Research On Parameter Estimation Of Reentry Target Based On Nonlinear Filtering

In modern warfare,ballistic missiles are undoubtedly one of the most deterrent weapons.With the development of ballistic missiles in many countries,the task of defense interception is becoming more and more important.The defense system of ballistic missile plays an important role in the field of national defense.In the whole defense process,the early warning detection of the target is a key link.The success or failure of the subsequent interception is determined by the successful identification of the ballistic targets in the space.Therefore,it is of great significance to identify the target’s characteristic parameters in the middle and reentry stage of the ballistic missile.In this paper,we mainly use nonlinear filtering technology to achieve the estimation of the mass resistance ratio of the reentry target and the extraction of the micro Doppler frequency of the middle target.The main contents of the paper are summarized as follows:1.A motion model of reentry target in reentry stage is established.Several coordinate systems commonly used in target tracking and their mutual transformation are introduced.On this basis,the state transition model and measurement model of reentry target are constructed by using dynamic knowledge.The concept of mass resistance ratio is expounded,and its estimation method is introduced.The difference of motion characteristics of targets with different mass resistance ratios is analyzed.Combined with the radar measurement error model,the influence of error on the moving state of the target is analyzed.2.Two nonlinear filtering methods under the Calman filtering framework are studied: the extended Calman filtering algorithm(EKF)and the unscented Calman filtering algorithm(UKF).The two methods are applied to the estimation of the mass resistance ratio of the reentry target,and the convergence heights of the targets with different mass drag ratios are analyzed.In order to solve the problem that the precision of EKF and UKF is not high,and the convergence speed is not fast enough,a method based on the particle filter(UPF)is proposed to estimate the mass resistance ratio.By improving the method of weight calculation in UPF,the problem of numerical sensitivity in recursive estimation is eliminated.The effect of three nonlinear filtering methods on the ratio of mass to resistance is contrasted respectively.It is concluded that UPF performs better in the face of strong nonlinear problems.3.Taking the spatial cone as the goal,the fretting characteristics of spin and precession are analyzed.The common method of time frequency analysis is introduced.A method of fretting Doppler frequency extraction based on particle filter is studied.First,a specific state transition model is established.It is concluded that high order particle filter needs to be used for estimation because of the existence of high order states.According to the measurement model,a state update model is established by using the idea of detection before tracking and using quasi likelihood function instead of quasi function.Finally,the implementation steps are given,and the extraction of micro Doppler frequency is completed.