Repetitive control has proven to be an efficient control technique in power factor correction by active filtering.\ud
Unfortunately, this technique shows a dramatic performance decay when the network frequency is not exactly known or\ud
it varies with time. In order to overcome the varying/uncertain frequency problem, a robust high-order repetitive control\ud
strategy can be used; however, most internal models obtained by these approaches are unstable. Although this fact does not\ud
compromise the closed-loop stability, practical problems can arise during the implementation. This study proposes and studies\ud
a stable second-order odd-harmonic repetitive control system, presents a stability analysis of high-order internal models and\ud
describes the performance degradation of the standard repetitive control in terms of the active filter (AF) application. In\ud
this way, an experimental validation has been carried out implementing the proposed internal model in a shunt AF current\ud
controller. As a result, this high-order controller allows dealing with the grid frequency variations without using adaptive\ud
schemes.Postprint (published version
Abstract-Automatic ranging and self-positioning is a very desirable property in wireless acoustic sensor networks (WASNs) where nodes have at least one microphone and one loudspeaker. However, due to environmental noise, interference and multipath effects, audio-based ranging is a challenging task. This paper presents a fast ranging and positioning strategy that makes use of the correlation properties of pseudo-noise (PN) sequences for estimating simultaneously relative time-of-arrivals (TOAs) from multiple acoustic nodes. To this end, a proper test signal design adapted to the acoustic node transducers is proposed. In addition, a novel self-interference reduction method and a peak matching algorithm are introduced, allowing for increased accuracy in indoor environments. Synchronization issues are removed by following a BeepBeep strategy, providing range estimates that are converted to absolute node positions by means of multidimensional scaling (MDS). The proposed approach is evaluated both with simulated and real experiments under different acoustical conditions. The results using a real network of smartphones and laptops confirm the validity of the proposed approach, reaching an average ranging accuracy below 1 centimeter.
Parametric methods for modeling the perceptually relevant features of head-related transfer functions (HRTFs) are very important for the development of low-cost immersive sound applications. This letter describes an efficient method based on a low-order infinite impulse response filter implemented by a chain of second order sections of conventional shelving and peak audio filters. The parameters (central frequency, gain, and quality factor) are numerically adjusted by iteratively fitting the frequency response of the filter to the desired HRTF. Besides allowing for low-order binaural models, the proposed approach provides an efficient way to synthesize HRTFs for non-measured angles by applying a simple interpolation between the parameters from neighboring responses. Additionally, the HRTF database size is significantly reduced.
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