Research On Low-sidelobe Adaptive Beamforming And DOA Estimation Of Four-dimensional Antenna Array

Two important research aspects of array signal processing are adaptive beamforming and Direction-of-Arrival(DOA)estimation.In this thesis,we mainly analyze the adaptive beamforming and DOA estimation based on the four dimensional(4D)antenna arrays.The major works are as follows:1.adaptive beamforming with low sidelobe of 4D antenna arrays.Firstly,wolf pack algorithm(WPA)is adopted to optimize time sequence to achieve harmonic components’adaptive beam with low sidelobe.Secondly,wolf swarm algorithm is used to optimize time sequence and weighted vectors to realize beamspace adaptive beam with low sidelobe.Finally,fundamental component’ adaptive beamforming can be realized based on an improved switch structure.Wolf pack algorithm is employed to optimize time sequence of the improved switch structure to obtain fundamental components’ adaptive beam with low sidelobe.2.DOA estimation of 4D antenna arrays based on compressive sensing(CS).DOA estimation for four dimensional antenna array with unknown number of sources based on sparse representation of covariance matrix estimates angles by getting sparse coefficient of 4D antenna arrays covariance vectors.The algorithm can get accurate DOA estimation without the need to know the number of signal sources in advance.DOA estimation for four dimensional antenna array based on weighted LI norm singular value decomposition algorithm construct sparse representation model of 4D antenna arrays firstly.Then it reduce dimensionality to receiving data matrix according to SVD decomposition.Weighted matrix constructed by the same way penalizes LI norm constrained model to get better sparse solution.The method can suppress spurious peaks effectively and own accurate DOA estimation.