Abstract

Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (RANSAC) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified RANSAC method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).

Highlights

  • In recent years, service robots with artificial intelligence technology have gained wide applications [1]

  • We introduce the RANdom SAmple Consensus (RANSAC) algorithm [33] in order to improve the performance of Direction of Arrival (DOA) estimation algorithm

  • The signals in different signal-to-noise ratio (SNR) are imported into a virtual indoor room to build reverberation. the RoomSim is employed to simulate a virtual room (RT60 = 100 ms), and reverberations in different intensities are generated by changing the reflection coefficients

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Summary

Introduction

Service robots with artificial intelligence technology have gained wide applications [1]. In order to improve the human-computer interaction service experience [2], for example, facing the person who is talking and acquiring the speech and audio signal [3], the humanoid robot is required to possess some form of accurate direction function. Since the speech signal of person is invariably a broadband signal and the room reverberation may pose a serious difficulty, the Direction of Arrival (DOA) estimation has been a very challenging task [4]. Various broadband signal DOA estimation algorithms have been reported, such as Incoherent Signal-subspace

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