Pub Date : 2014-05-19DOI: 10.1109/RADAR.2014.7133687
H. Hou, X. Mao, Hong Hong, A. Liu
The high resolution multiple signal classification (MUSIC) algorithm provides an efficient way to estimate direction- of-arrival (DOA). However, it performs poorly when weak signals are accompanied with strong ones. To solve this problem, an oblique projection filtering based DOA estimation algorithm is proposed without using a priori knowledge of the sources, such as directions, strength, modulation modes, etc. Numerical results verify the effectiveness of the proposed algorithm. It is shown that a high resolution DOA estimation of the incident sources can be achieved. The detection performances for weak signals are more stable and superior than that of the MUSIC algorithm.
{"title":"An oblique projection filtering based DOA estimation algorithm without a priori knowledge","authors":"H. Hou, X. Mao, Hong Hong, A. Liu","doi":"10.1109/RADAR.2014.7133687","DOIUrl":"https://doi.org/10.1109/RADAR.2014.7133687","url":null,"abstract":"The high resolution multiple signal classification (MUSIC) algorithm provides an efficient way to estimate direction- of-arrival (DOA). However, it performs poorly when weak signals are accompanied with strong ones. To solve this problem, an oblique projection filtering based DOA estimation algorithm is proposed without using a priori knowledge of the sources, such as directions, strength, modulation modes, etc. Numerical results verify the effectiveness of the proposed algorithm. It is shown that a high resolution DOA estimation of the incident sources can be achieved. The detection performances for weak signals are more stable and superior than that of the MUSIC 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":"134555124","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.2014.6875739
Yigong Xiao, G. Cui, L. Kong, Wei Yi, Jianyu Yang
The meteor trail interference worsens the performance of the sky wave OTHR. The interference greatly reduce target detection capability. An approach based on Gabor coefficient and binary hypothesis to restrain the meteor trail interference is developed in this paper. The approach is used to process the real measurement data of a Chinese sky wave radar and show effectiveness.
{"title":"A new approach of meteor trail interference excision in sky wave OTHR based on Gabor coefficient","authors":"Yigong Xiao, G. Cui, L. Kong, Wei Yi, Jianyu Yang","doi":"10.1109/RADAR.2014.6875739","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875739","url":null,"abstract":"The meteor trail interference worsens the performance of the sky wave OTHR. The interference greatly reduce target detection capability. An approach based on Gabor coefficient and binary hypothesis to restrain the meteor trail interference is developed in this paper. The approach is used to process the real measurement data of a Chinese sky wave radar and show effectiveness.","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":"133018181","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.2014.6875762
Pilei Yin, Xiaopeng Yang, Q. Liu, T. Long
The distributed coherent aperture radar (DCAR) consisting of several cooperating small aperture radars and a control center is proposed to replace the traditional large aperture radar, which is more easily to transport and less expensive to build. In this paper, the constitution and workflow of DCAR are described at first. In the following, the estimation methods of coherent parameters are introduced. And then, a robust full coherent technology based on step frequency signal is proposed to reduce the effect of time synchronization error. At last, the simulations are carried out to verify the proposed methods.
{"title":"Wideband distributed coherent aperture radar","authors":"Pilei Yin, Xiaopeng Yang, Q. Liu, T. Long","doi":"10.1109/RADAR.2014.6875762","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875762","url":null,"abstract":"The distributed coherent aperture radar (DCAR) consisting of several cooperating small aperture radars and a control center is proposed to replace the traditional large aperture radar, which is more easily to transport and less expensive to build. In this paper, the constitution and workflow of DCAR are described at first. In the following, the estimation methods of coherent parameters are introduced. And then, a robust full coherent technology based on step frequency signal is proposed to reduce the effect of time synchronization error. At last, the simulations are carried out to verify the proposed methods.","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":"132132151","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.2014.6875688
J. Metcalf, S. Blunt, B. Himed
We consider a set of non-linear transformations of order statistics incorporated into a machine learning approach to perform distribution identification from data with low sample support with the ultimate goal of determining the appropriate detection threshold. The set of transformations provide a means with which data may be compared to a library of known clutter distributions. Several common non-Gaussian distributions are discussed and incorporated into the initial implementation of the library. This approach allows for the addition of empirically measured clutter distributions, which may not have a known analytic form. The adaptive threshold estimation reduces the probability of false alarm when non-Gaussian clutter is present.
{"title":"A machine learning approach to distribution identification in non-Gaussian clutter","authors":"J. Metcalf, S. Blunt, B. Himed","doi":"10.1109/RADAR.2014.6875688","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875688","url":null,"abstract":"We consider a set of non-linear transformations of order statistics incorporated into a machine learning approach to perform distribution identification from data with low sample support with the ultimate goal of determining the appropriate detection threshold. The set of transformations provide a means with which data may be compared to a library of known clutter distributions. Several common non-Gaussian distributions are discussed and incorporated into the initial implementation of the library. This approach allows for the addition of empirically measured clutter distributions, which may not have a known analytic form. The adaptive threshold estimation reduces the probability of false alarm when non-Gaussian clutter is present.","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":"122853129","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.2014.6875680
J. Halman, A. N. O'Donnell, R. Burkholder
This paper explores the use of physical basis functions as an efficient and insightful sparse expansion for representing the electromagnetic scattering from large finite targets. Such an expansion is central to applying compressed sensing techniques. The closed-form physical optics solution for scattering from an arbitrary flat plate is used to extract the physical basis functions related to scattering mechanisms of edge and corner diffraction, and specular reflection. Orthogonal matching pursuits is applied to find the coefficients of the sparse expansion from the calculated scattered fields of a plate as a function of frequency and angle. Convergence is demonstrated as a function of the number of basis functions and compressed sensing samples.
{"title":"On the use of physical basis functions in a sparse expansion for electromagnetic scattering signatures","authors":"J. Halman, A. N. O'Donnell, R. Burkholder","doi":"10.1109/RADAR.2014.6875680","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875680","url":null,"abstract":"This paper explores the use of physical basis functions as an efficient and insightful sparse expansion for representing the electromagnetic scattering from large finite targets. Such an expansion is central to applying compressed sensing techniques. The closed-form physical optics solution for scattering from an arbitrary flat plate is used to extract the physical basis functions related to scattering mechanisms of edge and corner diffraction, and specular reflection. Orthogonal matching pursuits is applied to find the coefficients of the sparse expansion from the calculated scattered fields of a plate as a function of frequency and angle. Convergence is demonstrated as a function of the number of basis functions and compressed sensing samples.","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":"129070721","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.2014.6875831
John Jakabosky, S. Blunt, B. Himed
Polyphase-Coded FM (PCFM) radar waveforms generated using the power and spectrally efficient continuous phase modulation (CPM) framework can be further enhanced through the use of finer time control by subdividing each phase transition into sub-transitions and by allowing a greater phase excursion per transition interval, herein referred to as over-phasing. These two strategies are denoted collectively as “over-coding”. It is shown that various combinations of sub-transitions and over-phasing can greatly improve waveform design capabilities by expanding the available degrees-of-freedom. It is also demonstrated that the commensurate increase in computational complexity for optimization under the over-coding paradigm can largely be offset through GPGPU processing.
{"title":"Optimization of “over-coded” radar waveforms","authors":"John Jakabosky, S. Blunt, B. Himed","doi":"10.1109/RADAR.2014.6875831","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875831","url":null,"abstract":"Polyphase-Coded FM (PCFM) radar waveforms generated using the power and spectrally efficient continuous phase modulation (CPM) framework can be further enhanced through the use of finer time control by subdividing each phase transition into sub-transitions and by allowing a greater phase excursion per transition interval, herein referred to as over-phasing. These two strategies are denoted collectively as “over-coding”. It is shown that various combinations of sub-transitions and over-phasing can greatly improve waveform design capabilities by expanding the available degrees-of-freedom. It is also demonstrated that the commensurate increase in computational complexity for optimization under the over-coding paradigm can largely be offset through GPGPU processing.","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":"116410006","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.2014.6875790
Suqi Li, Wei Yi, L. Kong, Bailu Wang
This paper considers the problem of simultaneously detecting and tracking multiple targets based on the unthres-holed, track-before-detect style measurement model. The problem is formulated in a Bayesian framework by modeling the collection of states as a random finite set. [1] is the pioneer addressing this problem. However, the application of this work is largely restricted by its independence assumption which only holds when targets are well separated. This paper is committed to generalize this method to accommodate the arbitrary placement of targets. To this end, we propose a dynamic factorization based multitarget Bayesian filter which utilizes independence between targets whenever possible, while considers target estimation jointly when target states exhibit correlation. A novel sequential Monte Carlo implementation for the proposed multi-target Bayesian filter is also presented. Simulation results for a scenario with two crossing targets show the superior performance of the proposed filter.
{"title":"Dynamic factorization based multi-target Bayesian filter for multi-target detection and tracking","authors":"Suqi Li, Wei Yi, L. Kong, Bailu Wang","doi":"10.1109/RADAR.2014.6875790","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875790","url":null,"abstract":"This paper considers the problem of simultaneously detecting and tracking multiple targets based on the unthres-holed, track-before-detect style measurement model. The problem is formulated in a Bayesian framework by modeling the collection of states as a random finite set. [1] is the pioneer addressing this problem. However, the application of this work is largely restricted by its independence assumption which only holds when targets are well separated. This paper is committed to generalize this method to accommodate the arbitrary placement of targets. To this end, we propose a dynamic factorization based multitarget Bayesian filter which utilizes independence between targets whenever possible, while considers target estimation jointly when target states exhibit correlation. A novel sequential Monte Carlo implementation for the proposed multi-target Bayesian filter is also presented. Simulation results for a scenario with two crossing targets show the superior performance of the proposed filter.","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":"116675746","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.2014.6875599
Weiwei Wang, Zhu Yalin, Hongyi Zhao, Sunyong Wu
With the increase of the swath width and imaging resolution, the resulting enormous amount of sampling raw data aggravates storage and transmission load for Multi-channel synthetic aperture radar (SAR) system. Considering the fact that the correlation among the multi-channel SAR images is high, we propose a compressive sensing (CS)-based ground moving target indication framework with sparse sampled raw data. In the proposed framework, the SAR imaging of one channel is utilized as prior-knowledge, the clutter of other channels is suppressed with only small amount of raw data. Thus the moving targets can be accurately recovered by compressive sensing after clutter suppression. Experiment results demonstrate the proposed method performs well with a very limited number of samples, even if clutter scattering centers are non-sparse.
{"title":"Clutter suppression and GMTI with sparse sampled data for dual-channel SAR","authors":"Weiwei Wang, Zhu Yalin, Hongyi Zhao, Sunyong Wu","doi":"10.1109/RADAR.2014.6875599","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875599","url":null,"abstract":"With the increase of the swath width and imaging resolution, the resulting enormous amount of sampling raw data aggravates storage and transmission load for Multi-channel synthetic aperture radar (SAR) system. Considering the fact that the correlation among the multi-channel SAR images is high, we propose a compressive sensing (CS)-based ground moving target indication framework with sparse sampled raw data. In the proposed framework, the SAR imaging of one channel is utilized as prior-knowledge, the clutter of other channels is suppressed with only small amount of raw data. Thus the moving targets can be accurately recovered by compressive sensing after clutter suppression. Experiment results demonstrate the proposed method performs well with a very limited number of samples, even if clutter scattering centers are non-sparse.","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":"114505510","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.2014.6875642
H. Jiang, Wei Yi, L. Kong, G. Cui, Xiaobo Yang
This paper considers the radar detection and tracking of weak fluctuating targets in heterogeneous clutter via dynamic programming based track-before-detect (DP-TBD). The target fluctuating satisfies the well-known Swerling I model and radar clutter is modeled by G0 distribution, which is widely used to model heterogeneous clutter received by small grazing angle or high resolution radar. In this case, the log-likelihood ratio (LLR), which utilizes the clutter distribution information and the target fluctuating information, is required during the integration process of DP-TBD. Since no closed-form solution of LLR exists under this condition, we present a fast but accurate LLR approximation using variable resolution grid based method. Various simulations are used to examine the performance of the DP-TBD using the approximate LLR.
{"title":"Track-before-detect for fluctuating targets in heterogeneous clutter","authors":"H. Jiang, Wei Yi, L. Kong, G. Cui, Xiaobo Yang","doi":"10.1109/RADAR.2014.6875642","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875642","url":null,"abstract":"This paper considers the radar detection and tracking of weak fluctuating targets in heterogeneous clutter via dynamic programming based track-before-detect (DP-TBD). The target fluctuating satisfies the well-known Swerling I model and radar clutter is modeled by G0 distribution, which is widely used to model heterogeneous clutter received by small grazing angle or high resolution radar. In this case, the log-likelihood ratio (LLR), which utilizes the clutter distribution information and the target fluctuating information, is required during the integration process of DP-TBD. Since no closed-form solution of LLR exists under this condition, we present a fast but accurate LLR approximation using variable resolution grid based method. Various simulations are used to examine the performance of the DP-TBD using the approximate LLR.","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":"115818773","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.2014.6875816
Y. Abramovich, G. S. Antonio, G. Frazer, Charlie G. Williams
This paper proposes superdirective adaptive mode-selective processing in oversampled 2D transmitting (Tx) and receiving (Rx) antenna arrays, enabled by MIMO radar technology, for skywave OTHR applications. The actual reactive power in Tx antenna arrays is controlled by considering two main principles: (1) avoiding extreme steering angles in conventionally beamformed (for a single waveform) sub-arrays and (2) using time-division MIMO waveform sets with no simultaneous transmissions of different (orthogonal) MIMO waveforms from TX array phase centers separated by less than half the radar wavelength. We consider the propagation modes as per [1] to demonstrate the utility of the proposed technique for Tx and Rx antenna arrays with apertures significantly smaller than those introduced in [1].
{"title":"Superdirective mode selective OTHR with time-division MIMO beamforming","authors":"Y. Abramovich, G. S. Antonio, G. Frazer, Charlie G. Williams","doi":"10.1109/RADAR.2014.6875816","DOIUrl":"https://doi.org/10.1109/RADAR.2014.6875816","url":null,"abstract":"This paper proposes superdirective adaptive mode-selective processing in oversampled 2D transmitting (Tx) and receiving (Rx) antenna arrays, enabled by MIMO radar technology, for skywave OTHR applications. The actual reactive power in Tx antenna arrays is controlled by considering two main principles: (1) avoiding extreme steering angles in conventionally beamformed (for a single waveform) sub-arrays and (2) using time-division MIMO waveform sets with no simultaneous transmissions of different (orthogonal) MIMO waveforms from TX array phase centers separated by less than half the radar wavelength. We consider the propagation modes as per [1] to demonstrate the utility of the proposed technique for Tx and Rx antenna arrays with apertures significantly smaller than those introduced in [1].","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":"114773843","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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