Pub Date : 2014-05-19DOI: 10.1109/RADAR.2014.6875632
Bing Wang, G. Cui, L. Kong, Wei Yi
In this paper, we adopt the α-μ distribution to approximate the statistic distribution of the sum of independent and possibly non-identically distributed lognormal variables, and obtain the shape and scale parameters using both the moment matching method and Non-linear Least Square Method. Finally, we evaluate the performance via numerical simulations, the results illustrate that the α-μ approximation fits well the sum of the lognormal variables.
{"title":"Simple α-μ approximation to lognormal sums","authors":"Bing Wang, G. Cui, L. Kong, Wei Yi","doi":"10.1109/RADAR.2014.6875632","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875632","url":null,"abstract":"In this paper, we adopt the α-μ distribution to approximate the statistic distribution of the sum of independent and possibly non-identically distributed lognormal variables, and obtain the shape and scale parameters using both the moment matching method and Non-linear Least Square Method. Finally, we evaluate the performance via numerical simulations, the results illustrate that the α-μ approximation fits well the sum of the lognormal variables.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122993898","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 : 2014-05-19DOI: 10.1109/RADAR.2014.6875780
S. Sirianunpiboon, D. Cochran, S. Howard
Motivated primarily by electronic surveillance applications, but also by other potential uses in passive exploitation of radio frequency (RF) signals, this paper considers the problems of detecting the presence of and characterizing a radar transmitter using data collected at a spatially distributed suite of receivers. A characterization of a particular interest is determining the rank of the transmitted signal, which enables discrimination between multiple-input multiple-output (MIMO) and conventional radar transmitters as well as distinguishing between MIMO systems that simultaneously emit different numbers of linearly independent signals from their transmit arrays. In this paper, an invariant posterior distribution for position and signal rank of a MIMO radar emitter is derived based on non-informative prior distributions for the signal parameters. This allows MAP-based detection and signal rank estimation. These estimators are shown to significantly outperform maximum likelihood (ML)/BIC position and rank estimators.
{"title":"Invariant detection and estimation for MIMO radar signals","authors":"S. Sirianunpiboon, D. Cochran, S. Howard","doi":"10.1109/RADAR.2014.6875780","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875780","url":null,"abstract":"Motivated primarily by electronic surveillance applications, but also by other potential uses in passive exploitation of radio frequency (RF) signals, this paper considers the problems of detecting the presence of and characterizing a radar transmitter using data collected at a spatially distributed suite of receivers. A characterization of a particular interest is determining the rank of the transmitted signal, which enables discrimination between multiple-input multiple-output (MIMO) and conventional radar transmitters as well as distinguishing between MIMO systems that simultaneously emit different numbers of linearly independent signals from their transmit arrays. In this paper, an invariant posterior distribution for position and signal rank of a MIMO radar emitter is derived based on non-informative prior distributions for the signal parameters. This allows MAP-based detection and signal rank estimation. These estimators are shown to significantly outperform maximum likelihood (ML)/BIC position and rank estimators.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126261501","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 : 2014-05-19DOI: 10.1109/RADAR.2014.6875674
Shunjun Wei, Xiao-Ling Zhang, Jun Shi
Linear array SAR (LASAR) is a promising 3-D radar imaging technology. As 3-D radar images usually exhibit strong sparsity, compressed sensing sparse recovery algorithms can be used for LASAR imaging even if the echoes are under-sampled. However, most of the existing sparse recovery algorithms assume exact knowledge of the signal acquisition model, which is impractical for LASAR due to the phase errors are inevitable caused by uncertainties. In this paper, a novel sparse autofocus algorithm is proposed for LASAR imaging via Bayesian learning iterative maximum. In the scheme, the sparse scatterering coefficients are treated as exponential distribution and the phase errors are assumed as uniform distribution. Exploiting the Bayesian learning and maximum likelihood estimation, the approach solves a joint optimization problem to achieve phase errors estimation and image formation simultaneously. Simulation and experimental results are presented to confirm the effectiveness of the algorithm.
{"title":"Sparse autofocus via Bayesian learning iterative maximum and applied for LASAR 3-D imaging","authors":"Shunjun Wei, Xiao-Ling Zhang, Jun Shi","doi":"10.1109/RADAR.2014.6875674","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875674","url":null,"abstract":"Linear array SAR (LASAR) is a promising 3-D radar imaging technology. As 3-D radar images usually exhibit strong sparsity, compressed sensing sparse recovery algorithms can be used for LASAR imaging even if the echoes are under-sampled. However, most of the existing sparse recovery algorithms assume exact knowledge of the signal acquisition model, which is impractical for LASAR due to the phase errors are inevitable caused by uncertainties. In this paper, a novel sparse autofocus algorithm is proposed for LASAR imaging via Bayesian learning iterative maximum. In the scheme, the sparse scatterering coefficients are treated as exponential distribution and the phase errors are assumed as uniform distribution. Exploiting the Bayesian learning and maximum likelihood estimation, the approach solves a joint optimization problem to achieve phase errors estimation and image formation simultaneously. Simulation and experimental results are presented to confirm the effectiveness of the algorithm.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126589763","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 : 2014-05-19DOI: 10.1109/RADAR.2014.6875645
Weiming Tian, Tian Zhang, T. Zeng, Cheng Hu, T. Long
This paper presents a novel synchronization method for space-surface BiSAR (SS-BiSAR) illuminated by navigation satellites. Direct signal is utilized to obtain theoretical Doppler and navigation data. According to the navigation data and receiver position, theoretical Doppler history can be calculated. Comparing the tracking result and theoretical result, phase synchronization error could be estimated. After phase synchronization error is estimated and compensated, echo of SS-BiSAR is focused by bistatic back-projection algorithm. The proposed method has been verified by SS-BiSAR imaging experiment based on BeiDou signal.
{"title":"Space-surface BiSAR based on GNSS signal: Synchronization, imaging and experiment result","authors":"Weiming Tian, Tian Zhang, T. Zeng, Cheng Hu, T. Long","doi":"10.1109/RADAR.2014.6875645","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875645","url":null,"abstract":"This paper presents a novel synchronization method for space-surface BiSAR (SS-BiSAR) illuminated by navigation satellites. Direct signal is utilized to obtain theoretical Doppler and navigation data. According to the navigation data and receiver position, theoretical Doppler history can be calculated. Comparing the tracking result and theoretical result, phase synchronization error could be estimated. After phase synchronization error is estimated and compensated, echo of SS-BiSAR is focused by bistatic back-projection algorithm. The proposed method has been verified by SS-BiSAR imaging experiment based on BeiDou signal.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122253635","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 : 2014-05-19DOI: 10.1109/RADAR.2014.6875658
S. Bidon, O. Besson, J. Tourneret, F. Le Chevalier
In recent work we showed the interest of using sparse representation techniques to estimate a target scene observed by wideband radar systems. However the principle was demonstrated in a white noise background only. In this paper, we present an extended version of our sparse estimation technique that attempts to take into account the (possible) presence of diffuse clutter. More specifically, an autoregressive model is considered for the noise vector. Performance of the technique is studied on synthetic and experimental data. Pertinence of the noise model is discussed.
{"title":"Bayesian sparse estimation of migrating targets in autoregressive noise for wideband radar","authors":"S. Bidon, O. Besson, J. Tourneret, F. Le Chevalier","doi":"10.1109/RADAR.2014.6875658","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875658","url":null,"abstract":"In recent work we showed the interest of using sparse representation techniques to estimate a target scene observed by wideband radar systems. However the principle was demonstrated in a white noise background only. In this paper, we present an extended version of our sparse estimation technique that attempts to take into account the (possible) presence of diffuse clutter. More specifically, an autoregressive model is considered for the noise vector. Performance of the technique is studied on synthetic and experimental data. Pertinence of the noise model is discussed.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126079030","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 : 2014-05-19DOI: 10.1109/RADAR.2014.6875823
T. Guo, R. Qiu
Motivated by dual use of OFDM signal format for communications and radar in ever-worsening Electromagnetic (EM) coexistence environments, this paper deals with transmit waveform design problem considering multiple design objectives. Spectral nulling is a typical way for friendly coexistence with narrow band systems. However, a Non-Contiguous Orthogonal Frequency-Division Multiplexing (NC-OFDM) waveform generated by turning off the interfering sub-carriers does not lead to satisfactory results. In this paper a convex optimization based waveform design framework is used to achieve deep spectral nulling while retaining low waveform autocorrelation side lobes and good range resolution. Because of dual use of the waveform, the data blocks to transmit are either unknown or chosen from a known dataset. Optimal sub-carrier weights are obtained for given transmission data blocks. In addition, waveform design for unknown data blocks are discussed and examined.
{"title":"OFDM waveform design compromising spectral nulling, side-lobe suppression and range resolution","authors":"T. Guo, R. Qiu","doi":"10.1109/RADAR.2014.6875823","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875823","url":null,"abstract":"Motivated by dual use of OFDM signal format for communications and radar in ever-worsening Electromagnetic (EM) coexistence environments, this paper deals with transmit waveform design problem considering multiple design objectives. Spectral nulling is a typical way for friendly coexistence with narrow band systems. However, a Non-Contiguous Orthogonal Frequency-Division Multiplexing (NC-OFDM) waveform generated by turning off the interfering sub-carriers does not lead to satisfactory results. In this paper a convex optimization based waveform design framework is used to achieve deep spectral nulling while retaining low waveform autocorrelation side lobes and good range resolution. Because of dual use of the waveform, the data blocks to transmit are either unknown or chosen from a known dataset. Optimal sub-carrier weights are obtained for given transmission data blocks. In addition, waveform design for unknown data blocks are discussed and examined.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122542057","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 : 2014-05-19DOI: 10.1109/RADAR.2014.6875744
L. Li, G. Cui, Wei Yi, L. Kong, Xiaobo Yang
This paper addresses the problem of speckle covariance matrix estimation for Compound-Gaussian clutter. The speckle component is modeled as a low order autoregressive (AR) process. We derive the AR coefficients conditioned Likelihood function of the secondary data and propose an iterative approach for the optimizing problem under the criteria of Maximum-Likelihood (ML). We evaluate the performance of the new method by the normalized Frobenius norm of the error matrix and the normalized SINR through numerical simulations. The simulation results show that the new method outperforms existing methods in both accuracy and robustness.
{"title":"Maximum-Likelihood estimation for covariance matrix in Compound-Gaussian clutter via autoregressive modeling","authors":"L. Li, G. Cui, Wei Yi, L. Kong, Xiaobo Yang","doi":"10.1109/RADAR.2014.6875744","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875744","url":null,"abstract":"This paper addresses the problem of speckle covariance matrix estimation for Compound-Gaussian clutter. The speckle component is modeled as a low order autoregressive (AR) process. We derive the AR coefficients conditioned Likelihood function of the secondary data and propose an iterative approach for the optimizing problem under the criteria of Maximum-Likelihood (ML). We evaluate the performance of the new method by the normalized Frobenius norm of the error matrix and the normalized SINR through numerical simulations. The simulation results show that the new method outperforms existing methods in both accuracy and robustness.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122628470","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 : 2014-05-19DOI: 10.1109/RADAR.2014.6875550
T. Carroll, F. Rachford
A signal reflected from a radar target is modified according to the impulse response function of the target. This interaction may be thought of as a linear filter acting on the incident signal. Filters are not exactly invertible, which means that there is no continuous function between the filter output and its input. Likewise, there is no invertible function between the outputs of 2 different filters driven by the same signal, so there is no function between the responses of 2 different targets illuminated by the same incident signal. We apply a statistic from nonlinear dynamics that describes the probability that there is a function between 2 signals embedded in state space to develop a similarity statistic between 2 signals reflected from different targets. The similarity statistic describes how similar the targets are to each other. We have tested this similarity statistic with numerical simulations and acoustic experiments.
{"title":"Target identification based on state space analysis","authors":"T. Carroll, F. Rachford","doi":"10.1109/RADAR.2014.6875550","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875550","url":null,"abstract":"A signal reflected from a radar target is modified according to the impulse response function of the target. This interaction may be thought of as a linear filter acting on the incident signal. Filters are not exactly invertible, which means that there is no continuous function between the filter output and its input. Likewise, there is no invertible function between the outputs of 2 different filters driven by the same signal, so there is no function between the responses of 2 different targets illuminated by the same incident signal. We apply a statistic from nonlinear dynamics that describes the probability that there is a function between 2 signals embedded in state space to develop a similarity statistic between 2 signals reflected from different targets. The similarity statistic describes how similar the targets are to each other. We have tested this similarity statistic with numerical simulations and acoustic experiments.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127609856","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 : 2014-05-19DOI: 10.1109/RADAR.2014.6875534
M. Davis
Summary form only given. Ultra Wide Band Surveillance Radar is an emerging technology for detecting and characterizing targets and cultural features for military and geosciences applications. To characterize objects near and under severe clutter, it is necessary to have fine range and cross range resolution. The resultant wide bandwidth classifies the systems as ultra wideband, requiring special treatment in frequency allocation. This Tutorial is divided into four parts. The early history of Battlefield Surveillance Radar, UWB Frequency Allocation Process, UWB Synthetic Aperture Radar (SAR), and new research in Multi-mode Ultra-Wideband Radar.
{"title":"T08 — Ultra Wide Band surveillance radar","authors":"M. Davis","doi":"10.1109/RADAR.2014.6875534","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875534","url":null,"abstract":"Summary form only given. Ultra Wide Band Surveillance Radar is an emerging technology for detecting and characterizing targets and cultural features for military and geosciences applications. To characterize objects near and under severe clutter, it is necessary to have fine range and cross range resolution. The resultant wide bandwidth classifies the systems as ultra wideband, requiring special treatment in frequency allocation. This Tutorial is divided into four parts. The early history of Battlefield Surveillance Radar, UWB Frequency Allocation Process, UWB Synthetic Aperture Radar (SAR), and new research in Multi-mode Ultra-Wideband Radar.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127750925","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 : 2014-05-19DOI: 10.1109/RADAR.2014.6875742
L. Li, Haiguang Yang, G. Cui, L. Kong, Xiaobo Yang
This paper considers a radar system capable of adaptively adjusting its transmitted waveform, by which the system is able to dynamically mitigate the interference of the clutter, thus improve the detection performance. The key feature of the adaptive mechanism is the optimum waveform design, which is a complex multi-dimension optimizing problem and such a problem in this particular application has not yet been fully studied. Based on the structure of the general likelihood ration test (GLRT) detector and the compound-Gaussian (CG) clutter model, we derive the design objective function for the optimal phase modulated (PM) waveform. Then we simplify the objective function and propose an efficient iterative approach to solve this problem based on the pattern search algorithm. Numerical simulations confirm that the proposed algorithm is efficient to produce optimized waveforms for clutter mitigation in various conditions.
{"title":"Phase-modulated waveform design for target detection in clutter","authors":"L. Li, Haiguang Yang, G. Cui, L. Kong, Xiaobo Yang","doi":"10.1109/RADAR.2014.6875742","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875742","url":null,"abstract":"This paper considers a radar system capable of adaptively adjusting its transmitted waveform, by which the system is able to dynamically mitigate the interference of the clutter, thus improve the detection performance. The key feature of the adaptive mechanism is the optimum waveform design, which is a complex multi-dimension optimizing problem and such a problem in this particular application has not yet been fully studied. Based on the structure of the general likelihood ration test (GLRT) detector and the compound-Gaussian (CG) clutter model, we derive the design objective function for the optimal phase modulated (PM) waveform. Then we simplify the objective function and propose an efficient iterative approach to solve this problem based on the pattern search algorithm. Numerical simulations confirm that the proposed algorithm is efficient to produce optimized waveforms for clutter mitigation in various conditions.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131839663","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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