Research On Fast And Robust Adaptive Antenna Beamforming Algorithm

Adaptive beamforming technology plays a crucial role in military and civilian wireless communication because of its high resolution,high processing gain,and easy control of direction.The adaptive beamforming technology is essentially a spatial filter,which can effectively suppress interference and improve the output signal to interference and noise ratio(SINR).However,in the actual application environment,the entire array antenna system is inevitably affected by various errors,such as desired signal direction of arrival(DOA)estimation errors,local coherent scatter,array element position error,mutual coupling between array elements,etc.These errors lead to actual steering vector matrix is inconsistent with the ideal steering vector matrix,which will seriously affect the output performance of adaptive beamformer.In addition,the convergence speed is also a key indicator to measure the feasibility of the algorithm while the adaptive beamforming algorithm achieves excellent output performance.Therefore,it is of great important to study the robustness and fast calculation method of adaptive beamforming algorithm in various error environments.Since most of the current robust adaptive beamforming algorithms are studied based on single error,there is a problem of poor robustness under comprehensive error.So robust adaptive antenna fast beamforming algorithm is studied from the perspective of improving the robustness of the algorithm under various error environments and reducing the computational complexity of algorithm.The main work and innovations include:1.For the problem that the depth of null is shallow in current null broadening beamforming algorithm,which is not enough to effectively suppress the fast motion interference.A robust adaptive beamforming algorithm based on subspace projection and virtual antenna array(SPVAA)is proposed.The algorithm broadens the width of null by subspace projection technology,and further deepens the depth of null by combining the virtual antenna technology.The simulation results show that SPVAA algorithm has excellent performance on null broadening and can effectively suppress fast motion interference.2.Aiming at the problem of beamformer output performance degradation under the desired signal steering vector mismatch,two robust adaptive beamforming algorithms are proposed.Firstly,a robust beamforming algorithm based on spatial power spectrum sampling and steering vector estimation(SPSS-SVE)is proposed,which estimates the desired signal steering vector by constructing a correlation matrix in the approximate region of the desired signal DOA and reconstructs the interference plus noise covariance matrix by the spatial spectrum sampling technique,the robustness of the adaptive beamformer under the desired signal steering vector mismatch is improved,and the computational complexity of SPSS-SVE algorithm is greatly reduced compared with IPN-RAB algorithm.Then,for the problem that there is a close relationship between the output performance of SPSS-SVE algorithm and the number of array elements,which will result in poor output performance with fewer array elements.A robust adaptive beamforming algorithm based on spatial power spectrum sampling and virtual antenna technology is proposed,this algorithm introduces virtual antenna technology based on SPSSSVE algorithm,which increases the freedom of array antenna and expands the array aperture.The robustness of adaptive beamformer is improved with fewer array elements under the condition of desired signal steering vector mismatch.3.For the problem of that adaptive beamforming is a poor robustness in the comprehensive error environment,currently.A robust adaptive beamforming algorithm based on spatial power spectrum sampling and steering vector correction(SPSS-SVC)is proposed,this algorithm introduces the uncertain set constraint to reconstruct interference plus noise covariance matrix based on SPSS-SVE algorithm,and corrects the desired signal steering vector estimated by the uncertain set constraint method.The simulation results show that SPSS-SVC algorithm has an excellent performance under the comprehensive error environment.