92,842 research outputs found
Adaptive Bayesian State Estimation Integrating Non-stationary DGNSS Inter-Agent Distances
Bayesian navigation filters are broadly exploited in precise state estimation for kinematic applications such as vehicular positioning and navigation. Among these, Particle Filter (PF) has been shown as a valuable solution to support hybrid positioning algorithms such as sensor fusion to Global Navigation Satellite System (GNSS) and Cooperative Positioning (CP). Despite of an increased computational complexity w.r.t. conventional Kalman Filters (KFs), an effective weighting of the input measurements generally provides an improved accuracy of the output estimate. In the framework of the Differential GNSS (DGNSS) CP, this work presents an algorithm for the automated selection of the most appropriate error models for the tight-integration of non-stationary Differential GNSS (DGNSS) collaborative inter-agent distances. A model switching technique named Automated Adaptive Likelihood Switch (AALS) is proposed for a Cognitive Particle Filter (C-PF) architecture, based on the real-time approximation of the statistics of the inter-agent distances errors. The results achieved through realistic simulations demonstrated the effectiveness of the proposed solution in terms of error model selection. Therefore, an improvement of the position estimation accuracy was observed, since the cases in which DGNSS-CP would degrade performance due to possible mismodelling of the selected likelihood function are avoided
Dynamic Compressive Sensing of Time-Varying Signals via Approximate Message Passing
In this work the dynamic compressive sensing (CS) problem of recovering
sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear
measurements is explored from a Bayesian perspective. While there has been a
handful of previously proposed Bayesian dynamic CS algorithms in the
literature, the ability to perform inference on high-dimensional problems in a
computationally efficient manner remains elusive. In response, we propose a
probabilistic dynamic CS signal model that captures both amplitude and support
correlation structure, and describe an approximate message passing algorithm
that performs soft signal estimation and support detection with a computational
complexity that is linear in all problem dimensions. The algorithm, DCS-AMP,
can perform either causal filtering or non-causal smoothing, and is capable of
learning model parameters adaptively from the data through an
expectation-maximization learning procedure. We provide numerical evidence that
DCS-AMP performs within 3 dB of oracle bounds on synthetic data under a variety
of operating conditions. We further describe the result of applying DCS-AMP to
two real dynamic CS datasets, as well as a frequency estimation task, to
bolster our claim that DCS-AMP is capable of offering state-of-the-art
performance and speed on real-world high-dimensional problems.Comment: 32 pages, 7 figure
Application of Dirichlet Distribution for Polytopic Model Estimation
The polytopic model (PM) structure is often used in the areas of automatic control and fault detection and isolation (FDI). It is an alternative to the multiple model approach which explicitly allows for interpolation among local models. This thesis proposes a novel approach to PM estimation by modeling the set of PM weights as a random vector with Dirichlet Distribution (DD). A new approximate (adaptive) PM estimator, referred to as a Quasi-Bayesian Adaptive Kalman Filter (QBAKF) is derived and implemented. The model weights and state estimation in the QBAKF is performed adaptively by a simple QB weights\u27 estimator and a single KF on the PM with the estimated weights. Since PM estimation problem is nonlinear and non-Gaussian, a DD marginalized particle filter (DDMPF) is also developed and implemented similar to MPF. The simulation results show that the newly proposed algorithms have better estimation accuracy, design simplicity, and computational requirements for PM estimation
Application of Dirichlet Distribution for Polytopic Model Estimation
The polytopic model (PM) structure is often used in the areas of automatic control and fault detection and isolation (FDI). It is an alternative to the multiple model approach which explicitly allows for interpolation among local models. This thesis proposes a novel approach to PM estimation by modeling the set of PM weights as a random vector with Dirichlet Distribution (DD). A new approximate (adaptive) PM estimator, referred to as a Quasi-Bayesian Adaptive Kalman Filter (QBAKF) is derived and implemented. The model weights and state estimation in the QBAKF is performed adaptively by a simple QB weights\u27 estimator and a single KF on the PM with the estimated weights. Since PM estimation problem is nonlinear and non-Gaussian, a DD marginalized particle filter (DDMPF) is also developed and implemented similar to MPF. The simulation results show that the newly proposed algorithms have better estimation accuracy, design simplicity, and computational requirements for PM estimation
Complex Sparse Signal Recovery with Adaptive Laplace Priors
Because of its self-regularizing nature and uncertainty estimation, the
Bayesian approach has achieved excellent recovery performance across a wide
range of sparse signal recovery applications. However, most methods are based
on the real-value signal model, with the complex-value signal model rarely
considered. Typically, the complex signal model is adopted so that phase
information can be utilized. Therefore, it is non-trivial to develop Bayesian
models for the complex-value signal model. Motivated by the adaptive least
absolute shrinkage and selection operator (LASSO) and the sparse Bayesian
learning (SBL) framework, a hierarchical model with adaptive Laplace priors is
proposed for applications of complex sparse signal recovery in this paper. The
proposed hierarchical Bayesian framework is easy to extend for the case of
multiple measurement vectors. Moreover, the space alternating principle is
integrated into the algorithm to avoid using the matrix inverse operation. In
the experimental section of this work, the proposed algorithm is concerned with
both complex Gaussian random dictionaries and directions of arrival (DOA)
estimations. The experimental results show that the proposed algorithm offers
better sparsity recovery performance than the state-of-the-art methods for
different types of complex signals
Characterization of a qubit Hamiltonian using adaptive measurements in a fixed basis
We investigate schemes for Hamiltonian parameter estimation of a two-level
system using repeated measurements in a fixed basis. The simplest (Fourier
based) schemes yield an estimate with a mean square error (MSE) that decreases
at best as a power law ~N^{-2} in the number of measurements N. By contrast, we
present numerical simulations indicating that an adaptive Bayesian algorithm,
where the time between measurements can be adjusted based on prior measurement
results, yields a MSE which appears to scale close to \exp(-0.3 N). That is,
measurements in a single fixed basis are sufficient to achieve exponential
scaling in N.Comment: 5 pages, 3 figures, 1 table. Published versio
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