Research On Mainlobe Interference Suppression In Array Radar

When spatial interference signal is located in sidelobe,the application of the conventional adaptive beamforming method can suppress the interference effectively.But when the interference falls into mainlobe,it will form a null in mainlobe to suppress the interference,which causes distortion of mainlobe and beam-steering deviation and increase of sidelobe level,and leads to decrease of the output signal to interference plus noise ratio and the rise of the false-alarm rate,and peak shift will have an impact on estimation precision of the target direction.To deal with the problem of mainlobe jamming,two methods of suppressing the mainlobe interference based on blocking matrix preprocessing and based on eigen-projection matrix preprocessing are firstly discussed in this thesis.They can both eliminate influence of mainlobe interference and not form a null in mainlobe,which solves the distortion of mainlobe and the increase of sidelobe.Then the pattern reshaping methods,including weight compensation method,bleaching processing method,diagonal loading method and the combination of diagonal loading and linear constraints are put forward to solve the deviation of main beam-steering.On the basis of the eigen-projection,a mainlobe interference suppression method based on eigen-projection and covariance matrix reconstruction(EP-CMR)is researched under the conditions of not containing the desired signal in the training data.The algorithm improves the method of discriminating the eigenvector of mainlobe interference,which is more accurate because it is not necessary to select the discriminant factor.The algorithm uses the covariance matrix reconstruction technique,avoiding a matrix inversion operation,reducing the complexity of the algorithm,and also improves the influence of the small eigenvalues corresponding to the noise in the covariance matrix on the adaptive forming algorithm.In the practical engineering application,when the power of mainlobe jamming is too high,the sidelobe suppression performance of EP-CMR will be degraded.The nulling which formed in the sidelobe interference direction will become very shallow or even completely disappear.At the same time when the training data has desired signal,the algorithm will suppress the signal.Therefore,in accordance with EP-CMR,an improved algorithm based on eigen-projection and covariance matrix reconstruction is studied in this thesis.Through the comparative analysis,we can obtain that the EMP method does not need to estimate the direction of mainlobe interference,so it is more robust than BMP method.The pattern reshaping method based on the combination of diagonal loading and linear constraints is the best method to solve the deviation of main beam-steering among the four pattern reshaping methods.The computer simulation results demonstrate that an improved algorithm based on EP-CMR can ensure the deeper null steering in the sidelobe interference direction and not suppress the target signal while effectively suppressing the mainlobe interference,and thus raise the output signal to interference plus noise ratio of the system in the case of the higher power of the mainlobe interference.