Research On Source Localization For Moving Synthetic Array

Source localization is one of the vital tasks of morden signal processing and has been widely used in communications,radar,sonar and other fields.In this thesis,we focus on the direction of arrival(DOA)estimation problem of source localization technology.Recently,the passive synthetic array technology is widely studied in radar and underwater acoustic areas.Compared to convectional DOA estimation methods,the synthetic array methods can achieve high-resolution estimation performance only with small aperture array.This thesis systematically investigates the synthetic form and data model of different synthetic arrays,and proposes suitable DOA estimation methods according to their features.The topic has theoretical significance and practical value.The main contents of this thesis are as follows.1)A low complexity Discriate Fourier Transform(DFT)based DOA estimation algorithm for synthetic nested array is proposed.We first formulate the data model of synthetic nested array model.Then,we propose a low complexity DFT based DOA estimation method to get the coarse DOA estimation.Through the phase rotation operation,the estimates can be improved further.The DFT based method can achieve imporved estimation performance as it utilizes the full aperture of the virtual array while the conventional spatial smoothing subspace based methods will sacrifice half aperture.The Cramer-Rao bound(CRB)comparison between synthetic array and its corresponding physical array is given to prove that they are equivalent for DOA estimation.2)A subspace based DOA estimation algorithm for synthetic linear array considering phase noise is proposed.We first formulate the data model of moving linear array,then we use a two-dimensional peak search method to successively compensate the phase difference of the received signal by phase correction factors,which are estimated using two measurements data.By this way,a longer synthetic array can be formed and thus the estimation performance will be improved.Besides,we propose a reduced-dimensional method to turn the two-dimensional peak search to several one-dimensional ones to avoid the high computational burden but with little estimation performance loss.3)A trilinear decomposition based DOA estimation algorithm for synthetic double parallel linear array is proposed.We first formulate the data model of synthetic double parallel linear array which is formed by a linear array moving along the sideboard.Then we propose a trilinear decomposition based DOA estimation method which is based on the PARAllel FACtor(PARAFAC)model of the received signal and estimate the manifold iteratively so are the DOA estimates.As the proposed method makes full use of the multi-dimensional data model,it outperforms the subspace based methods.Besides,the proposed method does not need to perform peak search and can achieve auto-paired DOA estimation.4)A sparse representation based DOA estimation algorithm for synthetic aoucstic vector sensor(AVS)array is proposed.We first formulate the general data model of synthetic AVS array.Specitally,for synthetic nested AVS array,we introduce a traditional spatial smoothing subspace based DOA estimation method.Based on this method,we propose a sparse representation method to avoid the half aperture sacrificaiton caused by spatial smoothing operation.Besides,we also propose a two-step scheme to turn the two-dimensional grid search of sparse representation to one-dimensional to reduce the computational complexity.As the proposed two-step sparse representation method utilizes the full aperture of the virtual array,it can achieve better estimation performance and more DOF than spatial smoothing subspace based method.