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Sensors 2018, 18(2), 568; https://doi.org/10.3390/s18020568

Polynomial Phase Estimation Based on Adaptive Short-Time Fourier Transform

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
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Received: 2 January 2018 / Revised: 10 February 2018 / Accepted: 10 February 2018 / Published: 13 February 2018
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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Abstract

Polynomial phase signals (PPSs) have numerous applications in many fields including radar, sonar, geophysics, and radio communication systems. Therefore, estimation of PPS coefficients is very important. In this paper, a novel approach for PPS parameters estimation based on adaptive short-time Fourier transform (ASTFT), called the PPS-ASTFT estimator, is proposed. Using the PPS-ASTFT estimator, both one-dimensional and multi-dimensional searches and error propagation problems, which widely exist in PPSs field, are avoided. In the proposed algorithm, the instantaneous frequency (IF) is estimated by S-transform (ST), which can preserve information on signal phase and provide a variable resolution similar to the wavelet transform (WT). The width of the ASTFT analysis window is equal to the local stationary length, which is measured by the instantaneous frequency gradient (IFG). The IFG is calculated by the principal component analysis (PCA), which is robust to the noise. Moreover, to improve estimation accuracy, a refinement strategy is presented to estimate signal parameters. Since the PPS-ASTFT avoids parameter search, the proposed algorithm can be computed in a reasonable amount of time. The estimation performance, computational cost, and implementation of the PPS-ASTFT are also analyzed. The conducted numerical simulations support our theoretical results and demonstrate an excellent statistical performance of the proposed algorithm. View Full-Text
Keywords: adaptive short-time Fourier transform; instantaneous frequency gradient estimation; polynomial phase signals; parameters estimation; time–frequency signal analysis adaptive short-time Fourier transform; instantaneous frequency gradient estimation; polynomial phase signals; parameters estimation; time–frequency signal analysis
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Jing, F.; Zhang, C.; Si, W.; Wang, Y.; Jiao, S. Polynomial Phase Estimation Based on Adaptive Short-Time Fourier Transform. Sensors 2018, 18, 568.

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