Research On Robust Beamforming Technology And Its Application

As one of the key technologies in the array signal processing filed, adaptive beamforming technology has been widely used in many fileds such as radar, sonar, wireless communication, speech signal processing, electronic countermeasures, earthquake monitoring, and etc. When applying to reality, the array signals are inevitably affected by a variety of deviations, which seriously restrict the performance of the beamformer. Thus it is necessary to study on the robust beamforming algorithms. This thesis mainly study on the non-blind beamforming algorithm which relies on array manifold, and blind beamforming based on constant modulus or non-Gaussian. The main contents are as follows:1. The robust beamforming algorithm based on complex sparse matrix transform(CSMT) is studied. Firstly, aiming at the problem of covariance matrix estimate error casued by finite snapshots, covariance matrix estimating method based on CSMT is proposed. Furthermore we combine with the adavantage of diagonal loading(DL) and shrinkage, and propose two kings of beamforming algorithm: DL-CSMT and Shrinkage-CSMT. Those two beamforming algorithms have better output performance over diagonal loading algorithm. Then aiming at the problem of steering vector mismatch, the robust beamforming algorithm based on quadratic-constraint has been studied and it can not only improve the value of covariance matrix estimated in low snapshots, but also have robustness for steering vector mismatch caused by a variety of deviations. Finally, aiming at the problem of traditional subspace beamforming performance decrease in low SNR, a robust beamforming algorithm based on subspace proposed. This algorithm enables correction of the presumed steering vector through desired signal subspace, the subspace is formated from the steering vector relate to the lager projection value. It does not need to estimate the number of sources, and have robustness to steering vector mismatch.2. Thesis studies on the constant modulus beamforming. Firstly, aiming at the problem of higher sidelobe in low snapshots, we study on the sparse least squares constant modulus algorithm, which restrains the sidelobe gain through sparse restrict and can get good convergence speed and output performance in low snapshots. Then aiming at the problem of constant moudulus algorithm will converge to strong jamming in strong interference, the sparse least squares constant modulus beamforming algorithm based on diagonal loading is proposed. This algorithm enables weigh vector initialization through diagonal loading, can slove the error convergence problem in strong interference and has robustness for array of amplitude and phase error as well as signal pointing errors. However it only has the problem of diagonal loading selection. To solve this problem, least squares constant modulus beamforming algorithm based on eigenvector is proposed. This algorithm enables the initial weigh vector of LSCMA through the largest projection value eigenvector. It can slove the error convergence problem effectively, but the performance decreases when the desire and interference signal have litte differ power.3. The beamforming algorithm based on ICA has been studied. We study on the algorithm of complex fast independent component analysis; this algorithm has better performance over constant modulus algorithm in the condition of strong interference and closely spaces signals.4. The application of beamforming algorithms with actual data has been studied. The sparse least squares constant modulus algorithm based on eigenvector apply to receive actual data with directional antenna and got better performance in actual data testing.