This paper describes the available compression algorithms for SAR (synthetic aperture radar) raw data. The compression performances of three typical algorithms have been compared and the BAVQ algorithm is selected in view of the trade-off between the performance and the complexity. The hardware implementation scheme of the BAVQ encoder has been designed and the DSP hardware simulation result proves the feasibility of the DSP implementation.
{"title":"DSP hardware implementation of BAVQ encoding for SAR raw data","authors":"Leyu Zhu, Wen Hong, Jun Wang, Yunneng Yuan","doi":"10.1109/NRC.2002.999691","DOIUrl":"https://doi.org/10.1109/NRC.2002.999691","url":null,"abstract":"This paper describes the available compression algorithms for SAR (synthetic aperture radar) raw data. The compression performances of three typical algorithms have been compared and the BAVQ algorithm is selected in view of the trade-off between the performance and the complexity. The hardware implementation scheme of the BAVQ encoder has been designed and the DSP hardware simulation result proves the feasibility of the DSP implementation.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128623871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the application of nonuniform sampling reconstruction to sparse array beamforming to gain insight into its potential and identify additional challenges. The central concept of the nonuniform sampling beamformer is to use the nonuniform samples to reconstruct the samples of a uniform array with the same sampling density as the nonuniform array and then perform conventional beamforming. Our results show the potential of replacing elements in a sparse uniform linear array with uniform linear subarrays and using a nonuniform sampling reconstruction formula in improving the near-in (bandwidth supported by the array sampling density) grating lobes performance. However, this improved performance comes at the expense of significantly worsened performance in the out-of-band region (beyond the near-in grating lobe region). Additionally, the performance of the nonuniform sampling beamformer is extremely sensitive to phase noise. Although increasing the sampling density improved performance, the out-of-band performance and sensitivity are still areas of concern for most sparse arrays.
{"title":"Nonuniform sampling reconstruction applied to sparse array beamforming","authors":"S. Berger","doi":"10.1109/NRC.2002.999701","DOIUrl":"https://doi.org/10.1109/NRC.2002.999701","url":null,"abstract":"We investigate the application of nonuniform sampling reconstruction to sparse array beamforming to gain insight into its potential and identify additional challenges. The central concept of the nonuniform sampling beamformer is to use the nonuniform samples to reconstruct the samples of a uniform array with the same sampling density as the nonuniform array and then perform conventional beamforming. Our results show the potential of replacing elements in a sparse uniform linear array with uniform linear subarrays and using a nonuniform sampling reconstruction formula in improving the near-in (bandwidth supported by the array sampling density) grating lobes performance. However, this improved performance comes at the expense of significantly worsened performance in the out-of-band region (beyond the near-in grating lobe region). Additionally, the performance of the nonuniform sampling beamformer is extremely sensitive to phase noise. Although increasing the sampling density improved performance, the out-of-band performance and sensitivity are still areas of concern for most sparse arrays.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127618645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mehrotra developed a jerk model for tracking highly maneuvering targets in 1997, which include terms at the most up to the third order derivatives of target position. The model is investigated in this paper. By theoretical analysis, it is shown that the filter, which based on the jerk model, may suffer from deterministic steady state estimation deviations. To find a way out of this question, a current statistic jerk model, for short cs-jerk, is developed, in which the jerk maneuvering is assumed to be an exponential correlated stochastic process with non-zero mean. It consists of the cs-jerk model of target motion, and a tracking filter with compatible order. The stable performance of the cs-jerk model is also analyzed and the result indicates that the cs-jerk model eliminates performance limitation of the jerk model. The improved performance of the cs-jerk model over the jerk model is illustrated through simulation.
{"title":"A motion model for tracking highly maneuvering targets","authors":"Qiao Xiangdong, W. Baoshu","doi":"10.1109/NRC.2002.999767","DOIUrl":"https://doi.org/10.1109/NRC.2002.999767","url":null,"abstract":"Mehrotra developed a jerk model for tracking highly maneuvering targets in 1997, which include terms at the most up to the third order derivatives of target position. The model is investigated in this paper. By theoretical analysis, it is shown that the filter, which based on the jerk model, may suffer from deterministic steady state estimation deviations. To find a way out of this question, a current statistic jerk model, for short cs-jerk, is developed, in which the jerk maneuvering is assumed to be an exponential correlated stochastic process with non-zero mean. It consists of the cs-jerk model of target motion, and a tracking filter with compatible order. The stable performance of the cs-jerk model is also analyzed and the result indicates that the cs-jerk model eliminates performance limitation of the jerk model. The improved performance of the cs-jerk model over the jerk model is illustrated through simulation.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129231738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we introduce the basic concept of the rotational Fourier transform, the bilinear rotational time-frequency transforms, and the Radon transform. Based on a certain model of radar returns from moving targets, we propose a method using a rotational time-frequency-Radon transform for synthetic aperture radar imaging of moving targets in foliage.
{"title":"FOPEN SAR imaging of ground moving targets using rotational time-frequency-radon transforms","authors":"V. Chen, R. Lipps, M. Bottoms","doi":"10.1109/NRC.2002.999713","DOIUrl":"https://doi.org/10.1109/NRC.2002.999713","url":null,"abstract":"In this paper, we introduce the basic concept of the rotational Fourier transform, the bilinear rotational time-frequency transforms, and the Radon transform. Based on a certain model of radar returns from moving targets, we propose a method using a rotational time-frequency-Radon transform for synthetic aperture radar imaging of moving targets in foliage.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116664532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We derive the nonhomogeneity detector (NHD) for non-Gaussian interference scenarios and present a statistical analysis of the method. The non-Gaussian interference scenario is assumed to be modeled by a spherically invariant random process (SIRP). We present two methods for selecting representative (homogeneous) training data based on our statistical analysis of the NHD for finite sample support used in covariance estimation. In particular, exact theoretical expressions for the NHD test statistic probability density function (PDF) and its moments are derived. Additionally, we note that for SIRP interference, a simple transformation of the NHD test statistic admits an elegant representation as the ratio of a central-F distributed random variable and a beta distributed loss factor random variable. Performance analysis of the NHD is presented using both simulated data and measured data from the MCARM program.
{"title":"Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds","authors":"M. Rangaswamy, J. Michels, B. Himed","doi":"10.1109/NRC.2002.999736","DOIUrl":"https://doi.org/10.1109/NRC.2002.999736","url":null,"abstract":"We derive the nonhomogeneity detector (NHD) for non-Gaussian interference scenarios and present a statistical analysis of the method. The non-Gaussian interference scenario is assumed to be modeled by a spherically invariant random process (SIRP). We present two methods for selecting representative (homogeneous) training data based on our statistical analysis of the NHD for finite sample support used in covariance estimation. In particular, exact theoretical expressions for the NHD test statistic probability density function (PDF) and its moments are derived. Additionally, we note that for SIRP interference, a simple transformation of the NHD test statistic admits an elegant representation as the ratio of a central-F distributed random variable and a beta distributed loss factor random variable. Performance analysis of the NHD is presented using both simulated data and measured data from the MCARM program.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133124897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The research applies space-time adaptive processing (STAP) techniques to a pseudo-circular array generated by selectively thinning a rectangular array. A hybrid approach incorporating elevation interferometry and STAP techniques is used. Results show the thinned 16-element pseudo-circular array offers significant detection performance improvement over the baseline factored time-space (FTS) technique operating on a linear array, e.g., an 8-element horizontal linear array. Results are demonstrated for cases with and without range ambiguous clutter. This performance level is achieved using a factor of M less sample support than required for full adaptivity where M represents the number of pulses within a coherent processing interval.
{"title":"Elevation interferometric STAP using a thinned planar array","authors":"T. Hale, M. Temple, J. Raquet, M. Oxley, M. Wicks","doi":"10.1109/NRC.2002.999753","DOIUrl":"https://doi.org/10.1109/NRC.2002.999753","url":null,"abstract":"The research applies space-time adaptive processing (STAP) techniques to a pseudo-circular array generated by selectively thinning a rectangular array. A hybrid approach incorporating elevation interferometry and STAP techniques is used. Results show the thinned 16-element pseudo-circular array offers significant detection performance improvement over the baseline factored time-space (FTS) technique operating on a linear array, e.g., an 8-element horizontal linear array. Results are demonstrated for cases with and without range ambiguous clutter. This performance level is achieved using a factor of M less sample support than required for full adaptivity where M represents the number of pulses within a coherent processing interval.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116220694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We develop a beamforming technique called colored diagonal loading. This technique is a generalization of diagonal loading in which the covariance matrix is augmented with a scaled version of a colored matrix as opposed to using the identity matrix as with conventional diagonal loading. Thus as the loading is increased, the beampattern increasingly takes on the form of a desired quiescent pattern as opposed to that of a conventional (high sidelobe) pattern. The attractiveness of this technique is that it retains the robustness and simple formulation of diagonal loading while allowing insertion of additional quiescent structure. We compare this technique is to conventional diagonal loading and to other quiescent pattern techniques.
{"title":"Colored diagonal loading","authors":"J. Hiemstra","doi":"10.1109/NRC.2002.999749","DOIUrl":"https://doi.org/10.1109/NRC.2002.999749","url":null,"abstract":"We develop a beamforming technique called colored diagonal loading. This technique is a generalization of diagonal loading in which the covariance matrix is augmented with a scaled version of a colored matrix as opposed to using the identity matrix as with conventional diagonal loading. Thus as the loading is increased, the beampattern increasingly takes on the form of a desired quiescent pattern as opposed to that of a conventional (high sidelobe) pattern. The attractiveness of this technique is that it retains the robustness and simple formulation of diagonal loading while allowing insertion of additional quiescent structure. We compare this technique is to conventional diagonal loading and to other quiescent pattern techniques.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123316571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a MATLAB simulation that computes the probability of detection for a radar. It allows the user to input a wide range of radar characteristics. The target detection method has the capacity for handling fluctuating targets. This is important because actual targets are observed to scintillate and the probability of detection decreases as the signal-to-noise ratio decreases. This paper also considers characteristics of the operational mode of the radar system, such as its PRF (pulse repetition frequency), number of pulses processed, and the necessary range resolving ratio. The simulation utilizes formulas for detection probability associated with a target's range and uses the Swerling cases to model the! target's fluctuating cross section. The goal is to build a MATLAB simulation that can display radar observations in a format that facilitates analysis in range versus azimuth.
{"title":"MATLAB simulation for computing probability of detection","authors":"A. Lee, M. Mason","doi":"10.1109/NRC.2002.999764","DOIUrl":"https://doi.org/10.1109/NRC.2002.999764","url":null,"abstract":"This paper presents a MATLAB simulation that computes the probability of detection for a radar. It allows the user to input a wide range of radar characteristics. The target detection method has the capacity for handling fluctuating targets. This is important because actual targets are observed to scintillate and the probability of detection decreases as the signal-to-noise ratio decreases. This paper also considers characteristics of the operational mode of the radar system, such as its PRF (pulse repetition frequency), number of pulses processed, and the necessary range resolving ratio. The simulation utilizes formulas for detection probability associated with a target's range and uses the Swerling cases to model the! target's fluctuating cross section. The goal is to build a MATLAB simulation that can display radar observations in a format that facilitates analysis in range versus azimuth.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124661980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The optimality in Neyman-Pearson (NP) sense in distributed CFAR detection with multisensor is discussed. Most of the existing analysis of optimization of distributed CFAR detection in the NP sense is done under the limitation of binary local decision and no communication among local processors. We find that the real optimization in the NP sense can not be realized under this limitation. If local test statistics (LTS) are used and fused, the real optimal NP test could be implemented by likelihood ratio test (LRT).
{"title":"The optimality in Neyman-Pearson sense in the distributed CFAR detection with multisensor","authors":"Guan Jian, Meng Xiang-wei, Peng Ying-ning, He You","doi":"10.1109/NRC.2002.999695","DOIUrl":"https://doi.org/10.1109/NRC.2002.999695","url":null,"abstract":"The optimality in Neyman-Pearson (NP) sense in distributed CFAR detection with multisensor is discussed. Most of the existing analysis of optimization of distributed CFAR detection in the NP sense is done under the limitation of binary local decision and no communication among local processors. We find that the real optimization in the NP sense can not be realized under this limitation. If local test statistics (LTS) are used and fused, the real optimal NP test could be implemented by likelihood ratio test (LRT).","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125412909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a method for resolving a return multipath signal in the presence of very large noise. The method basically consists of choosing a function, whose correlation with the transmitted signal is either zero, or of an easily analyzed form. Hence, correlating such a function with the received signal will single out the noise component. An interesting feature of our approach is that large noise is not inherently disadvantageous. The paper describes the general method and then illustrate its application in some cases of general interest.
{"title":"Multipath signal recovery in the presence of very large noise","authors":"I. Gladkova","doi":"10.1109/NRC.2002.999686","DOIUrl":"https://doi.org/10.1109/NRC.2002.999686","url":null,"abstract":"In this paper, we present a method for resolving a return multipath signal in the presence of very large noise. The method basically consists of choosing a function, whose correlation with the transmitted signal is either zero, or of an easily analyzed form. Hence, correlating such a function with the received signal will single out the noise component. An interesting feature of our approach is that large noise is not inherently disadvantageous. The paper describes the general method and then illustrate its application in some cases of general interest.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128507222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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