51 research outputs found
Algorithms and structures for long adaptive echo cancellers
The main theme of this thesis is adaptive echo cancellation. Two novel independent
approaches are proposed for the design of long echo cancellers with improved
performance.
In the first approach, we present a novel structure for bulk delay estimation in
long echo cancellers which considerably reduces the amount of excess error. The
miscalculation of the delay between the near-end and the far-end sections is one
of the main causes of this excess error. Two analyses, based on the Least Mean
Squares (LMS) algorithm, are presented where certain shapes for the transitions
between the end of the near-end section and the beginning of the far-end one are
considered. Transient and steady-state behaviours and convergence conditions
for the proposed algorithm are studied. Comparisons between the algorithms
developed for each transition are presented, and the simulation results agree well
with the theoretical derivations.
In the second approach, a generalised performance index is proposed for the
design of the echo canceller. The proposed algorithm consists of simultaneously
applying the LMS algorithm to the near-end section and the Least Mean Fourth
(LMF) algorithm to the far-end section of the echo canceller. This combination results
in a substantial improvement of the performance of the proposed scheme over
both the LMS and other algorithms proposed for comparison. In this approach,
the proposed algorithm will be henceforth called the Least Mean Mixed-Norm
(LMMN) algorithm.
The advantages of the LMMN algorithm over previously reported ones are two
folds: it leads to a faster convergence and results in a smaller misadjustment error.
Finally, the convergence properties of the LMMN algorithm are derived and
the simulation results confirm the superior performance of this proposed algorithm
over other well known algorithms
Structure-Based Subspace Method for Multi-Channel Blind System Identification
In this work, a novel subspace-based method for blind identification of
multichannel finite impulse response (FIR) systems is presented. Here, we
exploit directly the impeded Toeplitz channel structure in the signal linear
model to build a quadratic form whose minimization leads to the desired channel
estimation up to a scalar factor. This method can be extended to estimate any
predefined linear structure, e.g. Hankel, that is usually encountered in linear
systems. Simulation findings are provided to highlight the appealing advantages
of the new structure-based subspace (SSS) method over the standard subspace
(SS) method in certain adverse identification scenarii.Comment: 5 pages, Submitted to IEEE Signal Processing Letters, January 201
Convergence and steady-state analysis of the normalized least mean fourth algorithm
The normalized least mean-fourth (NLMF) algorithm is presented in this work and shown to have potentially faster convergence. Unlike the LMF algorithm, the convergence behavior of the NLMF algorithm is independent of the input data correlation statistics. Sufficient conditions for the NLMF algorithm convergence in the mean are obtained and an analysis of the steady-state performance is carried out with a new approach. The latter uses the concept of feedback and bypasses the need for working directly with the weight error covariance matrix. Simulation results obtained in a system identification scenario confirms the theoretical predictions on performance of the NLMF algorithm
Convergence and steady-state analysis of the normalized least mean fourth algorithm
The normalized least mean-fourth (NLMF) algorithm is presented in this work and shown to have potentially faster convergence. Unlike the LMF algorithm, the convergence behavior of the NLMF algorithm is independent of the input data correlation statistics. Sufficient conditions for the NLMF algorithm convergence in the mean are obtained and an analysis of the steady-state performance is carried out with a new approach. The latter uses the concept of feedback and bypasses the need for working directly with the weight error covariance matrix. Simulation results obtained in a system identification scenario confirms the theoretical predictions on performance of the NLMF algorithm
A FAMILY OF NORMALIZED LEAST MEAN FOURTH ALGORITHMS
In this work, a family of normalized least mean fourth algorithms is presented. Unlike the LMF algorithm, the convergence behavior of these algorithms is independent of the input data correlation statistics. The first proposed algorithm uses a simple normalization of the regressor and is called simply the NLMF. The second algorithm consists of a mixed normalized LMF (XE-NLMF) algorithm which is normalized by the mixed signal and error powers. Finally, the third algorithm, called the variable XE-NLMF, is a modified version of the XE-NLMF where the mixed-power parameter is time-varying. An enhancement in performance is obtained through the use of these techniques over the LMF algorithm. Moreover, the simulation results obtained confirm the theoretical predictions on the performance of these normalized LMF algorithms
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