Research On Clutter Suppression Technology For Unmanned Airborne Passive Radar

In modern electronic warfare,unmanned airbore passive radar has been widely used because of its excellent anti-reconnaissance and anti-jamming ability,anti-radiation missile ability and anti-stealth ability.Unmanned airbore passive radar not only has the advantages of passive radar,but also has the advantages of small size,flexible combat and wide detection range.In this thesis,the clutter suppression problem of unmanned airbore passive radar is studied.The main work and innovations are as follows:First,in order to solve the problem of channel time selective fading and carrier interference caused by airborne platform motion,the channel estimation algorithm based on base spread model is applied to the reference signal reconstruction of unmanned airbore passive radar,which effectively improves the accuracy of reference signal reconstruction.Secondly,in order to solve the problem of high computational complexity in the noise suppression of Doppler broadening,a fast batch algorithm of frequency domain extension cancellation is proposed.The algorithm is used to improve the extended destructive batch algorithm,and the fast batch processing is realized by using the fast Fourier transform,and the characteristics of the symmetric Toeplitz matrix are used.The computational complexity of the matrix inversion is reduced,the algorithm complexity is reduced,and the real-time processing capability of the algorithm is improved.A variable-step long-distance-Doppler-normalized least-mean-square algorithm is proposed to reduce the steady-state offset error while the convergence speed is guaranteed,so as to improve the clutter suppression capability.Thirdly,in this paper,a modified beam-Doppler domain sparse constraint dimensionality reduction STAP algorithm is proposed,and the quasi-norm constraint of the gradient guidance is introduced on the basis of studying the STAP algorithm of the sparse constraint of the beam-Doppler domain.On the premise of keeping the performance of the algorithm,the improved algorithm simplifies the processing of the algorithm,reduces the algorithm complexity and reduces the required training samples.