Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316480
A. Uppuluri, R. Jost
The paper provides a review into the steps involved in acquiring and processing synthetic aperture radar (SAR) data transmitted by the European remote sensing (ERS) satellites. The paper reports on a simple MATLAB-based SAR processing system, that reads the image out of the complex SAR data files and that is suitable for use in the classroom to demonstrate one of the procedures used in SAR data processing. The paper can also help a beginner in the field of SAR signal processing to get information and understand the basics that are necessary to acquire and process a SAR image. The data, provided by the Alaskan Satellite Facility (ASF), is categorized into different levels and the paper describes the process of obtaining the level-1 basic image from the level-0 raw data file provided by ASF.
{"title":"MATLAB-based ERS SAR data acquisition and processing software for classroom use","authors":"A. Uppuluri, R. Jost","doi":"10.1109/NRC.2004.1316480","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316480","url":null,"abstract":"The paper provides a review into the steps involved in acquiring and processing synthetic aperture radar (SAR) data transmitted by the European remote sensing (ERS) satellites. The paper reports on a simple MATLAB-based SAR processing system, that reads the image out of the complex SAR data files and that is suitable for use in the classroom to demonstrate one of the procedures used in SAR data processing. The paper can also help a beginner in the field of SAR signal processing to get information and understand the basics that are necessary to acquire and process a SAR image. The data, provided by the Alaskan Satellite Facility (ASF), is categorized into different levels and the paper describes the process of obtaining the level-1 basic image from the level-0 raw data file provided by ASF.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127346434","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}
Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316441
R. Doviak
Doppler weather radars measure only the radial wind component of wind, and thus are limited in providing accurate information of damaging wind potential. The use of a phased array antenna opens the possibility that crossbeam winds can also be measured. This paper examines and compares two alternatives whereby a phased array weather radar can measure crossbeam winds. The theoretical accuracy of the quasi-horizontal component of the crossbeam wind for each of these alternatives is shown to be strongly dependent on turbulence intensity. Crossbeam winds can be measured with accuracies on the order of 2 ms/sup -1/ in less than 10 s if turbulence intensity is less than 1 ms/sup -1/.
{"title":"Crossbeam wind measurements with phased array Doppler weather radar: theory","authors":"R. Doviak","doi":"10.1109/NRC.2004.1316441","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316441","url":null,"abstract":"Doppler weather radars measure only the radial wind component of wind, and thus are limited in providing accurate information of damaging wind potential. The use of a phased array antenna opens the possibility that crossbeam winds can also be measured. This paper examines and compares two alternatives whereby a phased array weather radar can measure crossbeam winds. The theoretical accuracy of the quasi-horizontal component of the crossbeam wind for each of these alternatives is shown to be strongly dependent on turbulence intensity. Crossbeam winds can be measured with accuracies on the order of 2 ms/sup -1/ in less than 10 s if turbulence intensity is less than 1 ms/sup -1/.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114256334","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}
Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316439
W. Melvin, G. Showman, J. Guerci
Space-time adaptive processing (STAP) plays an important role in ground moving target indication (GMTI). Heterogeneous clutter environments prevent STAP from achieving its theoretical performance bounds. The incorporation of a priori knowledge into the signal processing architecture holds the potential to greatly enhance detection performance by mitigating heterogeneous clutter effects. In this paper we propose one possible knowledge-aided STAP approach comprised of the following elements: a knowledge-aided prediction/estimation filter, a discrete matched filter, and a partially adaptive STAP applied to the clutter residual, assisted by knowledge-aided training. We focus our discussion on justifying the aforementioned elements and independently characterizing their performance potential. Using both measured and simulated data, we find the potential for substantial performance improvement.
{"title":"A knowledge-aided GMTI detection architecture [radar signal processing]","authors":"W. Melvin, G. Showman, J. Guerci","doi":"10.1109/NRC.2004.1316439","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316439","url":null,"abstract":"Space-time adaptive processing (STAP) plays an important role in ground moving target indication (GMTI). Heterogeneous clutter environments prevent STAP from achieving its theoretical performance bounds. The incorporation of a priori knowledge into the signal processing architecture holds the potential to greatly enhance detection performance by mitigating heterogeneous clutter effects. In this paper we propose one possible knowledge-aided STAP approach comprised of the following elements: a knowledge-aided prediction/estimation filter, a discrete matched filter, and a partially adaptive STAP applied to the clutter residual, assisted by knowledge-aided training. We focus our discussion on justifying the aforementioned elements and independently characterizing their performance potential. Using both measured and simulated data, we find the potential for substantial performance improvement.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134441073","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}
Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316404
R. Lipps, V. Chen, M. Bottoms
Synthetic aperture radar (SAR) systems are designed to produce high quality imagery of a stationary target on the ground. These systems are not designed to handle moving targets and perform poorly in the areas of detecting and imaging moving targets. The paper presents advanced techniques developed to handle the detection and refocusing of moving targets for SAR systems.
{"title":"Advanced SAR GMTI techniques","authors":"R. Lipps, V. Chen, M. Bottoms","doi":"10.1109/NRC.2004.1316404","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316404","url":null,"abstract":"Synthetic aperture radar (SAR) systems are designed to produce high quality imagery of a stationary target on the ground. These systems are not designed to handle moving targets and perform poorly in the areas of detecting and imaging moving targets. The paper presents advanced techniques developed to handle the detection and refocusing of moving targets for SAR systems.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122234737","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}
Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316430
M. Picciolo, K. Gerlach
A robust, fast-converging, reduced-rank adaptive processor is introduced, based on diagonally loading the reiterative median cascaded canceller (RMCC). The new loaded reiterative median cascaded canceller (LRMCC) exhibits the highly desirable combination of: (1) convergence-robustness to outliers/targets/nonstationary data in adaptive weight training data, like the RMCC; (2) convergence performance that is approximately independent of the interference-plus-noise covariance matrix, like the RMCC; and (3) fast convergence at a rate commensurate with reduced-rank algorithms, unlike the RMCC. Measured airborne radar data from the MCARM space-time adaptive processing (STAP) database is used to show performance enhancements. It is concluded that the LRMCC is a practical and highly robust replacement for existing reduced-rank adaptive processors, exhibiting superior performance in nonideal measured data environments.
{"title":"A robust loaded reiterative median cascaded canceller","authors":"M. Picciolo, K. Gerlach","doi":"10.1109/NRC.2004.1316430","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316430","url":null,"abstract":"A robust, fast-converging, reduced-rank adaptive processor is introduced, based on diagonally loading the reiterative median cascaded canceller (RMCC). The new loaded reiterative median cascaded canceller (LRMCC) exhibits the highly desirable combination of: (1) convergence-robustness to outliers/targets/nonstationary data in adaptive weight training data, like the RMCC; (2) convergence performance that is approximately independent of the interference-plus-noise covariance matrix, like the RMCC; and (3) fast convergence at a rate commensurate with reduced-rank algorithms, unlike the RMCC. Measured airborne radar data from the MCARM space-time adaptive processing (STAP) database is used to show performance enhancements. It is concluded that the LRMCC is a practical and highly robust replacement for existing reduced-rank adaptive processors, exhibiting superior performance in nonideal measured data environments.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122300820","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}
Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316474
R. Xue, B. Yuan, Junfa Mao
The application of diffraction technology to ultra wideband synthetic aperture radars (UWB SAR) was investigated to find an optimal solution to high-quality radar imagery. The microwave imagery criterion is presented and the spatial frequency coverage is introduced to evaluate imaging systems at first. Then radar imagery is analyzed with the comprehensive consideration of the scattering mechanisms, the data acquisition system, and the image reconstruction algorithm. Theoretical and numerical results show UWB SAR exploiting diffraction technology has the potential to realize high-resolution geometric imaging and probe inherent physical properties of targets. This provides a theoretical basis for formation flight and optimization of SAR systems.
{"title":"Application of diffraction technology to UWB SAR research","authors":"R. Xue, B. Yuan, Junfa Mao","doi":"10.1109/NRC.2004.1316474","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316474","url":null,"abstract":"The application of diffraction technology to ultra wideband synthetic aperture radars (UWB SAR) was investigated to find an optimal solution to high-quality radar imagery. The microwave imagery criterion is presented and the spatial frequency coverage is introduced to evaluate imaging systems at first. Then radar imagery is analyzed with the comprehensive consideration of the scattering mechanisms, the data acquisition system, and the image reconstruction algorithm. Theoretical and numerical results show UWB SAR exploiting diffraction technology has the potential to realize high-resolution geometric imaging and probe inherent physical properties of targets. This provides a theoretical basis for formation flight and optimization of SAR systems.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127324773","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}
Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316500
H. Meng, Xiqin Wang, Hao Zhang, Yingning Peng
A method for evaluating the robustness of constant false alarm rate (CFAR) detectors is presented; it is based on the powerful methodology of influence function (IF) developed in the literature on robust statistics. The robustness of different kinds of CFAR detectors can be evaluated and compared by calculating the first derivative of the false alarm probability (FAP) and detection probability (DP) at an underlying distribution, which are named IF-FAP and IF-DP. The two IFs are compared among some kinds of CFAR detectors. It is concluded that the robustness of a detector can be asymptotically represented by the IF of the clutter power estimator. Finally, according to a robust measure drawn from the IF of the clutter power estimator, the "most robust" detectors in three ordered-statistic-based groups are presented.
{"title":"A method using influence function for evaluating robustness of CFAR detectors","authors":"H. Meng, Xiqin Wang, Hao Zhang, Yingning Peng","doi":"10.1109/NRC.2004.1316500","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316500","url":null,"abstract":"A method for evaluating the robustness of constant false alarm rate (CFAR) detectors is presented; it is based on the powerful methodology of influence function (IF) developed in the literature on robust statistics. The robustness of different kinds of CFAR detectors can be evaluated and compared by calculating the first derivative of the false alarm probability (FAP) and detection probability (DP) at an underlying distribution, which are named IF-FAP and IF-DP. The two IFs are compared among some kinds of CFAR detectors. It is concluded that the robustness of a detector can be asymptotically represented by the IF of the clutter power estimator. Finally, according to a robust measure drawn from the IF of the clutter power estimator, the \"most robust\" detectors in three ordered-statistic-based groups are presented.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116136303","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}
Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316407
A. E. Nordsjo
An extended Kalman filter, EKF, is proposed for tracking the position and velocity of a moving target. The suggested method is based on a nonlinear model which, in addition, incorporates means for estimating possible nonlinearities in the measurements of the target position. In many practical scenarios, the initial estimates of target position and velocity deviate significantly from the true ones. In order to reduce the impact of erroneous initial conditions and, hence, obtain a faster initial convergence to an acceptable trajectory, a certain constrained form of the EKF, named the CEKF, is introduced. Although the original Kalman filter for a purely linear system is inherently stable, there is no guarantee that the linearized model used in the EKF gives a stable algorithm. Hence, it is interesting to note that the proposed CEKF under certain mild conditions renders an exponentially stable algorithm. It is shown that this latter method can conveniently be formulated as a nonlinear minimization problem with a quadratic inequality constraint.
{"title":"A constrained extended Kalman filter for target tracking","authors":"A. E. Nordsjo","doi":"10.1109/NRC.2004.1316407","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316407","url":null,"abstract":"An extended Kalman filter, EKF, is proposed for tracking the position and velocity of a moving target. The suggested method is based on a nonlinear model which, in addition, incorporates means for estimating possible nonlinearities in the measurements of the target position. In many practical scenarios, the initial estimates of target position and velocity deviate significantly from the true ones. In order to reduce the impact of erroneous initial conditions and, hence, obtain a faster initial convergence to an acceptable trajectory, a certain constrained form of the EKF, named the CEKF, is introduced. Although the original Kalman filter for a purely linear system is inherently stable, there is no guarantee that the linearized model used in the EKF gives a stable algorithm. Hence, it is interesting to note that the proposed CEKF under certain mild conditions renders an exponentially stable algorithm. It is shown that this latter method can conveniently be formulated as a nonlinear minimization problem with a quadratic inequality constraint.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116594811","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}
Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316450
C. Capraro, G. Capraro, D. Weiner, M. Wicks, W. Baldygo
Secondary data selection for estimation of the clutter covariance matrix, needed in space-time adaptive processing (STAP), is normally obtained from range rings nearby the cell under test. The assumption is that these range rings contain cells that are representative of the clutter statistics in the test cell. However, in a nonhomogeneous terrain environment, this may not be true. An innovative approach is presented, in the area of knowledge-aided STAP, which utilizes terrain data from the United States Geological Survey (USGS) to aid in the selection of secondary data cells. Results have been obtained and compared with the sliding (cell averaging symmetric) window method of secondary data selection. This comparison indicates that making use of the surveillance terrain knowledge improves STAP performance.
{"title":"Improved STAP performance using knowledge-aided secondary data selection","authors":"C. Capraro, G. Capraro, D. Weiner, M. Wicks, W. Baldygo","doi":"10.1109/NRC.2004.1316450","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316450","url":null,"abstract":"Secondary data selection for estimation of the clutter covariance matrix, needed in space-time adaptive processing (STAP), is normally obtained from range rings nearby the cell under test. The assumption is that these range rings contain cells that are representative of the clutter statistics in the test cell. However, in a nonhomogeneous terrain environment, this may not be true. An innovative approach is presented, in the area of knowledge-aided STAP, which utilizes terrain data from the United States Geological Survey (USGS) to aid in the selection of secondary data cells. Results have been obtained and compared with the sliding (cell averaging symmetric) window method of secondary data selection. This comparison indicates that making use of the surveillance terrain knowledge improves STAP performance.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115194863","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}
Pub Date : 2004-04-26DOI: 10.1109/NRC.2004.1316432
J. K. Beard, K. Erickson, M. Monteleone, M. Wright, J. Russo
Costas arrays are permutation matrices that also provide a frequency indexing sequence that permits at most one coincident tone in cross-correlations of FSK waveforms. As such, they have obvious application as frequency indexing sequences in radar and communications when long codes with bounded autocorrelation are required or when Doppler is a significant portion of the transmitted bandwidth. All Costas arrays for orders less than 25 are known, with those for N=24 disclosed in the paper. Higher orders are found through number-theoretic generators and partial searches.
{"title":"Combinatoric collaboration on Costas arrays and radar applications","authors":"J. K. Beard, K. Erickson, M. Monteleone, M. Wright, J. Russo","doi":"10.1109/NRC.2004.1316432","DOIUrl":"https://doi.org/10.1109/NRC.2004.1316432","url":null,"abstract":"Costas arrays are permutation matrices that also provide a frequency indexing sequence that permits at most one coincident tone in cross-correlations of FSK waveforms. As such, they have obvious application as frequency indexing sequences in radar and communications when long codes with bounded autocorrelation are required or when Doppler is a significant portion of the transmitted bandwidth. All Costas arrays for orders less than 25 are known, with those for N=24 disclosed in the paper. Higher orders are found through number-theoretic generators and partial searches.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129587002","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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