Clutter And Jamming Suppression For Airborne Phased Array Radar

Airborne phased array radar receives very strong clutter when it detects moving targets at down-looking,and clutter decreases the performance of target detection.Space-time adaptive processing(STAP)combines space and time to suppress clutter,which improves the performance of target detection of airborne phased array radar.Classical STAP needs training samples to estimate the covariance matrix of the cell under test,and training samples should be independent and identical distributed(?D).To guarantee the performance of STAP,the number of training samples should be more than two times of the degree of the freedom.However,the training samples are not ?D in nonhomogeneous environments,which degrades the performance of STAP.Besides,repeater jamming contaminates the training samples of STAP and degrades the performance of STAP.It not only leads to false alarms,but also raises the threshold of target detection which degrades the probability of target detection.It's very important to effectively suppress clutter and repeater jamming for airborne phased array radar.This paper proposes several solutions to solve the problems above,and contents are as follows.1.Knowledge aided STAP based on road network data.The echo of vehicle from the main lobe may contaminate the training samples of STAP,which results in target self-nulling effect,and therefore degrades the probability of detection.To mitigate this problem,this paper proposes a knowledge aided(KA)STAP which is based on the road network data to select the training samples.This study firstly estimates the radial velocity of vehicle to radar;then the range-Doppler cells which may contain vehicle echo are obtained according to the velocity;in the following,this study distinguish whether the training samples contain vehicle echo according to the matching degree of the training samples with the steering vector of the main lobe and the clutter;finally,the samples containing vehicle echo are discarded when the covariance matrix for the STAP is estimated.Theory analysis and experimental results illustrate that the proposed method advances the output of signal to clutter plus noise ratio,and improves the performance of STAP in road network environments.2.Knowledge aided STAP in heterogeneous environments based on terrain and elevation data.In heterogeneous clutter environments,the training samples used to estimate the clutter covariance matrix for STAP may not share the same distribution with the cell under test(CUT),which leads to performance degradation for clutter suppression.This paper proposes a robust training sample selection method,it selects the samples whose clutter are similar to that of the CUT.Utilizing the terrain data and system parameters,a priori clutter covariance matrices are estimated,which are utilized to weigh the similarities between the CUT and initial training samples.The most similar samples to the CUT are selected to estimate the covariance of the CUT.The proposed method improves the performance of STAP in heterogeneous environments by selecting samples whose clutter are similar to that of the CUT.3.Training samples selection based on the covariance matrixes of subapertures and space-time spectrum.To solve the problem of nonhomogeneous clutter,we propose a training samples selection method based on the similarity of subaperture covariance matrixes and space-time spectrum.The method based on subapertures utilizes subaperture smoothing techniques to estimate subapertures' covariance matrices,and measures the similarities between the clutter covariance matrix of the CUT and the clutter covariance matrices of the training samples.Training samples whose clutter covariance matrices are similar to that of the CUT are selected.The method based on space-time spectrum utilizes space-time spectrums of training samples and CUT to measure the similarities of the training samples and CUT,and selects training samples whose spectrum are similarity to that of the CUT.Since the selected training samples share the same property with the CUT,the covariance matrix is well estimated,and the performance of STAP improves.4.Dense repeater jamming suppression for airborne phase array radar.Dense repeater jamming not only causes false alarms,but also raises the threshold of constant false alarm ratio detector nearby the jamming,which degrades the performance of targets detection.On the other hand,the samples contaminated by jamming degrade the performance of space time adaptive processing.To deal with these issues,two algorithms to suppress dense repeater jamming are proposed.The first algorithm is based on generalized sidelobe canceller.It firstly estimates the directions of the jammers,and then sum beams pointing at the jammers are formed,afterwards,generalized sidelobe canceller(GSC)is used to cancel the jamming in spatial domain.The covariance matrix of the GSC is estimated with the jamming samples selected from the clutter free region.Simulations show that the proposed algorithm reduces the false alarms caused by the dense repeater jamming and improves the performance of targets detection.In addition,the structure of this method is simple and it's easy to be realized.The second algorithm is based on maximum likelihood estimation.Firstly,the spatial steering vector of repeater jamming is estimated.Afterwards,maximum likelihood method is utilized to estimate the amplitude of target,and the estimated amplitude is used to detect moving target.Simulations show that the proposed method is capable to suppress clutter and repeater jamming.It improves the performance of moving target detection for airborne phase array radar in dense repeater jamming.