1,331 research outputs found
A New MCMC Sampling Based Segment Model for Radar Target Recognition
One of the main tools in radar target recognition is high resolution range profile (HRRP)â. âHoweverâ, âit is very sensitive to the aspect angleâ. âOne solution to this problem is to assume the consecutive samples of HRRP identically independently distributed (IID) in small frames of aspect anglesâ, âan assumption which is not true in realityâ. âHowever, bââased on this assumptionâ, âsome models have been developed to characterize the sequential information contained in the multi-aspect radar echoesâ. âThereforeâ, âthey only consider the short dependency between consecutive samplesâ. âHereâ, âwe propose an alternative modelâ, âthe segment modelâ, âto address the shortcomings of these assumptionsâ. âIn additionâ, âusing a Markov chain Monte-Carlo (MCMC) based Gibbs sampler as an iterative approach to estimate the parameters of the segment modelâ, âwe will show that the proposed method is able to estimate the parameters with quite satisfying accuracy and computational loadâ
Learning Hybrid System Models for Supervisory Decoding of Discrete State, with applications to the Parietal Reach Region
Based on Gibbs sampling, a novel method to identify mathematical models of neural activity in response to temporal changes of behavioral or cognitive state is presented. This work is motivated by the developing field of neural prosthetics, where a supervisory controller is required to classify activity of a brain region into suitable discrete modes. Here, neural activity in each discrete mode is modeled with nonstationary point processes, and transitions between modes are modeled as hidden Markov models. The effectiveness of this framework is first demonstrated on a simulated example. The identification algorithm is then applied to extracellular neural activity recorded from multi-electrode arrays in the parietal reach region of a rhesus monkey, and the results demonstrate the ability to decode discrete changes even from small data sets
Joint segmentation of multivariate astronomical time series : bayesian sampling with a hierarchical model
Astronomy and other sciences often face the problem of detecting and characterizing structure in two or more related time series. This paper approaches such problems using Bayesian priors to represent relationships between signals with various degrees of certainty, and not just rigid constraints. The segmentation is conducted by using a hierarchical Bayesian approach to a piecewise constant Poisson rate model. A Gibbs sampling strategy allows joint estimation of the unknown parameters and hyperparameters. Results obtained with synthetic and real photon counting data illustrate the performance of the proposed algorithm
Linear and nonlinear adaptive filtering and their applications to speech intelligibility enhancement
Spectral analysis for nonstationary audio
A new approach for the analysis of nonstationary signals is proposed, with a
focus on audio applications. Following earlier contributions, nonstationarity
is modeled via stationarity-breaking operators acting on Gaussian stationary
random signals. The focus is on time warping and amplitude modulation, and an
approximate maximum-likelihood approach based on suitable approximations in the
wavelet transform domain is developed. This paper provides theoretical analysis
of the approximations, and introduces JEFAS, a corresponding estimation
algorithm. The latter is tested and validated on synthetic as well as real
audio signal.Comment: IEEE/ACM Transactions on Audio, Speech and Language Processing,
Institute of Electrical and Electronics Engineers, In pres
- âŠ