radar Imaging System and Fast Detection Method for Targets

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ELECTRONICS 2025年第8期14卷 1585-1585页

作者： Wang, Chao Guo, Peiyuan Feng, Donghao Cao, Yangjie Zhang, Wenning Duan, Pengsong Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou 450000 Peoples R China Zhongyuan Univ Technol Software Coll Zhengzhou 450007 Peoples R China

This paper proposes a frequency-modulated continuous-wave synthetic aperture radar (FMCW-SAR) imaging system for fast target detection. The system's antenna array improves azimuthal resolution while maintaining low complexity using a 44-element equivalent virtual array and improves the data acquisition efficiency by employing the trigger and MCU control board. A series of improved algorithms are adopted to increase the speed of radar imaging and achieve fast detection. To solve the problem of large data volumes in traditional array antenna switching control methods, an array switching control algorithm is proposed based on the enhanced ordered statistical constant false alarm rate (EOS-CFAR). The data volume is reduced by dividing the array into several subarrays in advance. The echo signals acquired by the array switching control method are not continuous in the azimuthal direction, and data anomalies are handled by interpolating and compensating the received radar data to form compensated periodic data. The coherent background is subtracted from the padded signal using recursive averaging, resulting in high-resolution imaging while improving the data-processing speed. The TensorFlow-based omega-k algorithm is employed for synthetic aperture radar (SAR) imaging, which customizes the optimization of TensorFlow for array radar signals. For the radar signal phase optimization, an improved Adam Optimizer optimizes the phase of the radar signal to maintain phase smoothing, thereby improving the clarity of the radar image. The omega-k algorithm is optimized by TensorFlow and accelerated on the GPU to improve the efficiency of the large-scale fast Fourier transform (FFT) and Stolt interpolation operations, which improves the speed of radar imaging and enables fast detection.

关键词： array antenna FMCW-SAR fast target detection constant false alarm rate omega-k algorithm

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Translational-Invariant Ultrahigh-Resolution Spaceborne Bistatic SAR Focusing and Image Coordinate Analysis Using Bistatic Polynomial Model

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IEEE ACCESS 2023年 11卷 54462-54475页

作者： kang, Byung-Soo Agcy Def Dev Daejon 34186 South Korea

We have proposed a novel process flow for translational-invariant (TI) and ultrahigh-resolution (UHR) bistatic synthetic aperture radar (BSAR) operations, wherein a UHR BSAR image is obtained via coherent integration of sub-aperture images, and its two-dimensional (2D) coordinates are then interpreted. The main contributions of the proposed method are as follows. First, to address additional aliasing of the TI BSAR spectrum after sub-aperture division, linear range walk compensation and its restoration are subsequently performed. This efficiently extends the duration of the Doppler domain after sub-aperture division, accommodating the skewed shape of the TI BSAR spectrum. Second, spatial-variant focusing is achieved via the bistatic polynomial omega-k algorithm (BP-OkA), for which a key consideration is highlighted to perform BP-OkA in the sub-image integration process. In its absence, the same scatterers appeared at different positions in each sub-aperture image, causing failure of coherent accumulation. Finally, to retrieve the range-azimuth coordinates of targets within an illuminated scene, we established a high-order equation using bistatic polynomials, and the 2D image coordinates of targets were determined by solving the equation. This new concept is fully compatible with the proposed BP-OkA and can be used in various spaceborne TI BSAR scenarios. All these contributions were verified using numerical simulations. Accordingly, the proposed processing flow can provide focused SAR images with accurate image coordinate interpretations for TI and UHR BSAR operations.

关键词： Spaceborne radar Focusing Synthetic aperture radar Doppler effect Geometry Transmitters Receivers Bistatic radar curved-oribt omega-k algorithm spaceborne synthetic aperture radar (SAR) ultrahigh-resolution (UHR)

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An omega-k Imaging algorithm for Translational Variant Bistatic SAR Based on Linearization Theory

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2014年第3期11卷 627-631页

作者： Li, Zhongyu Wu, Junjie Yi, Qingying Huang, Yulin Yang, Jianyu Univ Elect Sci & Technol China Sch Elect Engn Chengdu 610054 Peoples R China

Doppler parameters, range cell migrations (RCMs), and higher order coupling terms in the raw data of translational variant bistatic synthetic aperture radar (TV-BiSAR) exhibit 2-D spatial variations. The 2-D spatial variations result in significant performance degradation of TV-BiSAR imaging. To solve these problems, an omega-k imaging algorithm based on linearization theory is proposed in this letter. Compared with the traditional omega-k algorithm that uses the 1-D Stolt transformation to eliminate only the spatial variations in the range direction, the proposed algorithm applies the 2-D Stolt transformations to achieve the goal of both 2-D frequency linearization and 2-D spatial-domain linearization. After the 2-D Stolt transformations, a focused image can be obtained by performing a 2-D inverse fast Fourier transform (IFFT). In the proposed omega-k imaging algorithm, the 2-D spatial variations of the Doppler parameters, RCM, and higher order coupling terms for TV-BiSAR can be simultaneously eliminated. However, in previous publications about BiSAR imaging algorithms, the spatial variations in the azimuth direction are barely considered, which reduces the imaging accuracy. Numerical simulations verify the effectiveness of the proposed method.

关键词： Bistatic synthetic aperture radar (BiSAR) omega-k algorithm translational variant two-dimensional spatial variations two-dimensional Stolt transformations

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A novel modified omega-k algorithm for circular trajectory scanning SAR imaging using series reversion

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2013年第1期2013卷 1-12页

作者： Liao, Yi Xing, Meng-dao Zhang, Lei Bao, Zheng Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

This article presents a modified omega-k algorithm for circular trajectory scanning synthetic aperture radar (CTSSAR) imaging. Due to the curvature of circular trajectory, it is difficult to have access to the two-dimensional frequency spectrum for CTSSAR via the principle of stationary phase (POSP), as conventional SAR imaging methods RD and CS. Herein, the analytic point target spectrum is first derived by series reversion and the POSP, based on which a modified omega-k algorithm is developed to focus data accurately. The accuracy can be controlled by keeping enough terms in the two series expansions so that a well-focused image can be achieved with a proper range approximation. After detailed analyses and experiments, the fourth-order approximation is proved to be the best choice. Furthermore, the computational efficiency is evaluated by comparing the given method with the back projection algorithm and other methods with different approximated orders. The proposed algorithm is verified to be the best one in terms of computational burden. A well-focused image is obtained by simulations, validating the feasibility of the proposed algorithm.

关键词： Circular trajectory scanning (CTS) synthetic aperture radar (SAR) (CTSSAR) Series reversion omega-k algorithm

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Using omega-k algorithm for bistatic synthetic aperture radar image formation based on modified extended Loffeld's bistatic formula

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IET RADAR SONAR AND NAVIGATION 2013年第4期7卷 383-392页

作者： Zamani, Mohammad Modarres-Hashemi, Mahmood Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran

Bistatic synthetic aperture radar (BSAR) as a way of Earth remote sensing has been developed considerably in recent years, both theoretically and practically due to its unsubstitutable services. BSAR frequency-domain processing algorithms are efficient ways of image formation in comparison to ideal two-dimensional matched filtering and to relatively accurate and time-consuming time-domain algorithms. Among these frequency-domain algorithms, omega-k is the most precise. The starting and key step of frequency-domain algorithms is the derivation of bistatic spectrum. Recently, a new bistatic spectrum is reported, which is probably the latest and the most modified version of Loffeld's bistaic formula (LBF), maintaining its accuracy even in azimuth-variant configurations with high squint angles. So far, this spectrum has only been used within range-Doppler algorithm to process BSAR data. The authors investigate the possibility and results of applying this modified version of LBF as a basis for omega-k algorithm. Two approaches, based on Stolt interpolation and inverse scaled Fourier transform, are examined and their effectiveness in general azimuth-variant geometry is validated through several simulations. The proposed implementations show higher performance in terms of image quality measurements as compared to extended LBF-based implementations.

关键词： matched filters radar imaging remote sensing by radar omega-k algorithm bistatic synthetic aperture radar image formation modified extended Loffeld's bistatic formula Earth remote sensing frequency-domain processing algorithms two-dimensional matched filtering time-domain algorithms frequency-domain algorithms bistatic spectrum azimuth-variant configurations Stolt interpolation inverse scaled Fourier transform

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AN omega-k IMAGING algorithm FOR TRANSLATIONAL INVARIANT BISTATIC FMCW SAR

AN OMEGA-K IMAGING ALGORITHM FOR TRANSLATIONAL INVARIANT BIS...

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Bao, Yi Li, Zhongyu Wu, Junjie Huang, Yulin Yang, Jianyu Univ Elect Sci & Technol China Sch Elect Engn Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9781479957750

The combination of frequency-modulated continuous-wave (FMCW) technology and bistatic synthetic aperture radar (SAR) promises a small, light-weight, cost-effective and high-resolution remote sensing system. In this paper, an omega-k imaging algorithm based on linearization theory to focus the raw data of translational invariant (TI) bistatic FMCW SAR is proposed. This method utilizes a bistatic point target reference spectrum (BPTRS) of generalized Loffeld's bistatic formula (GLBF), where the motion during the long signal duration time is taken into account. The proposed BPTRS significantly simplify the TI bistatic FMCW SAR processing. Based on the spatial linearization of GLBF, the Stolt transformation is further derived. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method.

关键词： Translational Invariant Bistatic Frequency-modulated Continuous-wave Synthetic Aperture Radar Generalized Loffeld's bistatic formula omega-k algorithm Stolt transformation

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Deep Learning Approach for Object Classification on Raw and Reconstructed GBSAR Data

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REMOTE SENSING 2022年第22期14卷 5673-5673页

作者： kacan, Marin Turcinovic, Filip Bojanjac, Dario Bosiljevac, Marko Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb 10000 Croatia

The availability of low-cost microwave components today enables the development of various high-frequency sensors and radars, including Ground-based Synthetic Aperture Radar (GBSAR) systems. Similar to optical images, radar images generated by applying a reconstruction algorithm on raw GBSAR data can also be used in object classification. The reconstruction algorithm provides an interpretable representation of the observed scene, but may also negatively influence the integrity of obtained raw data due to applied approximations. In order to quantify this effect, we compare the results of a conventional computer vision architecture, ResNet18, trained on reconstructed images versus one trained on raw data. In this process, we focus on the task of multi-label classification and describe the crucial architectural modifications that are necessary to process raw data successfully. The experiments are performed on a novel multi-object dataset RealSAR obtained using a newly developed 24 GHz (GBSAR) system where the radar images in the dataset are reconstructed using the omega-k algorithm applied to raw data. Experimental results show that the model trained on raw data consistently outperforms the image-based model. We provide a thorough analysis of both approaches across hyperparameters related to model pretraining and the size of the training dataset. This, in conclusion, shows how processing raw data provides overall better classification accuracy, it is inherently faster since there is no need for image reconstruction and it is therefore useful tool in industrial GBSAR applications where processing speed is critical.

关键词： object classification radar image reconstruction convolutional neural networks ResNet18 GBSAR omega-k algorithm

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Accelerating short range MIMO imaging with optimized Fourier processing 23

Accelerating short range MIMO imaging with optimized Fourier...

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Conference on Passive and Active MillimeterWave Imaging XXIII

作者： Fromenteze, Thomas Yurduseven, Okan Fabien, Berland Cyril, Decroze Smith, David R. Yarovoy, Alexander G. Univ Limoges XLIM UMR 7252 F-87000 Limoges France Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Ctr Wireless Innovat Inst Elect Commun & Informat Technol Belfast BT3 9DT Antrim North Ireland Duke Univ Ctr Metamat & Integrated Plasmon Durham NC 27708 USA Delft Univ Technol Microwave Sensing Signals & Syst NL-2628 CD Delft Netherlands

ISBN: (数字)9781510636002

ISBN: (纸本)9781510636002

In this paper, we describe the recent development of new algorithms applied to short-range radar imaging. Facing the limitations of classical backpropagation algorithms, the use of techniques based on Fast Fourier Transforms has led to substantial image computation accelerations, especially for Multiple-Input Multiple-Output systems. The necessary spatial interpolation and zero-padding steps are still particularly limiting in this context, so it is proposed to replace it by a more efficient matrix technique, showing improvements in memory consumption, image computation speed and reconstruction quality.

关键词： MIMO radar microwave imaging millimeter wave imaging Fourier processing omega-k algorithm

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