Parameter Estimation of Hybrid Sinusoidal FM-Polynomial Phase Signal

Abstract

This paper considers parameter estimation of a hybrid sinusoidal frequency modulated (FM) and polynomial phase signal (PPS) from a finite number of samples. We first show limitations of an existing method, the high-order ambiguity function (HAF), and then propose a new method by adopting the high-order phase function which was originally designed for the pure PPS. The proposed method estimates parameters of interest from peak locations in the time-frequency rate domain, which are less perturbed by the noise than peak values used by the HAF-based method. Numerical evaluation shows the proposed method can handle the hybrid FM-PPS signal with low sinusoidal frequency and improve estimation accuracy in terms of mean squared error for several orders of magnitude

Parameter estimation of frequency shift keying radar signal intercepted by Nyquist folding receiver using periodic linear frequency modulation local oscillator

“…Presently, the main existing methods of NZ index estimation are the multiple channel method [12] and the matching component function (MCF) [13,18]. In addition, the local high-order phase function (LHPF) [19] has the optimal transformation kernel for SFM component, and it can be used to estimate the signal that contains SFM part as well. Thus, focusing on the FSK signal intercepted by the NYFR using SFM LO, the three methods are investigated.…”

Parameter estimation for chirp signals using the spectrum phase

“…second‐order polynomial phase signals (PPSs), arise in many signal processing applications. The most notable applications are in radar, sonar, and wireless communications [1–5]. In recent years, a variety of algorithms have been proposed to estimate the chirp parameters.…”

Hardware implementation of the quasi‐maximum likelihood estimator core for polynomial phase signals

“…• Maximum likelihood (ML) estimators (accurate but with unacceptable complexity for PPS of order higher than 3); • Phase unwrapping (PU) estimators [accurate only for narrowband signals and high signal-to-noise ratio (SNR) but inaccurate for wideband signals and/or more emphatic noise] [3]; • Phase differentiation (PD) estimators (efficient techniques but with limited accuracy especially for higher-order PPSs) [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

“…This model has been studied for more than three decades with numerous techniques designed for parameter estimation. Three main groups of the parameter estimators are [1, 2]: Maximum likelihood (ML) estimators (accurate but with unacceptable complexity for PPS of order higher than 3); Phase unwrapping (PU) estimators [accurate only for narrowband signals and high signal‐to‐noise ratio (SNR) but inaccurate for wideband signals and/or more emphatic noise] [3]; Phase differentiation (PD) estimators (efficient techniques but with limited accuracy especially for higher‐order PPSs) [4–21]. …”

“…Phase differentiation (PD) estimators (efficient techniques but with limited accuracy especially for higher‐order PPSs) [4–21].…”