{"title":"Radar Holography using compressed sensing for point targets","authors":"Qian Zhu, J. Mathews, R. Volz","doi":"10.1109/USNC-URSI-NRSM.2014.6928072","DOIUrl":null,"url":null,"abstract":"The scientific community has been interested in observing meteors for decades due to the role of meteoriods in studying space weather, the upper atmosphere of the meteor zone, and various aspects of plasma physics. Meteor events detected by single receiver radar system are usually shown in the Range-Time-Intensity (RTI) plot. While we can not acquire meteor information in the cross-range domain with traditional single-receiver detection methods, the Radar Holography is a good alternative. The mathematical basis of radar holography is the fourier transform relationship[Woodman, 1997]. The images of interested targets can be obtained from the scattered electromagnetic field at a finite number of sampling points on the groud (Receiver Array). For point targets, the interested signal is natural sparse and compressible, therefore, by introducing the compressed sensing (CS) concept, we can approximately reconstruct the signal from only a few measurements, which can be less than the number required by the Nyquist-Shannon sampling theorem. However, the sparse approximation based on the CS is a NP-hard optimization problem therefore its solution can not be found easily. It is shown that by satisfying certain reconstruction conditions [Candes et al., 2008], we can approximate the original problem by l1 norm minimization, which is easily solvable by various algorithms. In this paper, we will apply the CS method to radar holography in the range, doppler frequency and cross-range domain for point targets. For modeling, a discrete linear radar signal mode is derived, and the sparse approximation based on CS has been applied. We demonstrate that this approach can provide satisfied resolution in both the temporal and spatial domain by better limiting usual ringing effect.","PeriodicalId":277196,"journal":{"name":"2014 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/USNC-URSI-NRSM.2014.6928072","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The scientific community has been interested in observing meteors for decades due to the role of meteoriods in studying space weather, the upper atmosphere of the meteor zone, and various aspects of plasma physics. Meteor events detected by single receiver radar system are usually shown in the Range-Time-Intensity (RTI) plot. While we can not acquire meteor information in the cross-range domain with traditional single-receiver detection methods, the Radar Holography is a good alternative. The mathematical basis of radar holography is the fourier transform relationship[Woodman, 1997]. The images of interested targets can be obtained from the scattered electromagnetic field at a finite number of sampling points on the groud (Receiver Array). For point targets, the interested signal is natural sparse and compressible, therefore, by introducing the compressed sensing (CS) concept, we can approximately reconstruct the signal from only a few measurements, which can be less than the number required by the Nyquist-Shannon sampling theorem. However, the sparse approximation based on the CS is a NP-hard optimization problem therefore its solution can not be found easily. It is shown that by satisfying certain reconstruction conditions [Candes et al., 2008], we can approximate the original problem by l1 norm minimization, which is easily solvable by various algorithms. In this paper, we will apply the CS method to radar holography in the range, doppler frequency and cross-range domain for point targets. For modeling, a discrete linear radar signal mode is derived, and the sparse approximation based on CS has been applied. We demonstrate that this approach can provide satisfied resolution in both the temporal and spatial domain by better limiting usual ringing effect.
几十年来,由于流星体在研究空间天气、流星区上层大气和等离子体物理的各个方面的作用,科学界一直对观测流星感兴趣。单接收机雷达探测到的流星事件通常用距离-时间-强度(RTI)图表示。传统的单接收机探测方法无法在跨距离域获取流星信息,而雷达全息技术是一种很好的替代方法。雷达全息的数学基础是傅里叶变换关系[伍德曼,1997]。从地面上有限个采样点的散射电磁场中获得感兴趣目标的图像(接收机阵列)。对于点目标,感兴趣的信号是自然稀疏和可压缩的,因此,通过引入压缩感知(CS)的概念,我们可以仅从少量的测量中近似地重建信号,这些测量可能少于Nyquist-Shannon采样定理所需的数量。然而,基于CS的稀疏逼近是一个NP-hard优化问题,因此它的解不容易找到。研究表明,通过满足一定的重构条件[Candes et al., 2008],我们可以通过l1范数最小化来近似原问题,该问题易于通过各种算法求解。本文将CS方法应用于点目标的距离域、多普勒频率域和跨距离域的雷达全息成像。为了建模,推导了一个离散线性雷达信号模式,并应用了基于CS的稀疏逼近。结果表明,该方法可以更好地限制通常的振铃效应,从而在时域和空域上提供满意的分辨率。