Study On Adaptive Constant Falase Alarm Rate Detection Technology In Complex Environment

Constant false alarm rate(CFAR)processing,as an important technical means to realize automatic target detection in radar system,nowadays has become an indispensable part in the area of radar signal processing.With the rapid development and application of military and civil radio technology,the electromagnetic environment of radar becomes more and more complex,so the performance requirements of target detection means are constantly improving.Many kinds of CFAR processing algorithms,which were earliest proposed,can’t keep good detection performance in complex background,for lack of adaptive abilities.Therefore,how to calculate the detection threshold adaptively according to the samples’ characteristics of reference cells has become the main research direction of CFAR algorithm.This thesis mainly studies on the adaptive CFAR algorithm in complex background.This thesis firstly derives and analyzes the fixed threshold detection,the method of cell average CFAR(CA-CFAR),greatest of CFAR(GO-CFAR),smallest of CFAR(SO-CFAR)and ordered statistic CFAR(OS-CFAR),Lays the foundation for the adaptive CFAR technology described later.Secondly in this thesis the heterogeneous clutter estimation CFAR(HCE-CFAR)algorithm and the variability index CFAR(VI-CFAR)algorithm,which are two common adaptive CFAR algorithms,are theoretically deduced and analyzed.Combined with simulation Simulation results in different environments,the detection performance of these two algorithms is illustrated.Besides,this thesis briefly introduces the principle of the algorithm of excision CFAR(E-CFAR),adaptive length CFAR(AL-CFAR)and trimmed mean CFAR(TM-CFAR),their advantages are also mentioned in short.Aiming to deal with the problem that the detection performance of VI-CFAR degrades in complex multi-target environment,an improved algorithm is proposed by combining VICFAR and the HCE-CFAR.The new algorithm successfully realizes the improved HCECFAR and E-CFAR applied to VI-CFAR without adding too much extra computation.The simulation results show that the improved algorithm can improve the detection performance in multi-target environment to a great extent while the deterioration of the performance in nonhomogeneous environment and clutter edge environment.Finally,this thesis proposes another improved VI-CFAR algorithm,which combines VICFAR and AL-CFAR,adopts a multi-level process of two-level variability index decision and one-level mean ratio decision,and adaptively selects the reference cells to caculate the detection threshold,so as to further improve the detection performance of common VICFAR in multi-objective environment.The core algorithm of the approach is CA-CFAR and GO-CFAR while the auxiliary means is statistics decision.By using multi-level decision and processing the samples of the nonhomogeneous.complex environment and homogeneous enviroment,this approach retains the sampling value belonging to the homogeneous environment in the reference windows as mcuh as possible,and adaptively selects the calculation means and parameters of the detection threshold.The simulation results show that this proposed CFAR method has better detection performance in homogeneous environment than common VI-CFAR,the false alarm rate control in clutter edge environment is in the same order of magnitude as common VI-CFAR,and the detection performance in complex multi-target environment is significantly enhanced,which proves that the proposed algorithm can effectively improve the target detection performance in complex scenes and has strong robustness in complex background.