Method estimating reflection coefficients of adaptive lattice filter and its application to system identification (original) (raw)
Related papers
Convergence properties of an adaptive digital lattice filter
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981
Convergence properties of a continuously adaptive digital lattice filter. used as a linear predictor are investigated for both an unnormalized and a normalized gradient adaptation algorithm. The PARCOR coefficient mean values and the output mean-square error (MSE) are approximated and a simple model is described which approximates these quantities as functions of time. Calculated curves using this model are compared with simulation results. Results obtained for a two-stage lattice are then compared with the two-stage least mean-square (LMS) transversal filter algorithm, demonstrating that it is possible but unlikely for the transversal filter to converge faster than the analogous lattice filter. I.
Analysis and Simulation of System Identification Based on LMS Adaptive Filtering Algorithm
This paper presents Adaptive Filtering and the least mean square algorithm which is widely used in Adaptive system. It realized the model and simulation of system identification Based on LMS algorithm by matlab and simulation. It can be seen that the adaptive FIR filter can simulation the unknown system well. Thus it can be got the system function of the unknown system through the parameters of the adaptive FIR filter and It can be carried out the function of the same hardware reconfiguration of the unknown system.
Frequency tracking performance of adaptive lattice filters
Asilomar Conference on Signals, Systems & Computers, 1991
Adaptive linear prediction filters have been proposed for the detection of narrowband signals in nonstationary noise and for the removal of narrowband interference from spread spectrum communications signals. In the present work, analytical results and computer models for the PARCOR coefficients, signal recovery error, and output misadjustment of the stochastic gradient adaptive lattice filter are obtained for a complex linear
Performance analysis of signed self-orthogonalising adaptive lattice filter
This paper describes the novel signed self-orthogonalizing adaptive lattice filter (SSALF) structure to enhance the slow convergence rate caused by an eigenvalue disparity whilst constraining the level of the convergence rate and the misadjustment required by a specification. The SSALF structure is also implemented by the partial lattice predictor in order to reduce a computational complexity. The performance analysis based on the convergence model of the lattice predictor is given in terms of the mean-squared error and the variance of the reflection coefficient error. Computer simulations are undertaken to verify the performance and the applicability of the proposed filter structure. He has published a book and several papers on the dynamics of neural networks. He has investigated the stability of networks of intelligent agents for British Telecom. His current interest are in models of intelligent agents, where adaptation takes place via neural network learning and evolutionary selection. He is also combining soft computing and economic dynamics.
Comparative Analysis of various Adaptive Filtering Algorithms for Adaptive System Identification
System identification is one of the most interesting applications for adaptive filters, for this dissertation provides a comparison of LMS, VSSLMS,NLMS and TDLMS adaptive algorithms. This process provided the best suitable algorithm for usage in adaptive filters for system identification. This technique Based on the error signal, where filter's coefficients are updated and corrected, in order to adapt, so the output signal has the same values as the reference signal. Its applications include echo cancellation, channel equalization, interference cancellation, and so forth. Simulation results show that the proposed algorithms outperform the standard NLMS and TDLMS algorithms in both convergence rate and steady-state performance for sparse systems identification.
A generalized multichannel least squares lattice algorithm based on sequential processing stages
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1984
A generalized multichannel least squares (LS) lattice algorithm which is appropriate for multichannel adaptive filtering and estimation is presented in this paper. It is shown that a muitichannel LS estimation algorithm with a different number of parameters to be estimated in each channel can be implemented by cascading lattice stages of nondescending dimension to form a generalized lattice structure. A
IEEE Transactions on Signal Processing, 2001
Several algorithms for adaptive IIR filters parameterized in lattice form can be found in the literature. The salient feature of these structures when compared with the direct form is that ensuring stability is extremely easy. On the other hand, while computing the gradient signals that drive the direct form update algorithms is straightforward, it is not so for the lattice algorithms. This has led to simplified lattice algorithms using gradient approximations. Although, in general, these simplified schemes present the same stationary points as the original algorithms, whether this is also true for convergent points has remained an open problem. This also applies to nongradient-based lattice algorithms such as hyperstability based and the Steiglitz-McBride algorithms. Here, we answer this question in the negative, by showing that for several adaptive lattice algorithms, there exist settings in which the stationary point corresponding to identification of the unknown system is not convergent. In addition, new lattice algorithms with improved convergence properties are derived. They are based in the cascade lattice structure, which allows the derivation of sufficient conditions for local stability.
This paper presents a performance analysis of three categories of adaptive filtering algorithms in the application of linear prediction. The classes of algorithms considered are Least-Mean-Square (LMS) based, Recursive Least-Squares (RLS) based and Lattice based adaptive filtering algorithms. The performances of the algorithms in each class are compared in terms of convergence behavior, execution time and filter length. The analysis determines the best converging algorithm from each class. Finally the best performing algorithm for adaptive linear prediction is selected.
System Identification through RLS Adaptive Filters
Ijca Proceedings on National Conference on Innovative Paradigms in Engineering and Technology, 2012
System Identification is one of the most interesting applications for adaptive filters, especially for the Least Mean Square algorithm, due to its robustness and calculus simplicity. Based on the error signal, the filter's coefficients are updated and corrected, in order to adapt, so the output signal has the same values as the reference signal. The application enables remarkable developments and research, creating an opportunity for automation and prediction. In this paper we focus on parameters of system identification by changing design parameters such as forgetting factor, filter length, initial value of filter weight and input variance of filter through MATLAB/SIMULINK Software.