摘要 :
A novel envelope alignment technique for terahertz radar ISAR imaging of maneuvering targets is presented in this paper. This method selects a single pulse echo as the envelope alignment benchmark through the correlation coefficie...
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A novel envelope alignment technique for terahertz radar ISAR imaging of maneuvering targets is presented in this paper. This method selects a single pulse echo as the envelope alignment benchmark through the correlation coefficient of adjacent range profiles, which solves the problems of jump error and drift error in the conventional adjacent amplitude correlation method. The imaging results of measured terahertz data demonstrate the effectiveness of this improved envelope alignment technique based on the adjacent amplitude correlation method.
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摘要 :
A novel envelope alignment technique for terahertz radar ISAR imaging of maneuvering targets is presented in this paper. This method selects a single pulse echo as the envelope alignment benchmark through the correlation coefficie...
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A novel envelope alignment technique for terahertz radar ISAR imaging of maneuvering targets is presented in this paper. This method selects a single pulse echo as the envelope alignment benchmark through the correlation coefficient of adjacent range profiles, which solves the problems of jump error and drift error in the conventional adjacent amplitude correlation method. The imaging results of measured terahertz data demonstrate the effectiveness of this improved envelope alignment technique based on the adjacent amplitude correlation method.
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摘要 :
Inverse Synthetic Aperture Radar (ISAR) imaging of maneuvering target has received much attention in recent years. In the paper, we first discuss translational motion compensation (TMC), which can be decomposed into two step: enve...
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Inverse Synthetic Aperture Radar (ISAR) imaging of maneuvering target has received much attention in recent years. In the paper, we first discuss translational motion compensation (TMC), which can be decomposed into two step: envelope alignment and autofocus. There are many effective algorithms for these two step, but most of algorithms are only suitable to steadily flying targets. The analysis of the paper shows that the envelope alignment algorithms for steadily flying targets are still effective for maneuvering targets, but most of autofocus algorithms are not work very well, especially when the maneuverability is great, so this paper also presents a new autofocus algorithm based on coherent intergation to solve this problem. Then after TMC, we discuss imaging problem of maneuvering target, in fact, it is a instantaneous spectrum estimation problem, most of instantaneous ISAR imaging methods of maneuvering target are based on time-frequency distribution, and suppose scatterer echo has constant amplitude and linear frequency modulation. In fact, when target has dihedrals and trihedrals component, or scatterer' migration through resolution cells (MTRC) exists, these situation all will cause the amplitude is not a constant. For maneuvering target, scatterer echo's Doppler is time varying, usually, its time-frequency distribution can be regard as linear or part linear. So we propose a instantaneous ISAR imaging method based on multicomponent amplitude modulation and linear frequency modulation(AM-LFM) signal's parameter estimation, this method first estimates the chirp rate of one component by dechirping, then demodulates this component to a sinusoidal signal, separates and cleans this component in Fourier domain, and estimates instantaneous amplitude, then for next component, finally we get all the scattterers instantaneous amplitudes and frequencies, and the instantaneous ISAR images are obtained now.
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摘要 :
Inverse Synthetic Aperture Radar (ISAR) imaging of maneuvering target has received much attention in recent years. In the paper, we first discuss translational motion compensation (TMC), which can be decomposed into two step: enve...
展开
Inverse Synthetic Aperture Radar (ISAR) imaging of maneuvering target has received much attention in recent years. In the paper, we first discuss translational motion compensation (TMC), which can be decomposed into two step: envelope alignment and autofocus. There are many effective algorithms for these two step, but most of algorithms are only suitable to steadily flying targets. The analysis of the paper shows that the envelope alignment algorithms for steadily flying targets are still effective for maneuvering targets, but most of autofocus algorithms are not work very well, especially when the maneuverability is great, so this paper also presents a new autofocus algorithm based on coherent intergation to solve this problem. Then after TMC, we discuss imaging problem of maneuvering target, in fact, it is a instantaneous spectrum estimation problem, most of instantaneous ISAR imaging methods of maneuvering target are based on time-frequency distribution, and suppose scatterer echo has constant amplitude and linear frequency modulation. In fact, when target has dihedrals and trihedrals component, or scatterer' migration through resolution cells (MTRC) exists, these situation all will cause the amplitude is not a constant. For maneuvering target, scatterer echo's Doppler is time varying, usually, its time-frequency distribution can be regard as linear or part linear. So we propose a instantaneous ISAR imaging method based on multicomponent amplitude modulation and linear frequency modulation(AM-LFM) signal's parameter estimation, this method first estimates the chirp rate of one component by dechirping, then demodulates this component to a sinusoidal signal, separates and cleans this component in Fourier domain, and estimates instantaneous amplitude, then for next component, finally we get all the scattterers instantaneous amplitudes and frequencies, and the instantaneous ISAR images are obtained now.
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摘要 :
Envelope alignment is important for ISAR imaging of moving target. If the target scattering characteristics scintillates during the coherent processing interval (CPI), jumping errors may occur in envelop alignment based on maximum...
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Envelope alignment is important for ISAR imaging of moving target. If the target scattering characteristics scintillates during the coherent processing interval (CPI), jumping errors may occur in envelop alignment based on maximum-correlation, which will lead to unexpected envelope migration. With the exploitation of target motion information, a new method is proposed to eliminate jumping errors. In detail, the migration values obtained by conventional maximum correlation are considered as measured data. Meanwhile, the target motion is described by linear model for the prediction and correction of envelope migration. Therefore, the measured data and predicted information are fused by regularization method to provide precise estimate of envelope migration. The results based on real measured data show that the proposed algorithm can effectively eliminate jumping errors and improve the performance of envelope alignment.
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摘要 :
Envelope alignment is important for ISAR imaging of moving target. If the target scattering characteristics scintillates during the coherent processing interval (CPI), jumping errors may occur in envelop alignment based on maximum...
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Envelope alignment is important for ISAR imaging of moving target. If the target scattering characteristics scintillates during the coherent processing interval (CPI), jumping errors may occur in envelop alignment based on maximum-correlation, which will lead to unexpected envelope migration. With the exploitation of target motion information, a new method is proposed to eliminate jumping errors. In detail, the migration values obtained by conventional maximum correlation are considered as measured data. Meanwhile, the target motion is described by linear model for the prediction and correction of envelope migration. Therefore, the measured data and predicted information are fused by regularization method to provide precise estimate of envelope migration. The results based on real measured data show that the proposed algorithm can effectively eliminate jumping errors and improve the performance of envelope alignment.
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摘要 :
Envelope alignment is important for ISAR imaging of moving target. If the target scattering characteristics scintillates during the coherent processing interval (CPI), jumping errors may occur in envelop alignment based on maximum...
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Envelope alignment is important for ISAR imaging of moving target. If the target scattering characteristics scintillates during the coherent processing interval (CPI), jumping errors may occur in envelop alignment based on maximum-correlation, which will lead to unexpected envelope migration. With the exploitation of target motion information, a new method is proposed to eliminate jumping errors. In detail, the migration values obtained by conventional maximum correlation are considered as measured data. Meanwhile, the target motion is described by linear model for the prediction and correction of envelope migration. Therefore, the measured data and predicted information are fused by regularization method to provide precise estimate of envelope migration. The results based on real measured data show that the proposed algorithm can effectively eliminate jumping errors and improve the performance of envelope alignment.
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There is an extensive literature using probabilistic models, such as hidden Markov models, for the analysis of biological sequences. These models have a clear theoretical basis, and many heuristics have been developed to reduce th...
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There is an extensive literature using probabilistic models, such as hidden Markov models, for the analysis of biological sequences. These models have a clear theoretical basis, and many heuristics have been developed to reduce the time and memory requirements of the dynamic programming algorithms used for their inference. Nevertheless, mirroring the shift in natural language processing, bioinformatics is increasingly seeing higher accuracy predictions made by recurrent neural networks (RNN). This shift is exemplified by basecalling on the Oxford Nanopore Technologies' sequencing platform, in which a continuous time series of current measurements is mapped to a string of nucleotides. Current basecallers have applied connectionist temporal classification (CTC), a method originally developed for speech recognition, and focused on the task of decoding RNN output from a single read. We wish to extend this method for the more general task of consensus basecalling from multiple reads, and in doing so, exploit the gains in both accelerated algorithms for sequence analysis and recurrent neural networks, areas that have advanced in parallel over the past decade. To this end, we develop a dynamic programming algorithm for consensus decoding from a pair of RNNs, and show that it can be readily optimized with the use of an alignment envelope. We express this decoding in the notation of finite state automata, and show that pair RNN decoding can be compactly represented using automata operations. We additionally introduce a set of Markov chain Monte Carlo moves for consensus basecalling multiple reads.
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摘要 :
There is an extensive literature using probabilistic models, such as hidden Markov models, for the analysis of biological sequences. These models have a clear theoretical basis, and many heuristics have been developed to reduce th...
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There is an extensive literature using probabilistic models, such as hidden Markov models, for the analysis of biological sequences. These models have a clear theoretical basis, and many heuristics have been developed to reduce the time and memory requirements of the dynamic programming algorithms used for their inference. Nevertheless, mirroring the shift in natural language processing, bioinformat-ics is increasingly seeing higher accuracy predictions made by recurrent neural networks (RNN). This shift is exemplified by basecalling on the Oxford Nanopore Technologies' sequencing platform, in which a continuous time series of current measurements is mapped to a string of nucleotides. Current basecallers have applied connectionist temporal classification (CTC), a method originally developed for speech recognition, and focused on the task of decoding RNN output from a single read. We wish to extend this method for the more general task of consensus basecalling from multiple reads, and in doing so, exploit the gains in both accelerated algorithms for sequence analysis and recurrent neural networks, areas that have advanced in parallel over the past decade. To this end, we develop a dynamic programming algorithm for consensus decoding from a pair of RNNs, and show that it can be readily optimized with the use of an alignment envelope. We express this decoding in the notation of finite state automata, and show that pair RNN decoding can be compactly represented using automata operations. We additionally introduce a set of Markov chain Monte Carlo moves for consensus basecalling multiple reads.
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The advancement of computerized modeling has allowed for the creation of extensive pneumatic tire models. These models have been used to determine many tire properties and tire-road interaction parameters which are either prohibit...
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The advancement of computerized modeling has allowed for the creation of extensive pneumatic tire models. These models have been used to determine many tire properties and tire-road interaction parameters which are either prohibitively expensive or unavailable with physical models.This paper focuses on the prediction of tire-ground interaction with emphasis on individual and combined effect of tire slip angle and camber angle at various operating parameters. The forces generated at tire contact such as rolling resistance, cornering force, aligning moment and overturning moment can be predicted and used to optimize the tire design parameters. In addition to above stated, the three-groove FEA truck tire model representing radial-ply tire of size 295/75R22.5 was used in vertical load deflection test to determine enveloping characteristics under various load conditions and inflation pressures.
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