Gradient Adaptive Lattice Algorithm Research Articles

Novel Variable Step-Size Gradient Adaptive Lattice Algorithm for Active Noise Control

In this paper, a novel variable step-size filtered-x gradient adaptive lattice (NVSS-FxGAL) algorithm for active noise control system is proposed. The gradient adaptive lattice (GAL) algorithm is capable of controlling the narrow band noise effectively. The GAL algorithm can achieve both fast convergence rate and low steady-state level using the variable step-size. However, it suffers from the convergence performance for varying signal characteristic since the global variable step-size is equally applied to all lattice stages. Therefore, the proposed algorithm guarantees the stable and consistency convergence performance by using the local variable step-size for the suitable each lattice stage. Simulation results confirm that the proposed algorithm can obtain the fast convergence rate and low steady-state level compared to the conventional algorithms.

A variable step-size gradient adaptive lattice algorithm for multiple sinusoidal interference cancelation

Abstract In passive sonar, adaptive algorithms can be used to cancel strong sinusoidal self-interferences. In order to correctly recover low-power target signals during the early stages of processing, these adaptive algorithms must provide fast convergence and, at the same time, narrow notches at the frequencies of the sinusoids. In this respect, the gradient adaptive lattice (GAL) algorithm is a very attractive choice. However, the GAL algorithm with a constant step-size parameter has to compromise between the convergence rate and notch bandwidths. Therefore, in this article, we propose a variable step-size scheme for the GAL algorithm that can achieve both a fast convergence rate and narrow notches. Simulation results demonstrate the efficiency of the proposed algorithm compared to both the conventional GAL algorithm and transversal adaptive filter combined with the variable step-size scheme.

Convergence and bias in the LSG algorithm for adaptive lattice filters

The lattice filter has several desirable characteristics that make it attractive in adaptive applications. The Lattice Stochastic Gradient (also called Gradient Adaptive Lattice algorithm) is popularly used to adapt the lattice filter. However, a theoretical study of the bias in the reflection coefficient and the convergence of the LSG algorithm have not been studied extensively yet. This paper presents some theoretical results on these issues. It is hoped that the results will also present some insights into the factors affecting the convergence of the filter.

Detection of tool wear using gradient adaptive lattice and pattern recognition analysis

A method of recognising tool wear states in a turning operation from the integrated information of cutting force and acoustic emission signals is presented. The approach, which employs gradient adaptive lattice analysis and pattern recognition techniques, is fast and yields accurate recognition of tool wear states in a wide range of cutting conditions. The gradient adaptive lattice algorithm is applied to recursively compute the autoregressive as well as partial correlation coefficients for both signals with high computational efficiency. Those coefficients are chosen to characterise the sensing signals and used as the feature inputs for pattern recognition. A stepwise search procedure was applied to select the most useful features for the recognition process. Both unsupervised learning technique (fuzzy C-means algorithm) and supervised learning technique (linear discriminant analysis) are used in the pattern recognition analysis. Characteristics of each method are discussed and performance in recognising the states of cutting tool (fresh or worn) is evaluated. The results show that this approach was successfully applied to signify a fresh or worn tool state with a high percentage of correct classification (over 90%).