Blind estimation method for underdetermined hybrid frequency hopping parameters based on time frequency diagram correction

Abstract

The invention discloses a blind estimation method for underdetermined hybrid frequency hopping parameters based on time frequency diagram correction in order to mainly solve the problem of large estimation errors caused by a poor signal to noise ratio in the prior art. The method comprises the following implementation steps: (1) performing short-time Fourier transformation on a signal to obtain a time frequency matrix of an observed signal; (2) calculating an energy matrix of the time frequency matrix; (3) correcting the time frequency diagram; (4) estimating frequency hopping signal parameters according to the corrected time frequency diagram. The method disclosed by the invention can be used for obtaining a clear time frequency diagram at a low signal to noise ratio, has the advantage of improving the estimation precision of the frequency hopping signal parameters, and can be applied to various frequency hopping communication scenes.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to an underdetermined mixed frequency hopping parameter blind estimation method, which can be used in the fields of frequency hopping communication reconnaissance, non-cooperative frequency hopping communication, military communication and the like. Background technique [0002] In recent years, frequency hopping communication technology has been widely used in various communication scenarios due to its strong anti-interference ability, good confidentiality and easy networking. One of the main tasks of researching frequency hopping communication technology is to estimate the characteristic parameters of frequency hopping signals. [0003] At present, the method based on time-frequency analysis is a commonly used method for blind estimation of frequency-hopping signal parameters. This type of method first uses some time-frequency analysis tools such as short-time Fourier change WVD, ...

AI Technical Summary

Problems solved by technology

This type of method has a huge time-frequency atomic dictionary and high algorithm complexity

At present, most algorithms are only suitable for the parameter estimation of a single frequency hopping signal. The characteristic matrix joint approximate optimization algorithm can separate multiple frequency hopping signals, and then use the time-frequency analysis method to estimate the parameters of multiple frequency hopping signals. But this method is only suitable for overdetermined or positive definite situations, and the performance is poor when the signal-to-noise ratio is low

For parameter estimation of frequency hopping signals in underdetermined situations, although the method of modifying the time-frequency diagram based on time-frequency sparsity can be applied, the selection of key parameters of this method is not adaptive and is greatly affected by the signal-to-noise ratio. Parameter estimation error is large

Images