Research On Smart Antenna DOA Estimation Technology

Array signal processing and its application has been one of the focuses in signal processing field. By sampling and processing signal both in time domain and in spatial domain, the information of interest contained in the signal can be exploited sufficiently, thereby interference can be suppressed more effectively, and so the capacity of system can be improved by introducing array signal processing algorithms. In this dissertation, the model and theory of array signal processing are studied; DOAs estimation and beamforming technologies and their correlated technology were researched deeply, which are the two main aspects in array signal processing field.1. The algorithm based on the eigen-space was investigated, it make the MVDR possess the ability resisting the pointing error, but the computational amount is very large, and the performance of the system will degrade sharply when the constraint vector is orthogonal with the signal steering vector. Aiming at the above deficiency of the eigne-space beamformer, the paper construct an average steering vector assuming that the DOA distribution of the signal is uniform in the specified angular region. The novel constraint steering vector broadens the mainlobe width of the beamformer, reduces the probability that signal is at the edge of the mainlobe, so it can combat the pointing error effectively. The paper also derives the selection criterion of the angular interval as the variance of the element number. The simulation results of the computer show the algorithm proposed by this paper is robust for the pointing error, but it do not increase the computation effort much.2. This dissertation investigated beamforming based on the minimum bit error rate and proposed a space-time equalization algorithm based on the minimum bit error rate and the adaptive implement of space-time equalization algorithm based on the minimum bit error rate, In order to determine the bit error rate of a decision system, we must firstly known the probability density function of the observed signal, unfortunately, we can not often obtain it.