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CPF-RANSAC for estimation of QFM signal parameters
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DOI：10.1016/j.sigpro.2026.110686.png)
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Abstract
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The paper presents new computationally efficient methods of parameter estimation of polynomial-phase signals (PPSs) described by a third order phase polynomial. Such signals are known as quadratic frequency modulated (QFM) signals or cubic phase (CP) signals. The combination of the cubic phase function (CPF) and random sample consensus (RANSAC) approach has been proposed as CPF-RANSC method to improve the efficiency of PPS parameter estimation by rejecting outliers, which occur particularly at low signal-to-noise ratio (SNR). Furthermore, the method was extended by incorporating the least squares (LS) technique, resulting in the CPF-RANSAC-LS algorithm, which yielded additional improvements. The analysis, supported by extensive simulations, proved the superiority of the proposed estimation methods over other known methods of estimation of such signals. The reduction of mean square error (MSE) and the reduction of SNR threshold in the CPF-RANSAC and CPF-RANSAC-LS methods are primarily due to the elimination of outliers through the RANSAC-based approach.
Keywords:
Cubic phase function
RANSAC
Quadratic frequency modulated signals
Parameter estimation
Outlier rejection
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