Research On Interference Suppression Algorithm For Sky-Wave Radar

The sky-wave radar exploits the ionospheric's reflection of electromagnetic waves to detect targets.It breaks through the limitation of the detection blind zone caused by the curvature of the earth and can achieve long-range target detection of1000 4000 km.With this advantage,the sky-wave radar has been widely used and researched in the fields of early warning,remote alert,and sea surface monitoring.Although the sky-wave radar has many advantages,there are still many challenges in sky-wave radar interference suppression due to the complex signal transmission environment of the sky-wave radar.This thesis focuses on the problem of radio frequency interference and clutter interference suppression in the sky-wave radar.The main work is summarized as follows:1.Aiming at the problem of radio frequency interference suppression in the sky-wave radar,a method of radio frequency interference suppression based on sparse similarity constraints is proposed.The proposed method fully exploits the sparsity of the filter to improve the performance of radio-frequency interference suppression.By analyzing the principle of the the similar constraint method,it is found that the essence of radio frequency interference suppression filter based on similar constraints is to add a narrowband filter to the matched filter to achieve both matched filtering and radio frequency interference suppression.However,the narrowband filter is sparse.The proposed method exploits the sparsity of the filter,maximizes the output signal to interference ratio and sparse the narrowband filter under similar constraints,which improves the performance of radio-frequency interference suppression.2.A fined weighting space-time adaptive clutter suppression method is proposed for the problem of non-homogeneous clutter environment.The proposed method fully exploits the clutter ridge area data of non-homogeneous samples to solve the problem of non-homogeneous clutter environment.By analyzing the principle of the traditional weighting method,it is found that in the process of weighting the sample,the traditional weighting method will lose the clutter ridge region data of non-homogeneous samples.The proposed method sets weighting coefficients on distance and angle-Doppler planes in the process of reconstructing the clutter covariance matrix to fully exploit the clutter ridge area data of non-homogeneous samples,which improve the data utilization and improve the clutter suppression performance under non-homogeneous clutter environment.3.To solve the problem of traing sample shortage,a sparse recovery space-time adaptive clutter suppression method in direct data domain is proposed.The proposed method only exploits the cell under test to recover the clutter power spectrum and fundamentally solves the problem of sample shortage.The proposed method recovers the clutter space-time power spectrum by sparsely iterating the covariance matrix of the cell under test,and then reconstructthe clutter covariance matrix of the cell under test,which avoid exploitng training samples.The methods proposed in this thesis have been verified by measured data.The results show that the proposed methods can effectively suppress corresponding interference.