Research On The Estimation Of Frequency-hopping Signals Based On Adaptive Sparse Algorithm

The way to estimate the parameter of the frequency hopping signals has become an significant research issue in the domain of communication reconnaissance.In order to analyze and estimation the parameters of hopping frequency signals effectively,the paper studies a few typical spectral estimation methods,i.e.Short Time Fourier Trransform?STFT??Adaptive Optimal-Kernel Time-Frequency Representation?AOK-TFR?,Iterative Adaptive Approach?IAA?,Sparse Learning via Iterative Minimization?SLIM?,Sparse Iterative Covariance-based Estimation?SPICE?,Likelihood-based Estimation of Sparse Parameters?LIKES?.But in the background of heavy-tailed distributions noise,the IAA?SLIM?LIKES and SPICE spectral estimation methods are not robust,therefore,the paper studies their l₁ norm extension and l_p norm extension,that is l₁-IAA;l_p-IAA;l₁-SLIM;l_p-SLIM;l₁-LIKES;l_p-LIKES;l₁-SPICE;l_p-SPICE.The l₁ norm extension and l_p norm extension of sparse spectral estimation methods can be understood like this:make the nonlinear frequency estimation into a parameters estimation problem of a linear model whose coefficient adjusted to amplitude at the known frequencies.By comparing the analysis of the classical spectrum estimation methods and thel₁ norm extension and l_p norm extension of sparse spectral estimation methods,it can be concluded:the l₁ norm extension and l_p norm extension of sparse spectral estimation methods in the background of impulse noise have a higher accuracy and a better resolution,which is important to analyze the spectral estimation and the detection and estimation of hopping frequency signals.And combined with the cycle diagram in the background of Gaussian noise and the cycle diagram in the background of impulse noise,we will compare each spectral estimation method respectively and finally demonstrate the final simulation results are true through the underwater experimental data.