Study On Clutter And Jamming Suppression Methods For Airborne Radar With Conformal Array

Phased-array radar has the advantages of high reliability,flexible working modes,and is easy for anti-jamming with spatial freedom,which makes it the preferred system for new equipment research and development.Currently,the mainstream airborne phased-arrays are mainly planar arrays,which have disadvantages such as self-weight,limited observation direction,and aerodynamic consistency.It is an important direction for the research and development of early warning aircraft to integrate the fuselage and phased-array antenna and miniaturize the carrier platform to improve the flight performance,detection performance and battlefield survivability of early warning aircraft.Jamming and clutter have been the bottlenecks that limit the detection performance of early warning aircraft.Jamming and clutter are always the bottlenecks of the detection performance of early warning aircraft.Compared with the plane array,the interference and clutter suppression of conformal array is more complex and difficult,which needs to make a profound study in the signal model and processing method.This research has the important theoretical significance and practical value for improving radar performance in complex battlefield environments.Space-time adaptive processing(STAP)technology is a clutter suppression technology for a moving platform radar in complex ground environments.The traditional STAP method requires a large number of independent and identically distributed samples to estimate the clutter and jamming statistical characteristics,and then constructs a space-time domain filter to suppress the clutter and jamming.The curvature of array caused by conformal array makes the clutter of different range gates has different space-time two-dimensional characteristics,which leads to the heterogeneous clutter.Besides,various non-ideal factors in the real environment and the system lead to non-homogeneous clutter which seriously affects the performance of the traditional STAP methods.In order to effectively suppress the clutter and jamming of the airborne conformal array radar,this dissertation studies the sparse reconstructed clutter and jamming covariance matrix,training sample compensation,and non-homogeneous sample selection,as follows:1.The STAP method for estimating the covariance matrix of clutter based on the sparse reconstruction principle is studied.In general,training samples at different range gates are difficult to meet the conditions of independent and identical distribution.It is worthwhile to reconstruct the clutter covariance matrix of the cell under test using only single snapshot data or fewer snapshot data.In Chapter 3,a method of estimating the clutter covariance matrix using knowledge aided semiparametric/sparse iterative covariance-based estimation(KASPICE)method is proposed after constructing the clutter space-time steering vector dictionary matrix by prior knowledge.Compared with the traditional sparse reconstruction STAP method,the proposed method has the advantages of not needing to estimate hyperparameters,low computational complexity,and obtaining analytical solutions.Simulation data processing experiments show that the proposed method can obtain good clutter suppression performance using only the data of the cell under test.2.Research on jamming suppression method of airborne conformal radar.The conformal array will cause the clutter to be heterogeneous,but the jamming will not change with the carrier formation.Therefore,the conventional STAP method is difficult to effectively suppress the jamming while suppressing the conformal array clutter.In chapter 4,the characteristics of airborne conformal array radar jamming are analyzed in detail,and a sparse reconstruction STAP method that can suppress clutter and jamming at the same time is proposed.The jamming has the same angle in each Doppler bin.Using this feature the proposed method selects the azimuth-elevation plane corresponding to the Doppler bin in the clear area to estimate the azimuth and elevation of the jamming.Then,with the prior information of system parameters,the proposed method can construct a dictionary matrix containing clutter and jamming space-time steering vectors.After estimating the power matrix corresponding to the dictionary matrix,the clutter plus jamming covariance matrix can be reconstructed.The results of simulation data prove that the proposed method can well estimate the angle of interference and can effectively suppress the clutter and interference of airborne conformal radar.3.The adaptive training sample compensation algorithm based on a dynamic dictionary matrix is studied.In order to reduce the heterogeneous clutter caused by conformal array or nonside-looking array,some scholars have proposed the training sample compensation algorithms which compensate the clutter spectrum center of the training sample to the clutter spectrum center of the cell under test.This kind of algorithm generally needs accurate prior information to estimate the clutter spectrum center of each range gate.However,in the real environment,various non-ideal factors lead to the inaccuracy of prior information.In Chapter 5,an adaptive clutter spectrum center estimation method based on the dynamic dictionary matrix is proposed.After the global mesh is divided,the method performs local mesh division based on the position corresponding to the maximum power point.At the same time,the base dictionary matrix is adopted to ensure the accuracy of power estimation.Simulation experiments show that the proposed method can accurately estimate the clutter spectrum center of each range gate with the less computational load.4.The method of sample selection and clutter covariance matrix reconstruction in nonhomogeneous clutter environment is studied.Non-homogeneous clutter caused by various non-ideal factors,in reality,will seriously affect clutter suppression performance.Selecting suitable training samples from a limited number of samples to reconstruct an accurate clutter covariance matrix can effectively improve performance of the STAP method to suppress clutter.In Chapter 6,a knowledge-aided Mahalanobis distance metric learning STAP method is proposed.This method uses the prior terrain and radar system parameters to construct the interference covariance matrix and echo data of each range gate.Then it combines the learning ideas of the Mahalanobis distance metric to determine whether the target may exist in each range gate,and make samples in a different group.Samples without target after grouping are used as training samples,and the similarity between the training samples and the data of the cell under test is calculated.The training samples with higher similarity are selected and weighted to construct the clutter plus noise covariance matrix of the cell under test.The measured data processing experiments show that the proposed method can more effectively select training samples with high similarity to the cell under test,thereby improving the clutter suppression performance.