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Haar-MUSIC: A New Hybrid Method for Frequency Estimation

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Abstract

Parameter estimation is very important in signal analysis. In this study, a new hybrid method based on implementation of Multiple Signal Classification (MUSIC) method with Discrete Haar transform (DHT) coefficients for frequency estimation of signals is proposed. This method decreases the input data size and sampling frequency and limits noise subspace correlation matrix according to Nyquist criteria. The realized simulations and real test data show that the proposed method converges to signals’ frequencies faster than the classical MUSIC algorithm and gives accurate results even under high noise.

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Data Availability

In this study, two types of data sets are used. The first one is synthetically created data set and the second one is data set which is obtained from laboratory experiments. Creation of synthetic data set is clarified in Table 1. The dataset which is obtained from laboratory experiment during the current study was recorded by [IEEE 1159.2 Working Group] and available in the repository: [https://web.archive.org/web/20200203211542/https://grouper.ieee.org/groups/1159/2/testwave.html] (wave_14a.xls and wave_15.xls files).

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  1. Electrical-Electronics Engineering Department, Faculty of Engineering, Bursa Uludağ University, Bursa, Turkey

    Nedim Aktan Yalçın & Fahri Vatansever

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  1. Nedim Aktan Yalçın
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Yalçın, N.A., Vatansever, F. Haar-MUSIC: A New Hybrid Method for Frequency Estimation. Circuits Syst Signal Process 42, 2916–2940 (2023). https://doi.org/10.1007/s00034-022-02245-7

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