Non-Linear Filtering based on Observations from Student's t Processes

Date

2012-03-10

Author

Saha, Saikat
Orguner, Umut
Gustafsson, Fredrik

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

106
views

0
downloads

We consider measurements from possibly zero-mean stochastic processes in a nonlinear filtering framework. This is a challenging problem, since it is only the second order properties of the measurements that bear information about the unknown state vector. The covariance function of the measurements can have both spatial and temporal correlation that depend on the state. Recently, a solution to this problem was presented for the case of Gaussian processes. We here extend the theory to Student's t processes. We illustrate the state observability by a simple but still realistic simulation example.

URI

https://hdl.handle.net/11511/36617

DOI

https://doi.org/10.1109/aero.2012.6187210

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Non-linear filtering based on observations from Gaussian processes Gustafsson, Fredrik; Saha, Saikat; Orguner, Umut (2011-03-12) We consider a class of non-linear filtering problems, where the observation model is given by a Gaussian process rather than the common non-linear function of the state and measurement noise. The new observation model can be considered as a generalization of the standard one with correlated measurement noise in both time and space. We propose a particle filter based approach with a measurement update step that requires a memory of past observations which can be truncated using a moving window to obtain a fi...
Frequency estimation of a single real-valued sinusoid: An invariant function approach Candan, Çağatay; Çelebi, Utku (2021-08-01) An invariant function approach for the computationally efficient (non-iterative and gridless) maximum likelihood (ML) estimation of unknown parameters is applied on the real-valued sinusoid frequency estimation problem. The main attraction point of the approach is its potential to yield a ML-like performance at a significantly reduced computational load with respect to conventional ML estimator that requires repeated evaluation of an objective function or numerical search routines. The numerical results ind...
Meta Soft Label Generation for Noisy Labels Algan, Gorkem; Ulusoy, İlkay (2021-01-01) The existence of noisy labels in the dataset causes significant performance degradation for deep neural networks (DNNs). To address this problem, we propose a Meta Soft Label Generation algorithm called MSLG, which can jointly generate soft labels using meta-learning techniques and learn DNN parameters in an end-to-end fashion. Our approach adapts the meta-learning paradigm to estimate optimal label distribution by checking gradient directions on both noisy training data and noise-free meta-data. In order t...
Nonintrusive model order reduction for cross-diffusion systems Karasözen, Bülent; Mülayim, Gülden; Uzunca, Murat (2022-12-01) In this paper, we investigate tensor based nonintrusive reduced-order models (ROMs) for parametric cross-diffusion equations. The full-order model (FOM) consists of ordinary differential equations (ODEs) in matrix or tensor form resulting from finite difference discretization of the differential operators by taking the advantage of Kronecker structure. The matrix/tensor differential equations are integrated in time with the implicit–explicit (IMEX) Euler method. The reduced bases, relying on a finite sample...
Multitarget tracking performance metric: deficiency aware subpattern assignment Oksuz, Kemal; CEMGİL, ALİ TAYLAN (2018-03-01) Multitarget tracking is a sequential estimation problem where conditioned on noisy sensor measurements, state variables of several targets need to be estimated recursively. In this study, the authors propose a novel performance measure for multitarget tracking named as Deficiency Aware Subpattern Assignment (DASA), that can be used to consistently compare algorithms in a broad spectrum of formulations ranging from conventional data association methods to random finite set based multitarget tracking algorith...

Citation Formats

S. Saha, U. Orguner, and F. Gustafsson, “Non-Linear Filtering based on Observations from Student’s t Processes,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36617.