LSE and MSE optimum partition-based FIR-IIR deconvolution filters with best delay

Date

2005-10-01

Author

Tuncer, Temel Engin

Metadata

Show full item record

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

Item Usage Stats

187
views

0
downloads

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, optimum partitioning and ring-based partitioning.,, Resulting FIR-IIR filters are compared with the FIR and FIR-IIR unit circle best delay inverse filters in terms of their LSE. It is shown that the partition-based FIR-IIR filter performances are about 4-5 dB better than their FIR counterpart for the same complexity. Then, starting with the best delay FIR Wiener deconvolution filter formulation, we extended our results for the design of FIR-IIR MSE optimum deconvolution filters. We have compared the optimum-partitioning and ring-based FIR-IIR inverse filters with the FIR and IIR Wiener deconvolution filters. It is shown that FIR-IIR best delay inverse filters are less sensitive to the estimation errors compared with the IIR Wiener filters, and they perform better than the FIR Wiener filters. Furthermore, they are always causal and stable, making them suitable for real-time implementations.

Subject Keywords

Signal Processing, Electrical and Electronic Engineering

URI

https://hdl.handle.net/11511/39948

Journal

IEEE TRANSACTIONS ON SIGNAL PROCESSING

DOI

https://doi.org/10.1109/tsp.2005.855394

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Risk-sensitive particle filters for mitigating sample impoverishment Orguner, Umut (Institute of Electrical and Electronics Engineers (IEEE), 2008-10-01) 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-sensitiv...
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...
Factor Graph Based LMMSE Filtering for Colored Gaussian Processes Sen, Pinar; Yılmaz, Ali Özgür (Institute of Electrical and Electronics Engineers (IEEE), 2014-10-01) We propose a reduced complexity, graph based linear minimum mean square error (LMMSE) filter in which the non-white statistics of a random noise process are taken into account. Our method corresponds to block LMMSE filtering, and has the advantage of complexity linearly increasing with the block length and the ease of incorporating the a priori information of the input signals whenever possible. The proposed method can be used with any random process with a known autocorrelation function by use of an approx...
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...
Wavelet domain image resolution enhancement using cycle spinning and edge modelling Temizel, Alptekin (2005-09-08) In this paper we present a wavelet domain image resolution en-hancement algorithm. An initial high-resolution approximation to the original image is obtained by means of zero-padding in the wavelet domain. This is further processed using the cycle-spinning methodology which reduces ringing. A critical element of the algo-rithm is the adoption of a simplified edge profile suitable for the description of edge degradations such as blurring due to loss of resolution. Linear regression using a minimal training s...

Citation Formats

T. E. Tuncer, “LSE and MSE optimum partition-based FIR-IIR deconvolution filters with best delay,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, pp. 3780–3790, 2005, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39948.