Study On STAP Methods For Airborne Radar In Heterogeneous Environment

When airborne radar works,ground clutter is an important factor affecting target detection.The space time adaptive processing(STAP)utilizes the space-time coupling characteristics of ground clutter to form a notch that matches the clutter ridge,and effectively suppresses the clutter while maintaining the target gain as much as possible.However,STAP requires enough uniform samples of independent and identical distributed(IID)to estimate the clutter characteristics,but in reality clutter is often heterogeneous,which limits the number of uniform samples that STAP can obtain,seriously affects the accuracy of covariance matrix estimation,and often leads to a significant decrease in clutter suppression performance.Both oblique side arrays and terrain undulations are common causes of clutter heterogeneous,so it is of great significance to study the clutter heterogeneous caused by oblique side arrays and terrain undulations.This thesis focuses on improving the clutter suppression performance of STAP in a heterogeneous environment.Aiming at the problem of clutter heterogeneity caused by short-range clutter in oblique side arrays,a new covariance estimation method combining equal Doppler samples and equal conical Angle samples and a clutter partition suppression method are proposed.Aiming at the problem of heterogeneous clutter caused by the mountainous environment with rapidly undulating terrain,an iterative adaptive STAP method for dimensionality reduction in the Doppler domain with less computation in the case of a single snapshot is proposed.The main contents of the thesis are summarized as follows:1.The problem of clutter suppression of oblique side arrays is studied.Echoes from a single range unit at medium and high repetition rates include both long-range clutter and short-range clutter.In order to better estimate the characteristics of each clutter component,a new method of covariance matrix estimation is proposed according to the characteristics that the long-range clutter with the same cone Angle is linear distribution in the range Doppler plane and the short-range clutter is curving distribution.The method not only takes samples along the Doppler axis direction,but also takes samples along the clutter distribution curve of equal cone angle,so that the training samples include samples with close distribution characteristics of long-range clutter and short-range clutter.The simulation results show that compared with the commonly used non-uniform segmentation method,the proposed method significantly improves the clutter suppression effect in the short-range clutter sidelobe region.In order to obtain the optimal clutter suppression effect in the whole range Doppler plane,a clutter partition suppression method is proposed in this thesis.The method divides the entire range Doppler plane into five regions: high-space wide region,strong heterogeneous clutter region,heterogeneous clutter region,uniform clutter sidelobe region,and uniform clutter mainlobe region.Different sample selection methods are used for each region to estimate the covariance matrix and perform space-time adaptive processing.The simulation results show that the method can obtain better clutter suppression effect in the whole range Doppler plane.2.The problem of clutter suppression in mountainous areas with rapidly undulating terrain is studied.Compared with the ideal flat terrain environment,in the mountainous environment,the clutter dynamic range is large,and the clutter ridges in the space-time two-dimensional plane are prone to discontinuity and offset,and change rapidly with distance,making the situation of insufficient uniform samples more serious.With its advantage of high-resolution spectral estimation performance in the case of a single snapshot,the iterative adaptive approach(IAA)can theoretically solve the above-mentioned problem of insufficient uniform samples.However,when the degree of freedom of the system is high,the IAA divides a large number of grids on the space-time two-dimensional plane,resulting in high complexity of power spectrum estimation.To solve this problem,this thesis proposes an iterative adaptive STAP method for dimensionality reduction in the Doppler domain.This method performs Doppler filtering on echo data,uses Doppler filtering to localize the clutter,and confines the clutter to a small spatial domain.For each Doppler channel,use IAA to estimate the power spectrum only in the spatial domain,reconstruct the covariance matrix,and perform space-time adaptive processing.The simulation results show that the proposed method can achieve better clutter suppression effect and significantly reduce the amount of computation when the number of pulses is large.