A Model-Driven Adaptive Time-Frequency Transformation Method for Polynomial Phase Signals

Abstract

The invention provides a model-driven adaptive time-frequency transformation method for polynomial phase signals. Time-frequency decomposition of the polynomial phase signals can be completed. Each signal component obtained by decomposition is a single component corresponding to only one frequency point at any time, and then signal frequency distribution at a corresponding moment is generated by direct calculation on the basis of an instantaneous frequency value of each signal component at each moment and a Sinc function only with a main lobe reserved. The defect of existence of cross terms due to corresponding non-single components of multiple frequency points at a moment in traditional time frequency transformation is overcome, and time frequency distribution with better time frequency joint resolution and without any cross term interference is output finally. The method has the advantages that the principle is simple, operation is convenient, the adverse influence of cross term interference and the loss of time-frequency joint resolution of the classical time frequency analysis method can be effectively overcome, and the quality and benefit of time frequency analysis of the non-stationary polynomial phase signals can be effectively improved.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to a model-driven polynomial phase signal adaptive time-frequency transformation method. 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 two-dimensional time-frequ...

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

To sum up, the sequential processing methods of multidimensional search for dimensionality reduction all have more or less error transfer effects, and each has its own characteristics or shortcomings: the PPT method is fast and simple, but there is a high signal-to-noise ratio threshold. In the case of the above low SNR, the estimation performance needs to be improved, and there may be recognition problems in the multi-component case; ML-HAF, PHAF, IGAF, etc. can effectively solve the recognition problem in multi-signal processing, but the amount of calculation is generally relatively large. Large; the cubic phase function method has a low SNR threshold, and has a strong processing capability for low SNR, but has a large amount of calculation, and there are identification problems in the case of multiple signals

In addition, the method based on time-frequency analysis has a large amount of calculation, and there are also contradictions between time-frequency aggregation, cross-terms between multiple signals, and kernel function design. For high-order phase signals, there are also interference items in the signal.

The adaptive kernel function design method that makes full use of signal characteristics is a hot spot, but at this stage it is also facing the problems of high difficulty and large amount of calculation

[0019] At the same time, the time-frequency decomposition method applied to polynomial phase signals not only requires good time-frequency joint resolution, but also requires little or no cross-term interference; the existing processing technology is reducing cross-term and maintaining high time-frequency resolution. Insufficient in terms of capabilities, how to reduce cross-term interference and improve the time-frequency joint resolution ability has become a major practical problem in the time-frequency decomposition and time-frequency analysis of polynomial phase signals

Images