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Abstract
Noise-interference suppression and data-processing acceleration are crucial to aeroacoustic measurements with phased array in wind tunnels. In this paper, we develop a "multi-window" beamforming algorithm that recursively processes data based on an array-acquisition model. This spatial filtering algorithm is derived from the Kalman filter theory for signal processing. The simulated results show that by using recursive operations, accurate signal estimation is acquired with incoherent and coherent background noise removed in the presence of both channel noise and phase noise. The convergence rate of this recursive algorithm is faster than the existing algorithms. As a result, considerable storage space and computational resources are saved, while testing defects in wind tunnel measurement are revealed and corrected immediately on-site. It has a great potential for real-time localization of sound sources in a noisy environment.
Highlights
The unwanted noise generated from an aircraft is partially or completely due to the interactions between moving bodies and nearby turbulence flows (Lighthill, 1952)
Welch’s method (Welch, 1967), can be carried out with the removal of diagonal elements simultaneously. It can proceed in two steps as follows: First, measure the background noise XB12 including wind tunnel noise XB1 and protruding noise XB2, the corresponding cross spectral matrix (CSM) CB12 is obtained from the output of the acoustic sensors as CS 1⁄4 GhXSX†SiG† % hCB12Si À hCB12 i; (9)
The severe incoherent background noise XB1 at À0:6 < x < À0:2 mainly comes from a noisy wind tunnel fan that produces the testing flow of a free stream at 30 m/s
Summary
The unwanted noise generated from an aircraft is partially or completely due to the interactions between moving bodies and nearby turbulence flows (Lighthill, 1952). A method combining state observer with extended Kalman filter (OBS-EKF) was developed for coherent background noise removal (Bai and Huang, 2011). It did not consider channel noise and phase noise, and converges slowly with manually-set parameters (Johansson and Medvedev, 2003). An intelligent algorithm combining state observer and strong tracking filter (OBSSTF) was proposed by the present authors (Ji et al, 2019) It greatly increases the convergence speed of removing coherent background noise.
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