Multiple Model Adaptive Estimation Algorithm for Systems with Parameter Change

Date

2016-01-01

Author

Söken, Halil Ersin

Metadata

Show full item record

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

Item Usage Stats

203
views

0
downloads

This paper presents an autonomous multiple model (AMM) estimation algorithm for systems with sudden parameter changes. Estimates of a bank of Kalman filters (KFs) are merged based on a newly defined likelihood function. The function is composed of two measures, one for weighting the filters during the steady state mode and the other for weighting when there is a change in the parameters. Compared to the interacting multiple model (IMM) method, the KFs do not interact but compete on the basis of the likelihood function without any necessity for the mixing step. The proposed algorithm is demonstrated for estimating the residual magnetic moment (RMM) affecting the spacecraft dynamics. The numerical results show that the Competing AMM (CAMM) guarantees higher steady state accuracy than the IMM with a simpler algorithm structure.

URI

https://hdl.handle.net/11511/69782

DOI

https://doi.org/10.1109/sice.2016.7749088

Collections

Department of Aerospace Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

A new likelihood approach to autonomous multiple model estimation Söken, Halil Ersin (Elsevier BV, 2020-04-01) This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules....
Multiple Description Coding of 3D Dynamic Meshes Based on Temporal Subsampling Bici, M. Oguz; Akar, Gözde (2010-01-21) In this paper, we propose a Multiple Description Coding (MDC) method for reliable transmission of compressed time consistent 3D dynamic meshes. It trades off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The method is based on partitioning the mesh frames into two sets by temporal subsampling and encoding each set independently by a 3D dynamic mesh coder. The encoded independent bitstreams or so-called descriptions are transm...
Online state estimation for discrete nonlinear dynamic systems with nonlinear noise and interference Demirbaş, Kerim (2015-01-01) This paper presents a real-time recursive state filtering and prediction scheme (PR) for discrete nonlinear dynamic systems with nonlinear noise and random interference, such as undesired random jamming or clutter. The PR is based upon discrete noise approximation, state quantization, and a suboptimal implementation of multiple composite hypothesis testing. The PR outperforms both the sampling importance resampling (SIR) particle filter and auxiliary sampling importance resampling (ASIR) particle filter; wh...
Multiple description scalar quantization based 3D mesh coding Bici, M. Oguz; Akar, Gözde (2006-10-11) In this paper, we address the problem of 3D Model transmission over error-prone channels using multiple description coding (MDC). The objective of MDC is to encode a source into multiple bitstreams, called descriptions, supporting multiple quality levels of decoding. Compared to layered coding techniques, each description can be decoded independently to approximate the model. In the proposed approach, the mesh geometry is compressed using multiresolution geometry compression. Then multiple descriptions are ...
A neuro-fuzzy MAR algorithm for temporal rule-based systems Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04) This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...

Citation Formats

H. E. Söken, “Multiple Model Adaptive Estimation Algorithm for Systems with Parameter Change,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/69782.