Abstract
Parameter estimation of chirp signal, such as instantaneous frequency (IF), instantaneous frequency rate (IFR), and initial phase (IP), arises in many applications of signal processing. During the phase-based parameter estimation, a phase unwrapping process is needed to recover the phase information correctly and impact the estimation performance remarkably. Therefore, we introduce support vector regression (SVR) to predict the variation trend of instantaneous phase and unwrap phases efficiently. Even though with that being the case, errors still exist in phase unwrapping process because of its ambiguous phase characteristic. Furthermore, we propose an SVR-based joint estimation algorithm and make it immune to these error phases by means of setting the SVR's parameters properly. Our results show that, compared with the other three algorithms of chirp signal, not only does the proposed one maintain quality capabilities at low frequencies, but also improves accuracy at high frequencies and decreases the impact with the initial phase.
Highlights
Chirp signals, that is, second-order polynomial phase signals, are common in various areas of science and engineering
The research on estimating these chirp parameters is divided into two parts: one is based on cubic phase function (CPF), even high-order phase function (HPF) [4,5,6] and the other is based on maximum likelihood (ML) [7,8,9]
Phase unwrapping process is a key point in phase-based frequency estimation of chirp signal
Summary
That is, second-order polynomial phase signals, are common in various areas of science and engineering. The research on estimating these chirp parameters is divided into two parts: one is based on cubic phase function (CPF), even high-order phase function (HPF) [4,5,6] and the other is based on maximum likelihood (ML) [7,8,9] The former has the advantage in fast calculation of IFR, but costs more to find IF. By extending the phase noise model of [11] to chirp signals, Li et al derived an improved ML estimator and analyzed its performance in the time domain [12, 13]. It has been shown that, except for the property of low sensitivity to initial phase and closely approaching the Cramer-Rao lower bound (CRLB) at low frequencies, the proposed algorithm improves its estimation performance at high frequencies
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