Study On The DOA Estimation Algorithm For Smart Antennas

Personal mobile communication spreads rapidly with the growth of information communication demands. As the main method of personal communication, wireless mobile communication attracted the attention of this industry. The limited wireless frequency resources and the rapid growth of personal wireless communication demand became more and more conflicted, and in the mean while, the complicated wireless channel condition makes signal transport in multi-channel condition in real work, so it is necessary to introduce methods of signal promotion to improve the performance of receive antenna system. Smart Antenna can adjust its directional pattern adaptively to aim its main lobe to the direction of signals and form null to the interference, and it significantly improves the SINR. In mobile communication system, smart antenna is capable of improving the usage of frequency resource, improving system capacity, extending the coverage area of base stations and significantly improving the performance of telecommunication. Smart antenna realized SDMA (Space Division Multi Access) which is another multi accessing method besides FDMA (Frequency Division Multi Access), TDMA (Time Division Multi Access) and CDMA (Code Division Multi Access), and it had become one of the hottest research fields of 3G. Among these techniques, direction-of-arrival (DOA) estimation is an important part during the research work of smart antenna. Both the division of user signals in uplink and the alternative emitting in the downlink, the DOA information of user signals is necessary to realize the aimed emitting of smart antenna.In this dissertation the traditional DOA estimation methods are summarized and the simulation and performance comparison are given according to same situation. And we have specially analyzed the representative sub-space method MUSIC (Multi Signal Classification) and its improvement method under temporary correlated condition SS-MUSIC (Space Smooth MUSIC), given the simulation results of comparison with traditional methods. Then start from the basic structure of ICA (Independent Components Analysis) we introduced sparse signal analyze from the example of MP (matching pursuit). Then we have discussed the selection of elements in the Dictionary. After that we have applied FOCUSS (Focal Underdetermined System solver) into DOA estimation and have made improvements according to its degrade in noisy condition. FOCUSS is an exhaust algorithm, so we have made improvements in element dictionary to reduce the calculate complexity. Simulation results demonstrated the validity of the methods.