The Experts below are selected from a list of 219 Experts worldwide ranked by ideXlab platform
Wenchao Xue - One of the best experts on this subject based on the ideXlab platform.
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radar target recognition algorithm based on rcs Observation Sequence set valued identification method
Chinese Control Conference, 2014Co-Authors: Ting Wang, Yanlong Zhao, Wenchao XueAbstract:This paper studies the problem of radar target recognition based on radar cross section (RCS) Observation Sequence. First, the authors compute the discrete wavelet transform of RCS Observation Sequence and extract a valid statistical feature vector containing five components. These five components represent five different features of the radar target. Second, the authors establish a set-valued model to represent the relation between the feature vector and the authenticity of the radar target. By set-valued identification method, the authors can estimate the system parameter, based on which the recognition criteria is given. In order to illustrate the efficiency of the proposed recognition method, extensive simulations are given finally assuming that the true target is a cone frustum and the RCS of the false target is normally distributed. The results show that the set-valued identification method has a higher recognition rate than the traditional fuzzy classification method and evidential reasoning method.
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radar target recognition algorithm based on rcs Observation Sequence set valued identification method
Conference on Computational Complexity, 2014Co-Authors: Ting Wang, Yanlong Zhao, Wenchao XueAbstract:In this paper, we study the problem of target recognition based on RCS Observation Sequence. First, we use discrete wavelet transform of RCS Observation Sequence and extract five valid statistical features in the transform domain. Second, we establish the model and apply the method of set-valued identification to determine the relationship between these five characteristics and the target, thus giving recognition criteria. Finally, simulate the situation that the true target is frustum and the false target with normally distributed RCS. Comparing the results to the ones of fuzzy classification method and evidential reasoning method, the result shows that the set-valued identification method has a higher recognition rate.
Ting Wang - One of the best experts on this subject based on the ideXlab platform.
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radar target recognition algorithm based on rcs Observation Sequence set valued identification method
Chinese Control Conference, 2014Co-Authors: Ting Wang, Yanlong Zhao, Wenchao XueAbstract:This paper studies the problem of radar target recognition based on radar cross section (RCS) Observation Sequence. First, the authors compute the discrete wavelet transform of RCS Observation Sequence and extract a valid statistical feature vector containing five components. These five components represent five different features of the radar target. Second, the authors establish a set-valued model to represent the relation between the feature vector and the authenticity of the radar target. By set-valued identification method, the authors can estimate the system parameter, based on which the recognition criteria is given. In order to illustrate the efficiency of the proposed recognition method, extensive simulations are given finally assuming that the true target is a cone frustum and the RCS of the false target is normally distributed. The results show that the set-valued identification method has a higher recognition rate than the traditional fuzzy classification method and evidential reasoning method.
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radar target recognition algorithm based on rcs Observation Sequence set valued identification method
Conference on Computational Complexity, 2014Co-Authors: Ting Wang, Yanlong Zhao, Wenchao XueAbstract:In this paper, we study the problem of target recognition based on RCS Observation Sequence. First, we use discrete wavelet transform of RCS Observation Sequence and extract five valid statistical features in the transform domain. Second, we establish the model and apply the method of set-valued identification to determine the relationship between these five characteristics and the target, thus giving recognition criteria. Finally, simulate the situation that the true target is frustum and the false target with normally distributed RCS. Comparing the results to the ones of fuzzy classification method and evidential reasoning method, the result shows that the set-valued identification method has a higher recognition rate.
Yanlong Zhao - One of the best experts on this subject based on the ideXlab platform.
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radar target recognition algorithm based on rcs Observation Sequence set valued identification method
Chinese Control Conference, 2014Co-Authors: Ting Wang, Yanlong Zhao, Wenchao XueAbstract:This paper studies the problem of radar target recognition based on radar cross section (RCS) Observation Sequence. First, the authors compute the discrete wavelet transform of RCS Observation Sequence and extract a valid statistical feature vector containing five components. These five components represent five different features of the radar target. Second, the authors establish a set-valued model to represent the relation between the feature vector and the authenticity of the radar target. By set-valued identification method, the authors can estimate the system parameter, based on which the recognition criteria is given. In order to illustrate the efficiency of the proposed recognition method, extensive simulations are given finally assuming that the true target is a cone frustum and the RCS of the false target is normally distributed. The results show that the set-valued identification method has a higher recognition rate than the traditional fuzzy classification method and evidential reasoning method.
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radar target recognition algorithm based on rcs Observation Sequence set valued identification method
Conference on Computational Complexity, 2014Co-Authors: Ting Wang, Yanlong Zhao, Wenchao XueAbstract:In this paper, we study the problem of target recognition based on RCS Observation Sequence. First, we use discrete wavelet transform of RCS Observation Sequence and extract five valid statistical features in the transform domain. Second, we establish the model and apply the method of set-valued identification to determine the relationship between these five characteristics and the target, thus giving recognition criteria. Finally, simulate the situation that the true target is frustum and the false target with normally distributed RCS. Comparing the results to the ones of fuzzy classification method and evidential reasoning method, the result shows that the set-valued identification method has a higher recognition rate.
Iven Mareels - One of the best experts on this subject based on the ideXlab platform.
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estimation of noisy quantized gaussian ar time series with randomly varying Observation coefficient
IEEE Transactions on Signal Processing, 1995Co-Authors: Vikram Krishnamurthy, Iven MareelsAbstract:Presents an estimation algorithm for the parameters of Gaussian auto-regressive AR processes from one-bit quantized Observation Sequences. The input signal to the quantizer is the AR signal corrupted by multiplicative white Gaussian noise. The estimation algorithm is computationally inexpensive as it involves counting the number of occurrences of particular patterns of zeros and ones in the Observation Sequence. >
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estimation of noisy quantized random Observation coefficient ar time series
International Symposium on Information Theory, 1994Co-Authors: Vikram Krishnamurthy, Iven MareelsAbstract:We present a consistent, asymptotically normal estimation algorithm for the parameters of auto-regressive (AR) processes from 1-bit quantized Observations. The input signal to the quantifier is the AR signal corrupted by multiplicative white Gaussian noise. Our algorithm is computationally inexpensive as it involves counting the number of occurrences of particular patterns of zeros and ones in the Observation Sequence and then solving a Yule-Walker type system. >
Keiichi Tokuda - One of the best experts on this subject based on the ideXlab platform.
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Product of Experts for Statistical Parametric Speech Synthesis
IEEE Transactions on Audio, Speech, and Language Processing, 2012Co-Authors: Heiga Zen, Mark John Francis Gales, Yoshihiko Nankaku, Keiichi TokudaAbstract:Multiple acoustic models are often combined in statistical parametric speech synthesis. Both linear and non-linear functions of an Observation Sequence are used as features to be modeled. This paper shows that this combination of multiple acoustic models can be expressed as a product of experts (PoE); the likelihoods from the models are scaled, multiplied together, and then normalized. Normally these models are individually trained and only combined at the synthesis stage. This paper discusses a more consistent PoE framework where the models are jointly trained. A training algorithm for PoEs based on linear feature functions and Gaussian experts is derived by generalizing the training algorithm for trajectory HMMs. However for non-linear feature functions or non-Gaussian experts this is not possible, so a scheme based on contrastive divergence learning is described. Experimental results show that the PoE framework provides both a mathematically elegant way to train multiple acoustic models jointly and significant improvements in the quality of the synthesized speech.
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hidden markov models based on multi space probability distribution for pitch pattern modeling
International Conference on Acoustics Speech and Signal Processing, 1999Co-Authors: Keiichi Tokuda, Takashi Masuko, Noboru Miyazaki, Takao KobayashiAbstract:This paper discusses a hidden Markov model (HMM) based on multi-space probability distribution (MSD). The HMMs are widely-used statistical models to characterize the Sequence of speech spectra and have successfully been applied to speech recognition systems. From these facts, it is considered that the HMM is useful for modeling pitch patterns of speech. However, we cannot apply the conventional discrete or continuous HMMs to pitch pattern modeling since the Observation Sequence of the pitch pattern is composed of one-dimensional continuous values and a discrete symbol which represents "unvoiced". MSD-HMM includes discrete HMMs and continuous mixture HMMs as special cases, and further can model the Sequence of Observation vectors with variable dimension including zero-dimensional Observations, i.e., discrete symbols. As a result, MSD-HMMs can model pitch patterns without heuristic assumption. We derive a reestimation algorithm for the extended HMM and show that it can find a critical point of the likelihood function.
