Adaptive filter

About: Adaptive filter is a research topic. Over the lifetime, 36472 publications have been published within this topic receiving 623734 citations.

The FIR window function design based on evolutionary algorithm

Abstract: This paper improved the function of the FIR window based on evolutionary algorithms. This window function is different from the Han window, Hamming window and Blackman window. Firstly it combines cosine series to window function linearly. Then it makes linear programming on conditions that digital Filter should meet according to characteristics of FIR digital filter and adjusts window function weights according to changes of the signal to improve the algorithm. Advantage of this method is that it can select a more appropriate window function according to characteristics of filter. The simulation results show that the filter designed in this method is more intelligent.

Application of the on‐line recursive least‐squares method to perform structural damage assessment

Abstract: The identification of structural damage is an important objective of health monitoring for civil infrastructure. An on-line recursive least-squares (RLS) identification technique is applied in this study to identify the time-varying dynamic parameters of structures subjected to earthquake loadings. Based on the framework of adaptive filters, the observations are obtained sequentially in real time. It is desirable to perform the identification tasks recursively to save computation time and to be able to observe the variations of parameters on-line. Computation of the classical least-squares (LS) method can be arranged recursively so that the estimated parameters at previous step can be used to predict the responses at current time. The one-step ahead predicted error between estimated response and measured response is calculated by the on-line RLS method and the dynamic properties of system can be identified as well. The purpose of this study is to apply the RLS method to verify the identification procedures and to perform the damage assessment on a three-floor shaking table benchmark model tested at NCREE in Taiwan. Furthermore, both classical LS and RLS methods are implemented to investigate the recorded strong-motion data of Tai-Tung Fire Bureau Building located at Tai-Tung City in Taiwan, which had been demolished due to severe damage after a magnitude 6.2 earthquake in 2006. By observing the variations of the identified time-varying modal properties of both benchmark model and real building, global damage behavior due to weak element or failure of components can be revealed. Copyright © 2009 John Wiley & Sons, Ltd.

Improved Adaptive Smoothing Filter for Indoor Localization Using RSSI

Abstract: In the indoor location estimation system, which has recently been actively studied, the received signal strength indicator contains a high level of noise when measuring the signal strength in the range between two nodes consisting of a receiver and a transceiver. To minimize the noise level, this paper proposes an improved adaptive smoothing filter that provides different exponential weights to the current value and previous averaged one of the data that were obtained from the nodes, because the characteristic signal attenuation of the received signal strength indicator generally has a log distribution. The proposed method can effectively decrease the noise level by using a feedback filter that can provide different weights according to the noise level of the obtained data and thus increase the accuracy in the distance and location without an additional filter such as the link quality indicator, which can verify the communication quality state to decrease the range errors in the indoor location recognition using ZigBee based on IEEE 802.15.4. For verifying the performance of the proposed improved adaptive smoothing filter, actual experiments are conducted in three indoor locations of different spatial sections. From the experimental results, it is verified that the proposed technique is superior to other techniques in range measurement.

Adaptive Detection of Islanding and Power Quality Disturbances in a Grid-Integrated Photovoltaic System

Abstract: Global photovoltaic (PV) generation is increasing steadily at about $$30\%$$ growth rate over the last decade. Depleting environment owing to extensive use of fossil fuels is expected to further continue with this growth rate. With such large PV penetration in the utility grid, perturbation-based active islanding detection methods are becoming detrimental, marred with issues like degradation of power quality and deteriorating system stability. This paper uses morphological filters combined with empirical mode decomposition (EMD) to implement an efficient adaptive signal processing-based detection for islanding as well as PQ disturbances. Two-stage morphological median filter (MMF-2) is used to overcome the noise vulnerability associated with EMD. Field programmable gate array implementation is developed for real-time detection of PQ events. Classification of power quality disturbances is obtained using a support vector machine classifier. The results demonstrate fast and accurate real-time detection under various noisy scenarios without applying any parameter perturbations.