A Time-Frequency Transformation Method of Polynomial Phase Signal Based on Particle Swarm Optimization

Abstract

The invention provides a polynomial phase signal time-frequency transform method based on particle swarm optimization which is capable of performing time-frequency decomposition on polynomial phase signals; each signal component acquired from the decomposition is a single component corresponding to only one frequency point at any moment; the signal components and instantaneous frequency values ofall moments are subjected to direct calculation by reserving only main-lobe-responsive Sinc function so as to generate a signal frequency distribution of each corresponding moment; the defect is overcome that non-single components with one moment corresponding to multiple frequency points in traditional time-frequency transform have cross components; a time-frequency distribution with no cross-component interference and good time-frequency joint resolution is finally output. The polynomial phase signal time-frequency transform method based on particle swarm optimization has the advantages thatthe principle is simple, operating is convenient, the adverse influence of cross-component disturbances from the conventional time-frequency analysis methods and the loss of time-frequency joint resolution can be effectively overcome, and the quality and benefit of nonstationary polynomial phase signal time-frequency analysis can be effectively improved.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to a polynomial phase signal adaptive time-frequency transformation method based on particle swarm optimization. Background technique [0002] Many natural and artificial signals, such as speech, biomedical signals, waves propagating in dispersive media, mechanical vibrations, animal sounds, music, radar, sonar signals, etc., are typical non-stationary signals, which are characterized by continuous Time is limited, and the frequency is time-varying, non-stationary, nonlinear, non-uniform, non-structural, non-deterministic, non-integrable, non-reversible, amorphous, irregular, non-continuous, non-smooth, non-periodic, non-smooth symmetrical features. Joint time-frequency analysis (joint time-frequency analysis, referred to as time-frequency analysis) focuses on the time-varying characteristics of real signal components, and expresses a one-dimensional time signal in the form of a tw...

AI Technical Summary

Problems solved by technology

[0018] However, the existing time-frequency analysis methods are still difficult to fully apply to polynomial phase signals whose phase modulation can be expressed as a finite term polynomial series

[0019] The patent "A Model-Driven Polynomial Phase Signal Adaptive Time-Frequency Transformation Method" proposes a feasible new idea and method for the above-mentioned practical problems, but does not specifically give the specific practicality of various modern optimization algorithms. Operation and Calculation Process

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