In this work, a mathematical model for stochastic uncertain systems where the system uncertainty is handled by polynomial chaos method is developed. For uncertain systems where the system uncertainty is modeled by a f...
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In this work, a mathematical model for stochastic uncertain systems where the system uncertainty is handled by polynomial chaos method is developed. For uncertain systems where the system uncertainty is modeled by a first order polynomial chaos expansion, the estimation of the system states are done by an augmented Kalman filter equations developed by averaged least square method. The performance of the proposed robust estimation algorithm is shown by an uncertain system used as a framework example in previous works.
In this study, the mutual information between the state sequence and the measurements is proposed as an observability measure and this measure is analyzed in detail for linear time invariant discrete-time Gaussian sys...
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In this study, the mutual information between the state sequence and the measurements is proposed as an observability measure and this measure is analyzed in detail for linear time invariant discrete-time Gaussian systems. The following results are found as the two basic properties of the measure from the analyses; the unobservable states of the deterministic system have no effect on it. So the measure proposed here is an observability measure of observable states. Secondly, any observable part with no measurement uncertainty makes the measure infinite.
In this research, usage of the Doppler measurement as well as range and azimuth in tracking filter measurement vector to improve the target tracking performance in 2D pulse-Doppler radar is investigated. Tracking filt...
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This work proposes a maximum a posteriori (mAP) based parameter learning algorithm for acoustic-to-articulatory inversion. Inversion method is based on single global linear dynamic system (GLDS) representation of acou...
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This work proposes a maximum a posteriori (mAP) based parameter learning algorithm for acoustic-to-articulatory inversion. Inversion method is based on single global linear dynamic system (GLDS) representation of acoustic and articulatory data. mAP based learning algorithm considers a prior distribution for the parameter set as well as the likelihood of the training data. Therefore in this paper, we investigate the selection of prior distributions with hyperparameters for GLDS to improve the performance of articulatory inversion. The performance of the proposed learning algorithm and comparison of it with the maximum likelihood (mL) based learning method are examined on an extensive set of examples. These results show that the performance of the articulatory inversion method based on GLDS is significantly improved via mAP based learning algorithm.
In this study, we examined articulatory inversion using audiovisual information based on Gaussian mixture model (Gmm). In this method the joint distribution of the articulatory movement and audio (and/or visual) data ...
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In this study, we examined articulatory inversion using audiovisual information based on Gaussian mixture model (Gmm). In this method the joint distribution of the articulatory movement and audio (and/or visual) data are modelled via a mixture of Gaussians. The conditional expected value of the Gmm is used as regression function between the audio (and/orvisual) and ar-ticulatory spaces. We also examined various fusion methods in order to combine acoustic and visual information in articula-tory inversion. The fusion methods improve the performance of articulatory inversion.
In this study Imm-PF (Interacting multiple model - Particle Filter) algorithm has been developed in order to achieve increased performance for tracking maneuvering targets. Track lost rate and gate sizes are determine...
In this work, a mathematical model for stochastic uncertain systems where the system uncertainty is handled by polynomial chaos method is developed. For uncertain systems where the system uncertainty is modeled by a f...
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The observability measure based on the mutual information between state and/or state sequence and measurements, originally proposed by mohler and Hwang (1988), is analyzed in detail and improved further for linear tim...
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In this study, the mutual information between the state sequence and the measurements is proposed as an observability measure and this measure is analyzed in detail for linear time invariant discrete-time Gaussian sys...
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This work proposes a maximum a posteriori (mAP) based parameter learning algorithm for acoustic-to-articulatory inversion. Inversion method is based on single global linear dynamic system (GLDS) representation of acou...
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