SPTM-18.1

A Robust M-estimate Adaptive Filter For Impulse Noise Suppression
Yuexian Zou, S.C. Chan, T.S. Ng (Department of Electrical and Electronic Engineering, The University of Hong Kong)

In this paper, a robust M-estimate adaptive filter for impulse noise suppression is proposed. The objective function used is based on a robust M-estimate. It has the ability to ignore or down weight large signal error when certain thresholds are exceeded. A systematic method for estimating such thresholds for adaptation is also proposed. An advantage of the proposed method is that its solution is governed by a system of linear equation. Therefore, fast adaptation algorithms for traditional linear adaptive filters can be applied. In particular, a M-estimate recursive least square (M-RLS) adaptive algorithm is studied in detail. Simulation results show that it is more robust against individual and consecutive impulse noise than the MN-LMS [2] and the N-RLS algorithms [7]. It also has fast convergence speed and a low steady state error similar to its RLS counterpart.

SPTM-18.2

LOCAL ADAPTIVE DE-NOISING TECHNIQUES IN TRANSFORM DOMAIN FOR EMCG DE-NOISING
Hakan Oktem, Karen O Egiazarian (Tampere University of Technology Signal Processing Laboratory Finland), Juha Nousiainen (Tampere University of Technology Ragnar Granit Institute Finland)

There are various de-noising algorithms and optimization methods for different signal and noise characteristics. However, the signals used in real application may have deviations from the model. For example: signal and/or noise may not be stationary or a proper model for them may not be available. MCG (magnetocardiography) is an example signal, where conventional de-noising methods are not giving satisfactory results. Local adaptive processing allow to modify filtering parameters according to the specific properties of different �portions� of a signal. In this paper a methodology for adopting the transform domain local adaptive processing to the specific task of MCG signal de-noising is introduced.

SPTM-18.3

A Stochastic Diffusion Approach to Signal Denoising
Hamid Krim (ECE Dept., NCSU, Raleigh NC 27695-7914), Yufang Bao (ECE Dept., NCSU, Raleigh NC 27695-7914 and BUPT, P.R. China)

We present a stochastic formulation of a linear diffusion equation (or heat equation), and in light of the potential applications ranging from signal denoising to image enhancement/segmentation of its nonlinear extensions, we propose a more general nonlinear stochastic diffusion. The constructed stochastic framework, in contrast to traditional deterministic approaches, unveils the sources of of existing limitations and allows us to further significantly improve the performance by addressing the key problem. Substantiating examples are provided.

SPTM-18.4

Amplitude Estimation with Application to System Identification
Petre Stoica (Systems and Control Group, Uppsala University, Uppsala, Sweden.), Hongbin Li, Jian Li (Department of Electrical and Computer Engineering,University of Florida, Gainesville, Florida, USA.)

We investigate herein the problem of amplitude estimation of sinusoidal signals from observations corrupted by colored noise. A relatively large number of amplitude estimators are described which encompass Least Squares (LS) and Weighted Least Squares (WLS) methods. Additionally, filterbank approaches, which are widely used for spectral analysis, are extended to amplitude estimation. Specifically, we consider the recently introduced MAtched-FIlterbank (MAFI) approach and show that, by appropriately designing the prefilters, the MAFI approach includes the WLS approach. The amplitude estimation techniques discussed in this paper do not model the noise, and yet they are all asymptotically statistically efficient. It is their different finite-sample properties that are of particular interest to this study. Numerical examples are provided to illustrate the differences among the various estimators. Though amplitude estimation applications are numerous, we focus on system identification using sinusoidal probing signals.

SPTM-18.5

Performance Analysis of Two Approaches to Closed Loop System Identification via Cyclic Spectral Analysis
Channarong Tontiruttananon, Jitendra K Tugnait (Auburn University)

The problem of closed loop system identification given noisy time-domain input-output measurements is considered. It is assumed that the various disturbances affecting the system are zero-mean stationary whereas the closed loop system operates under an external cyclostationary input which is not measured. Noisy measurements of the (direct) input and output of the plant are assumed to be available. The closed loop system must be stable but it is allowed to be unstable in open loop. Recently we proposed two identification algorithms using cyclic-spectral analysis of noisy input-output data. In this paper we provide an asymptotic performance analysis of the recently proposed parameter estimators. Computer simulation examples are presented in support of the analysis.

SPTM-18.6

Blind Channel Identification Using RLS Method Based on Second-Order Statistics
Takashi Kimura, Hideaki Sasaki (R & D Lab. Kyushu Matsushita Electric Co., Ltd.), Hiroshi Ochi (Faculty of Eng., Univ. of the Ryukyus)

In this paper, we show a new blind identification algorithm which is based on second order statistics and exploits a Single-Input Double-Output(SIDO) model. It is suitable for a real-time processing system because of lower operation and high-speed convergence. The proposed blind identification algorithm is superior to conventional algorithms in view of simple structure and the uniqueness of solution. We also verify its efficiency by computer simulation.

SPTM-18.7

A Deterministic Blind Identification Technique for SIMO Systems of Unknown Model Order
Gopal T Venkatesan (University of Minnesota), Lang Tong (Cornell University), Mos Kaveh, Ahmed H Tewfik (University of Minnesota), Kevin M Buckley (Villanova University)

In this paper we present a method for the deterministic blind identification of single-input multiple-output systems with unknown model order. The technique, that is applicable to both the FIR and IIR cases, requires only an upper bound of the model order. It is based on the special kernel structure of block Toeplitz matrices. When the model order is overestimated, this special structure entails the true solution to be embedded in the overestimated solution in a unique shift-chain form. This special shift-chain structure is then utilized to extract the true solution.

SPTM-18.8

Blind Channel Equalization Using Weighted Subspace Methods
Rafael Ruiz, Margarita Cabrera ((Dept. of Signal Theory and Communications, Polytechnic University of Catalunya))

This paper addresses the problems of blind channel estimation and symbol detection with second order statistics methods from the received data. It can be shown that this problem is similar to Direction Of Arrival (DOA) estimation, where many solutions like the MUSIC algorithm or "weighted" techniques (as Deterministic Maximum Likelihood or Weighted Subspace Fitting method) have been developed. In this proposal we extend these techniques to blind channel identification problem in an unified framework known as Subspace Fitting. In this framework the estimated and the received data are "fitting" through the subspaces in a least square sense. Then, in order to solve this problem and estimate the channel, a modified Gauss-Newton type algorithm is suggested. Simulations are carried out comparing the proposed solutions with a classical signal subspace-based blind channel identification scheme.

SPTM-18.9

A Least Squares Component Normalization Approach to Blind Channel Identification.
Carlos Avendano (CIPIC, University of California at Davis), Jacob Benesty, Dennis R Morgan (Bell Laboratories, Lucent Technologies)

We describe a new method for blind system identification that uses the cross relation properties between two or more sensor signals to estimate the impulse responses of the channels. The method performs as well or better than other similar blind identification techniques under noisy and ill-conditioned channel conditions, and is computationally simpler to implement.

SPTM-18.10

Blind equalization of multiuser CDMA channels: a frequency-domain approach
Konstantinos I Diamantaras (Dept. of Informatics, Technological Education Institute, GR-54101 Sindos, Greece), Athina P Petropulu (Dept. of ECE, Drexel University, Philadelphia PA 19104)

The blind estimation of mixing channels resulting from frequency selective fading and multipath in a multi-user CDMA system is an important problem in wireless communications. We present a novel frequency-domain approach using second order spectral statistics for recovering the unknown channels. Unlike other methods which are based on time-domain analysis we make no particular assumption about the support of the mixing channels except that they have finite length (FIR). The method is based on the fact that the source sequences obtain a known spectral color derived from the corresponding spreading code used in CDMA.

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Last Update: February 4, 1999 Ingo Höntsch