Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure...

In this work, we derive the maximum a posteriori (MAP) symbol detector for a multiple-input multiple-output system in the presence of Wiener phase noise due to noisy local oscillators. As in single-antenna systems, the computation of the optimal receiver is an infinite dimensional problem and is thus unimplementable in practice. In this purview, we propose three suboptimal, low-complexity algor...

This paper presents a complete procedure for sensor compatibility correction of a fixed-wing Unmanned Air Vehicle (UAV). The sensors consist of a differential air pressure transducer for airspeed measurement, two airdata vanes installed on an airdata probe for angle of attack (AoA) and angle of sideslip (AoS) measurement, and an Attitude and Heading Reference System (AHRS) that provides attitud...

Consider a multivariate Gaussian random vector which can be partitioned into observed and unobserved components.We review a technique proposed almost twenty years ago in the astrophysics literature to sample from the posterior Gaussian distribution of the unobserved components given the observed components [6]. This technique can be computationally cheaper than the standard approach which requi...

Abstract The optimal maximum-likelihood multi-user detection problem in synchronous Code-Division Multiple Access (CDMA) is NP-hard; the asynchronous version of the problem is super-exponential. The computational difficulty has driven research into suboptimal algorithms that provide reliable decisions and ensure polynomial computational costs. The Probabilistic Data Association (PDA) method, or...

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a hybrid Markov Chain Monte Carlo sampling algorithm. Candidate draws for the unobserved volatilities are obtained ...

In this paper Bayesian methods are applied to a stochastic volatility model using both the prices of the asset and the prices of options written on the asset. Posterior densities for all model parameters, latent volatilities and the market price of volatility risk are produced via a Markov Chain Monte Carlo sampling algorithm. Candidate draws for the unobserved volatilities are obtained in bloc...

Abstract We propose a state estimation approach to time-varying magnetic resonance imaging utilizing priori information. In estimation, the time-dependent image reconstruction problem is modeled by separate evolution and observation models. our method, we compute estimates using Kalman filter steady-state smoother data-driven estimate for process noise covariance matrix, constructed from conven...