We show that the performance of future high-resolution SAR modes will be limited by anomalous propagation effects, rather than by platform measurement errors, or focusing algorithm limitations, or RF wavelength. This is established by calculating the phase history distortions that result from specified atmospheric temperature profiles. Simulations show the effects of such phase distortions upon SAR images.
{"title":"Anomalous propagation limitations to high-resolution SAR performance","authors":"M. Denny, I. Scott","doi":"10.1109/NRC.2002.999727","DOIUrl":"https://doi.org/10.1109/NRC.2002.999727","url":null,"abstract":"We show that the performance of future high-resolution SAR modes will be limited by anomalous propagation effects, rather than by platform measurement errors, or focusing algorithm limitations, or RF wavelength. This is established by calculating the phase history distortions that result from specified atmospheric temperature profiles. Simulations show the effects of such phase distortions upon SAR images.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"14 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":"126747920","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, the frequency estimation of the sinusoidal signals with unknown lowpass envelope is addressed. Due to mismodeling, the performance of the conventional subspace-based method degrades significantly in these cases. By developing the method applied to the parametric localization of distributed sources, an eigenanalysis-based method is proposed for the frequency estimate. The comparisons of the proposed method and the nonlinear least-squares (NLS) approach with each other as well as the Cramer-Rao bound (CRB), are presented. The simulations illustrate the good performance in the precision and super-resolution.
{"title":"Frequency estimation of the sinusoidal signals with unknown lowpass envelopes based on the eigenanalysis","authors":"F. Ge, Q. Wan, Xiutan Wang, Yingning Peng","doi":"10.1109/NRC.2002.999760","DOIUrl":"https://doi.org/10.1109/NRC.2002.999760","url":null,"abstract":"In this paper, the frequency estimation of the sinusoidal signals with unknown lowpass envelope is addressed. Due to mismodeling, the performance of the conventional subspace-based method degrades significantly in these cases. By developing the method applied to the parametric localization of distributed sources, an eigenanalysis-based method is proposed for the frequency estimate. The comparisons of the proposed method and the nonlinear least-squares (NLS) approach with each other as well as the Cramer-Rao bound (CRB), are presented. The simulations illustrate the good performance in the precision and super-resolution.","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":"128790360","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}
A new technique is described that generalizes the usefulness of the discrete Fourier transform for spectral analysis of radar data to applications where the discrete data points to be analyzed are not sampled at regular intervals and/or do not have equal statistical weight. This method finds a frequency-domain representation which best represents the given time-domain data in the least-square-residual sense. The theoretical implications of such an approach are discussed, and an example of the technique applied to a Doppler processing task is given. The merits of the approach relative to other spectral analysis techniques are discussed.
{"title":"Technique for frequency analysis of unevenly sampled radar data","authors":"M. House, P. Mountcastle","doi":"10.1109/NRC.2002.999694","DOIUrl":"https://doi.org/10.1109/NRC.2002.999694","url":null,"abstract":"A new technique is described that generalizes the usefulness of the discrete Fourier transform for spectral analysis of radar data to applications where the discrete data points to be analyzed are not sampled at regular intervals and/or do not have equal statistical weight. This method finds a frequency-domain representation which best represents the given time-domain data in the least-square-residual sense. The theoretical implications of such an approach are discussed, and an example of the technique applied to a Doppler processing task is given. The merits of the approach relative to other spectral analysis techniques are discussed.","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":"130747148","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}
A multistage median cascaded canceller is introduced as a hybrid combination of the multistage Wiener filter (MWF) and the recently introduced median cascaded canceller (MCC). The hybrid processor is configured for radar space-time adaptive processing and tested using measured airborne radar data from the MCARM database as well as using simulated airborne radar clutter and jamming data. Results show the hybrid processor maintains a high signal-to-interference-plus-noise ratio and exhibits a mixture of useful characteristics from the two separate processors. Optimal rank reduction capability from the MWF portion is retained and robustness to targets/outliers and non-stationary data are attributed to the MCC portion. In addition, valuable synergistic features are discovered, such as an order 15 dB lower average azimuth-Doppler sidelobe level in the adapted pattern, compared to the MWF.
{"title":"An adaptive multistage median cascaded canceller","authors":"M. Picciolo, K. Gerlach, J. S. Goldstein","doi":"10.1109/NRC.2002.999738","DOIUrl":"https://doi.org/10.1109/NRC.2002.999738","url":null,"abstract":"A multistage median cascaded canceller is introduced as a hybrid combination of the multistage Wiener filter (MWF) and the recently introduced median cascaded canceller (MCC). The hybrid processor is configured for radar space-time adaptive processing and tested using measured airborne radar data from the MCARM database as well as using simulated airborne radar clutter and jamming data. Results show the hybrid processor maintains a high signal-to-interference-plus-noise ratio and exhibits a mixture of useful characteristics from the two separate processors. Optimal rank reduction capability from the MWF portion is retained and robustness to targets/outliers and non-stationary data are attributed to the MCC portion. In addition, valuable synergistic features are discovered, such as an order 15 dB lower average azimuth-Doppler sidelobe level in the adapted pattern, compared to the MWF.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"24 6 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":"132753816","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 new signal processing method to improve the identification of interface between different layered media, using a ground penetrating radar (GPR) recording. Our methodological approach is based on Monte Carlo Markov chain (MCMC) model. The deconvolution of the GPR signal is obtained in considering a stochastic estimation related to a maximum a posteriori criterion. The only known elements are the signal recorded from the GPR backscattering (one dimension approximation), and the order of the ARMA signal model for the emitted pulse.
{"title":"Interface identification using a GPR signal: a Monte Carlo Markov chain approach","authors":"A. Coatanhay, Jean-Jacques Szkolnik","doi":"10.1109/NRC.2002.999693","DOIUrl":"https://doi.org/10.1109/NRC.2002.999693","url":null,"abstract":"This paper presents a new signal processing method to improve the identification of interface between different layered media, using a ground penetrating radar (GPR) recording. Our methodological approach is based on Monte Carlo Markov chain (MCMC) model. The deconvolution of the GPR signal is obtained in considering a stochastic estimation related to a maximum a posteriori criterion. The only known elements are the signal recorded from the GPR backscattering (one dimension approximation), and the order of the ARMA signal model for the emitted pulse.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"96 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":"128378314","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}
Sea clutter refers to the backscattered returns from a patch of the sea surface illuminated by a transmitted radar pulse. Since the complicated sea clutter signals depend on the complex wave motions an the sea surface, it is reasonable to study sea clutter from nonlinear dynamics, especially chaos, point of view, instead of simply based on random processes. In the past decade, Dr. Simon Haykin's (1997) group at the McMaster University of Canada carried out analysis of some sea clutter data using chaos theory, based on the the assumption that a chaotic attractor is fully characterized by a non-integer fractal dimension and a positive Lyapunov exponent. Thus, they concluded that sea clutter signals are chaotic. In other words, the complicated sea clutter waveforms are generated by nonlinear deterministic interactions of a few modes (i.e., number of degrees of freedom). However, a numerically estimated non-integral fractal dimension and a positive Lyapunov exponent may not be sufficient indication of chaos. Cowper and Mulgrew (see Proc. UCNN, vol.4, p.2633, July 1999), Noga (see Ph.D thesis, Cambridge University, 1998), and Davies (1994) separately have questioned the chaoticness of the radar sea clutter. We show, using the direct dynamical test for deterministic chaos developed by Gao and Zheng, which is one of the more stringent criteria for low-dimensional chaos, a two minute duration sea clutter data is not chaotic. We also carry out a multifractal analysis of this sea clutter data set, and find that the original sea clutter amplitude signal is approximately multifractal, while the envelope signal, formed by picking up the successive local maxima of the amplitude signal, thus measuring the energy of successive waves on the sea surface, is well modeled as multifractals. These behaviors determine that the amplitude signal follows approximately log-normal distribution while the envelope signal, and thus the energy of the successive waves of the sea surface, is log-normally distributed. Approximate log-normal distributions for the amplitude signal has been observed earlier. However, by using the multiplicative multifractal theory, there is theoretical justification for the log-normal distribution of sea clutter, as discussed. The implications of the multifractal nature of sea clutter may have relevance for the detection of point targets on the sea surface.
{"title":"Multifractal features of sea clutter","authors":"Jianbo Gao, K. Yao","doi":"10.1109/NRC.2002.999768","DOIUrl":"https://doi.org/10.1109/NRC.2002.999768","url":null,"abstract":"Sea clutter refers to the backscattered returns from a patch of the sea surface illuminated by a transmitted radar pulse. Since the complicated sea clutter signals depend on the complex wave motions an the sea surface, it is reasonable to study sea clutter from nonlinear dynamics, especially chaos, point of view, instead of simply based on random processes. In the past decade, Dr. Simon Haykin's (1997) group at the McMaster University of Canada carried out analysis of some sea clutter data using chaos theory, based on the the assumption that a chaotic attractor is fully characterized by a non-integer fractal dimension and a positive Lyapunov exponent. Thus, they concluded that sea clutter signals are chaotic. In other words, the complicated sea clutter waveforms are generated by nonlinear deterministic interactions of a few modes (i.e., number of degrees of freedom). However, a numerically estimated non-integral fractal dimension and a positive Lyapunov exponent may not be sufficient indication of chaos. Cowper and Mulgrew (see Proc. UCNN, vol.4, p.2633, July 1999), Noga (see Ph.D thesis, Cambridge University, 1998), and Davies (1994) separately have questioned the chaoticness of the radar sea clutter. We show, using the direct dynamical test for deterministic chaos developed by Gao and Zheng, which is one of the more stringent criteria for low-dimensional chaos, a two minute duration sea clutter data is not chaotic. We also carry out a multifractal analysis of this sea clutter data set, and find that the original sea clutter amplitude signal is approximately multifractal, while the envelope signal, formed by picking up the successive local maxima of the amplitude signal, thus measuring the energy of successive waves on the sea surface, is well modeled as multifractals. These behaviors determine that the amplitude signal follows approximately log-normal distribution while the envelope signal, and thus the energy of the successive waves of the sea surface, is log-normally distributed. Approximate log-normal distributions for the amplitude signal has been observed earlier. However, by using the multiplicative multifractal theory, there is theoretical justification for the log-normal distribution of sea clutter, as discussed. The implications of the multifractal nature of sea clutter may have relevance for the detection of point targets on the sea surface.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"75 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":"131644213","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 bistatic STAP performance. We show that typical bistatic clutter environments appear nonstationary. Nonstationary behavior exacerbates STAP implementation. In the absence of corrective measures, SINR losses due to covariance estimation error approaches 30 dB for the numerical examples considered herein. Using localized STAP processing coupled with a time-varying weight procedure, we show that much of the performance loss can be restored.
{"title":"Bistatic STAP: application to airborne radar","authors":"W. Melvin, M. J. Callahan, M. Wicks","doi":"10.1109/NRC.2002.999683","DOIUrl":"https://doi.org/10.1109/NRC.2002.999683","url":null,"abstract":"We investigate bistatic STAP performance. We show that typical bistatic clutter environments appear nonstationary. Nonstationary behavior exacerbates STAP implementation. In the absence of corrective measures, SINR losses due to covariance estimation error approaches 30 dB for the numerical examples considered herein. Using localized STAP processing coupled with a time-varying weight procedure, we show that much of the performance loss can be restored.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"133 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":"121365442","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 the performance of several multichannel adaptive processing detection methods, including a model-based approach which exhibits robustness in correlated disturbances ranging from Gaussian to K-distributed with high tailed probability density functions modeled as compound-Gaussian clutter. Specifically, we consider detection in dense signal environments where training data contains multiple discrete signals in the spatial-temporal domain. For this problem, we compare methods featuring robustness to such processes with the recently proposed non-homogeneity detection (NHD) method, a preprocessing approach for training data selection prior to detection algorithm implementation. Issues considered here include robust detection with respect to clutter texture power variations and multiple signal environments, constant false alarm rate (CFAR) performance and efficient estimation with limited training data.
{"title":"Robust multichannel detection in heterogeneous airborne radar disturbance","authors":"J. Michels, M. Rangaswamy, B. Himed","doi":"10.1109/NRC.2002.999733","DOIUrl":"https://doi.org/10.1109/NRC.2002.999733","url":null,"abstract":"This paper presents the performance of several multichannel adaptive processing detection methods, including a model-based approach which exhibits robustness in correlated disturbances ranging from Gaussian to K-distributed with high tailed probability density functions modeled as compound-Gaussian clutter. Specifically, we consider detection in dense signal environments where training data contains multiple discrete signals in the spatial-temporal domain. For this problem, we compare methods featuring robustness to such processes with the recently proposed non-homogeneity detection (NHD) method, a preprocessing approach for training data selection prior to detection algorithm implementation. Issues considered here include robust detection with respect to clutter texture power variations and multiple signal environments, constant false alarm rate (CFAR) performance and efficient estimation with limited training data.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"42 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":"128414205","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}
An efficient importance sampling simulation method is presented for estimating the thresholds to achieve very low probabilities of false alarm for radar receivers in clutter modeled as a non-Gaussian, spherically invariant random vector. Thresholds at false alarm probabilities of 10/sup -6/ and lower are estimated with only 10,000 trials for both known and unknown clutter covariance matrix cases.
{"title":"Efficient determination of thresholds via importance sampling for Monte Carlo evaluation of radar performance in non-Gaussian clutter","authors":"D. L. Stadelman, D. Weiner, A. D. Keckler","doi":"10.1109/NRC.2002.999731","DOIUrl":"https://doi.org/10.1109/NRC.2002.999731","url":null,"abstract":"An efficient importance sampling simulation method is presented for estimating the thresholds to achieve very low probabilities of false alarm for radar receivers in clutter modeled as a non-Gaussian, spherically invariant random vector. Thresholds at false alarm probabilities of 10/sup -6/ and lower are estimated with only 10,000 trials for both known and unknown clutter covariance matrix cases.","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":"115703554","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}
With advances in Doppler weather radar, severe storm and tornado detection has improved greatly. However, the resolution limitations of deployed radar systems can still limit severe storm detection. In the case of larger tornadoes, characteristic abrupt changes in wind direction can usually be detected between adjacent range-angle bins. However for smaller tornadoes, the rotating cell may be contained within one bin. In this case, a wind directional change cannot be detected, and the tornado may go undetected. The purpose of this research is to develop analytical and computer simulation models of typical Doppler weather radar measurements. These models can be used to determine how various factors affect the reflectivity, velocity, and spectrum width measurements that are commonly used in storm detection algorithms. The models account for convolution in azimuth due to the radar antenna pattern, convolution in range due to the radar pulse shape, randomness of the weather events and measurements, variations in the measurements between radar pulses, and the addition of noise to the measurements. Using both analytical and simulation models allows for simulated data to be generated, as well as equations that predict the behavior of the data. Therefore, the analytical and simulation models can be used to test the other's accuracy. Additionally, the analytical model can be used to create future algorithms (e.g. resolution enhancement), and the simulated data can be used as a test for these algorithms.
{"title":"Analytical and computer model of a Doppler weather radar system","authors":"R. Hersey, M. A. Richards, J. H. McClellan","doi":"10.1109/NRC.2002.999758","DOIUrl":"https://doi.org/10.1109/NRC.2002.999758","url":null,"abstract":"With advances in Doppler weather radar, severe storm and tornado detection has improved greatly. However, the resolution limitations of deployed radar systems can still limit severe storm detection. In the case of larger tornadoes, characteristic abrupt changes in wind direction can usually be detected between adjacent range-angle bins. However for smaller tornadoes, the rotating cell may be contained within one bin. In this case, a wind directional change cannot be detected, and the tornado may go undetected. The purpose of this research is to develop analytical and computer simulation models of typical Doppler weather radar measurements. These models can be used to determine how various factors affect the reflectivity, velocity, and spectrum width measurements that are commonly used in storm detection algorithms. The models account for convolution in azimuth due to the radar antenna pattern, convolution in range due to the radar pulse shape, randomness of the weather events and measurements, variations in the measurements between radar pulses, and the addition of noise to the measurements. Using both analytical and simulation models allows for simulated data to be generated, as well as equations that predict the behavior of the data. Therefore, the analytical and simulation models can be used to test the other's accuracy. Additionally, the analytical model can be used to create future algorithms (e.g. resolution enhancement), and the simulated data can be used as a test for these algorithms.","PeriodicalId":448055,"journal":{"name":"Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322)","volume":"19 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":"124162788","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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