Yalçın, Nedim AktanVatansever, Fahri2024-10-082024-10-082022-11-300278-081Xhttps://doi.org/10.1007/s00034-022-02245-7https://link.springer.com/article/10.1007/s00034-022-02245-7https://hdl.handle.net/11452/46041Parameter 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.eninfo:eu-repo/semantics/closedAccessPower-systemClassificationDecompositionAlgorithmSignalsHaar transformMusic algorithmFrequency estimationScience & technologyTechnologyEngineering, electrical & electronicEngineeringArticle0009147894000032916294042510.1007/s00034-022-02245-71531-5878