Research On Direction Of Arrival Estimation Algorithms Based On Compressive Sensing

With the rapid development of signal processing technology, the performance requirements of direction-finding system in various fields become stricter. To improve the accuracy of source localization, DOA estimation algorithms have been highly regarded. As a matter of fact, conventional algorithms represented by MUSIC algorithm already have a high accuracy and resolution by appropriate improvements. But they are usually invalid to coherent sources and there are stringent requirements for SNR, the number of snapshots and other conditions. In recent years, with the arisen of compressed sensing theory, some scholars turn their attention to a sparse representation model of the received signal and attempt to utilize it to achieve signal recovery together with DOA estimation. It’s significant that only a single snapshot of the antenna array is enough, and these algorithms are insensitive to the correlation between the sources. In this paper, an in-depth analysis on some key technologies of such algorithms is presented.Firstly, the traditional model of DOA estimation and relevant basic theory of the model based on compressed sensing are discribed. The differences and connections between them are explained in detail. Moreover, the paper introduced the corresponding dimension reduction strategies aiming at the increasing computation brought by multiple snapshots and 2-D DOA estimation. All of these lay the theoretical foundation for the following research.Several DOA estimation algorithms based on different norm minimization are introduced for ordinary arrays. The paper focus on their principles and concrete steps under conditions of a single snapshot or more. Then the superiority of these algorithms in the case of a single snapshot or coherent sources is verified by simulation results. Comparative analysis of their merits and drawbacks is provided in order to choose appropriate algorithm for practical applications.Finally, the paper introduced a sparse representation model based on polarization sensitive array and a BOMP algorithm for DOA estimation. Since the particularity of the model hasn’t been take into consideration, a novel algorithm(P-BGSP) is proposed in this paper. The selection criterion of atoms is modified based on the polarization of signals, which enables the choice of atoms and polarization estimation to be achieved simultaneously. Then the subspace pursuit algorithm is extended to the recovery of block sparse signal in the model, thereby achieving DOA estimation. Simulation results show that, under the same conditions, the performance of the proposed algorithm significantly transcends the BOMP algorithm and equally easy to implement. The effectiveness of the algorithm is also demonstrated by the estimation results of measured data.