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FIR Filter Design via Spectral Factorization and Convex Optimization
FIR Filter Design Spectral Factorization Convex Optimization
2015/7/13
We consider the design of finite impulse response (FIR) filters subject to upper and lower bounds on the frequency response magnitude. The associated optimization problems, with the filter coefficient...
This paper presents a novel approach for constrained state estimation from noisy measurements. The optimal trending algorithms described in this paper assume that the trended system variables have the...
We introduce a heuristic for designing filters that have low complexity coefficients, as measured by the total number of nonzeros digits in the binary or canonic signed digit (CSD) representations of ...
Covariance inflation in the ensemble Kalman filter: a residual nudging perspective and some implications
Covariance inflation ensemble Kalman filter residual nudging perspective some implications
2013/6/17
This note examines the influence of covariance inflation on the distance between the measured observation and the simulated (or predicted) observation with respect to the state estimate. In order for ...
The parameters of temporal models, such as dynamic Bayesian networks, may be modelled in a Bayesian context as static or atemporal variables that influence transition probabilities at every time step....
A multiple filter test for change point detection in renewal processes with varying variance
A multiple filter test change point detection renewal processes varying variance
2013/4/27
Non-stationarity of the event rate is a persistent problem in modeling time series of events, such as neuronal spike trains. Motivated by a variety of patterns in neurophysiological spike train record...
Toward Practical N2 Monte Carlo: the Marginal Particle Filter
Practical N2 Monte Carlo Marginal Particle Filter
2012/9/19
Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dy-namic models. These methods allow us to ap-proximate the joint posterior distribution using sequential ...
A two-stage denoising filter: the preprocessed Yaroslavsky filter
Image denoising Yaroslavsky lter Wavelets Curvelets Nonlocal Means
2012/9/18
This paper describes a simple image noise removal method which combines a preprocessing step with the Yaroslavsky lter for strong numerical, visual, and theoretical performance on a broad class of im...
Bridging the ensemble Kalman and particle filter
Bridging the ensemble Kalman particle filter
2012/9/17
In many applications of Monte Carlo nonlinear filtering, the propagation step is com-putationally expensive, and hence, the sample size is limited. With small sample sizes, the update step becomes cru...
A higher order correlation unscented Kalman filter
Sequential Parameter Estimation Nonlinear Systems Unscented Kalman Filter Continuous-discrete State Space Estimation of Uncorrelated States Volatility Estimation
2012/9/19
Many nonlinear extensions of the Kalman filter, e.g., the extended and the unscented Kalman filter, reduce the state densities to Gaussian densities. This approximation gives sufficient results in man...
Parameter estimation in the stochastic Morris-Lecar neuronal model with particle filter methods
Parameter estimatio stochastic Morris-Lecar neuronal mode particle filter methods
2012/9/19
In this paper, we consider the classic measurement error regression scenario in which our independent,or design, variables are observed with several sources of additive noise. We will show that our mo...
INS/GPS 组合导航系统的本质是非线性的, 为改善非线性下INS/GPS 组合导航精度, 提出将一种新的非线性滤波cubature Kalman filter(CKF) 应用于INS/GPS 组合导航中. 为此, 建立了基于平台失准角的非线性状态模型和以速度误差及位置误差描述的观测模型, 分析了CKF 滤波原理, 设计了INS/GPS 组合滤波器, 对组合导航非线性模型进行了仿真. 仿真结果显...
Comparison of SCIPUFF Plume Prediction with Particle Filter Assimilated Prediction for Dipole Pride 26 Data
Data Assimilation Particle Filter
2011/7/19
This paper presents the application of a particle filter for data assimilation in the context of puff-based dispersion models. Particle filters provide estimates of the higher moments, and are well su...
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
Hidden Markov Models State-Space Models Sequential Monte Carlo
2011/7/5
Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models.
Data Driven Computing by the Morphing Fast Fourier Transform Ensemble Kalman Filter in Epidemic Spread Simulations
Data Driven Computing Morphing Fast Fourier Transform Ensemble Kalman Filter Epidemic Spread Simulations
2010/3/11
The FFT EnKF data assimilation method is proposed and applied to a stochastic
cell simulation of an epidemic, based on the S-I-R spread model. The FFT EnKF
combines spatial statistics and ensemble f...