Risk-sensitive particle filters for mitigating sample impoverishment

Download

index.pdf

Date

2008-10-01

Author

Orguner, Umut

Metadata

Show full item record

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

Item Usage Stats

121
views

134
downloads

Risk-sensitive filters (RSF) put a penalty to higher-order moments of the estimation error compared to conventional filters as the Kalman filter minimizing the mean square error (MSE). The result is a more cautious filter, which can be interpreted as an implicit and automatic way to increase the state noise covariance. On the other hand, the process of jittering, or roughening, is well known in particle filters to mitigate sample impoverishment. The purpose of this contribution is to introduce risk-sensitive particle filters (RSPF) as an alternative approach to mitigate sample impoverishment based on constructing explicit risk functions from a general class of factorizable functions. It is first shown that RSF can be done in nonlinear systems using a recursion of an infinite dimensional information state which involves general risk functions. Then, this information state calculation is carried out using particle approximations. Some alternative approaches, generalizations, specific cases, comparison to existing methods of sample impoverishment mitigation and issues related to the selection of risk functions and parameters are examined. Performance of the resulting filter using various risk functions is illustrated on a simulated scenario and compared with the roughening method.

Subject Keywords

Signal Processing, Electrical and Electronic Engineering

URI

https://hdl.handle.net/11511/35288

Journal

IEEE TRANSACTIONS ON SIGNAL PROCESSING

DOI

https://doi.org/10.1109/tsp.2008.928520

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

LSE and MSE optimum partition-based FIR-IIR deconvolution filters with best delay Tuncer, Temel Engin (Institute of Electrical and Electronics Engineers (IEEE), 2005-10-01) We considered the least squares error (LSE) and mean square error (MSE) optimum best delay deconvolution filter design. We presented the partition-based finite impulse response-infinite impulse response (FIR-IIR) filters where the channel zeros are partitioned into two regions. The first region is composed of the selected channel zeros inside the unit circle, and the second region is composed of the remaining channel zeros outside the first region. Two methods for obtaining partitions are proposed, namely, ...
New initialization methods for discrete coefficient FIR filter design with coefficient scaling and the use of scale factor in the design process Çiloğlu, Tolga (Institute of Electrical and Electronics Engineers (IEEE), 2006-02-01) The initialization of filter coefficients in discrete-coefficient finite-impulse-response (FIR) filter design (with coefficient scaling) using coefficient-value-assignment-based optimization techniques is considered. A common weakness of existing initialization measures, a total-square-error (TSE) measure and a maximum-error (ME) measure, is described. New TSE and ME measures that overcome the weakness are introduced. As opposed to the current knowledge, it is revealed that TSE and ME measures do not necess...
Risk sensitive particle filters for mitigating sample impoverishment Orguner, Umut (2007-08-29) Risk-sensitive filters (RSF) put a penalty to higher-order moments of the estimation error compared to conventional filters as the Kalman filter minimizing the mean square error. The result is a more cautious filter, which can be interpreted as an implicit and automatic way to increase the state noise covariance. On the other hand, the process of jittering, or roughening, is well-known in particle filters to mitigate sample impoverishment. The purpose of this contribution is to introduce risk-sensitive part...
Robust adaptive unscented Kalman filter for attitude estimation of pico satellites Hacızade, Cengiz; Söken, Halil Ersin (Wiley, 2014-02-01) Unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation results for the estimation problems of nonlinear systems even when high nonlinearity is in question. However, in case of system uncertainty or measurement malfunctions, the UKF becomes inaccurate and diverges by time. This study introduces a fault-tolerant attitude estimation algorithm for pico satellites. The algorithm uses a robust adaptive UKF, which performs correction for the process noise covariance (Q-adapt...
Deconvolution and preequalization with best delay LS inverse filters Tuncer, Temel Engin (Elsevier BV, 2004-11-01) A new method for finding the best delay for the design of least-squares (1,S) inverse filters is introduced. It is shown that there is a considerable difference between the LS errors of a best delay filter and an arbitrary LS inverse filter. Proposed method is an effective and computationally efficient approach for the design of LS optimum filters. Deconvolution problem is considered and the MSE performances of pseudoinverse, preequalization and LS inverse filtering are investigated. In this respect, the th...

Citation Formats

U. Orguner, “Risk-sensitive particle filters for mitigating sample impoverishment,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, pp. 5001–5012, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35288.