Research On Digital Beamforming Algorithm

Under ideal conditions, adaptive array signal processing methods can get excellent performance and adaptive beamformers provide an improvement in array output signal-to-interference-plus-noise-ratio (SINR) in comparison with conventional beamforming. In practical operating circumstances, the performance of adaptive array signal processing methods degrade extremely due to existing errors. Therefore the study of robust algorithm for adaptive array signal processing is becoming more urgent and important. The focus in this dissertation is on discussion of improving the performance and robustness of adaptive array beamformers. The main content can be outlined as follows:In chapter 1, we first provide a background of this work and then present an overview of robust adaptive array processing. We highlight the key points of this dissertation at the end of this charter.In chapter 2, a modified projection transformation method is presented, which utilizes preliminary estimation of interference DOA. It is demonstrated that the side lobe performance of adapted antenna patterns can be significantly improved with this method and increased convergence rate of adaptive algorithm when the array manifold is not known exactly.In chapter 3, we study the adaptive beamforming method when the covariance matrix with desired signal present. The eigenspace-based adaptive array beamformers have good performance when desired signal SNR is large, but severely degrade when desired signal is small. We discuss an improving method by combined with conventional beamformer.In chapter 4, a Chebyshev method beamforming for uniform circular arrays (UCAs) is proposed based on Dolph-Chebyshev method for uniform linear arrays (ULAs). This method is that it computationally efficient which makes it eminently suitable for real-time beamforming and beamstearing applications. We describe the new approach for synthesizing array patterns with guaranteed least sidelobe levels for any look direction. In the chapter, we are also presented a low sidelobe beamforming synthesis technique. The main beam is pointed in the proper direction by choosing the steering vector for that direction. The sidelobes are controlled by introducing a large number of interfering signal at many angles throughout the sidelobe region. The algorithm iterates on the interference powers until a suitable pattern is obtained.In chapter 5, Constant modulus algorithm (CMA) is a well-known blind adaptive beamforming method as it requires no pre-knowledge about the signal except that the transmitted signal waveform has a constant envelope. The multistage constant modulus (CM) array is a cascade adaptive beamforming system that can recover several narrowband co-channel signals without training. The main idea of the SMI-CMA is to use SMI to determine the initial weight for CMA operation. The method can come up with the desire signal in despite of the interfering signal is stronger than the desire signal.