Knowledge-Based Clutter Suppression Methods For Airborne Radar

Clutter and jamming suppression is the primary problem of airborne radar's target detection.Algorithm of space-time adaptive processing(STAP)can suppress the clutter in both space domain and time domain,which can considerably improve the moving target detection performance of airborne radar.Since heterogeneous clutter environment and the range dependence of short-range are frequently encountered in real applications,the traditional STAP algorithm which uses training samples beside the cell under test(CUT)to estimate covariance matrix do not apply.How to estimate the clutter covariance matrix accurately and efficiently is the crux of further enhancing the moving target detection performance.This work studies the STAP algorithm in heterogeneous interference environments,analyses the necessity of training samples selection in such environments.The existing samples selection algorithms are modeled and simulated with their actual performance presented by measured data.A new adaptive samples selection method is proposed based on the previous algorithm.The new method have a better selection performance when the system's degrees of freedom(DoF)is small.The main contents comprise the following.1.The second chapter presents the clutter model of airborne radar and the basic theory of STAP algorithm.The necessity of dimension reduction algorithm is discussed after introducing fully STAP algorithm.Give some explanation of famous descending dimension STAP algorithm.Parameters of STAP algorithm performance evaluation are also presented.2.The third chapter gives the simulation of airborne radar's clutter signals in heterogeneous interference environments.Show the differences between the minimum variance spectrum and Fourier spectrum.Illuminate covariance matrix estimation method in heterogeneous interference environments and the basic principle of knowledge-aided space-time adaptive processing(KA-STAP)filter.3.The Fourth chapter studies two samples selection methods that are inner products algorithm(GIP)and Fourier spectral similarity(FSPS)samples selection algorithm.Discuss the algorithms' applicability in different heterogeneous interference environments with simulation analysis.The simulation results show the superiority of GIP algorithm when the environment has little heterogeneous samples while show the inferiority of when the environment is strong heterogeneous.FSPS algorithm show the robustness in the high degree of environment' heterogeneity.Show the effectiveness of both two algorithms by processing measured data.4.The existing training samples selection algorithm based on Fourier Spectral Similarity(FSPS)cannot meet the resolution requirement in both the target signal contaminated samples discarding step and the similar samples selecting step when the system's degrees of freedom(DoF)is small.To mitigate this problem,this paper proposes a training samples selection algorithm based on sparse recovery technology.Compared with FSPS,the proposed method is able to improve the performance of both the contaminated samples discarding step and the analogous samples selecting step under a small system's DoF situation.Simulation results verify the effectiveness of the proposed method.