Study Of Fast Adaptive Beamforming Technique For Antenna Array

Digital beamforming(DBF) technique can greatly improve the capability of interference suppression, and can conveniently form multi-beam. It is applied for many areas such as radar, communication sonar. For a large-scale adaptive array, heavy computational load and high-rate data transmission are two challenges in the implementation of an adaptive DBF system. Moreover, the large-scale array becomes extremely sensitive to array imperfections. In this dissertation, some research works have been made on fast digital beamforming technique:Partioned-Parallel DBF algorithm, DBF algorithm based on subarray, reduced rank DBF algorithm and robust DBF algorithm. The main contributions are illustrated as follows:1. The efficient Partitioned-Parallel DBF method has been used to deal with the bottleneck of high-rate data transmission and reduce the computational cost. First, the Partitioned-Parallel Linearly Constrained Minimum Variance (PLCMV) is extended to adaptive 2-D antenna array processing. An implementation scheme of the 2-D PLCMV algorithm based on a distributed-parallel-processing system is also present. Then, an efficient Partioned-Parallel DBF algorithm based on the least mean square algorithm (PLMS) is proposed. Moreover, low sidelobe of the beam pattern is obtained by constraining the static steering vector with Tschebyscheff coefficients. PLMS has the same performance as the conventional LMS algorithm. Also, PLMS requires less computational load than PLCMV. as well as it is easier to be implemented to do real time adaptive array processing. An implementation scheme of the PLMS algorithm based on a distributed-parallel-processing system is also present.2. Subarray technique for DBF system is investigated. First, for the large plane antenna arrays with subarrays, the randomly staggered structure is applied to decrease the grating lobes. Binary particle swarm optimization (BPSO) algorithm is used to find the optimal structure of the randomly staggered subarrays. Simulated results show that the plane antenna arrays with subarrays of this new structure can get low side lobe in the scanning range. Then, PLCMV based on the optimal staggered structure is proposed, wich can adaptive cancel the interferences. Combine subarray to reduce the adaptive dimension and the Partioned-Paralle algorithm to achieve fast processing.3. In conventional reduced rank beamformer based on Generalized Sidelobe Canceller (GSC) or Minimum Variance beamformer (MVB). reducing rank matrix is usually obtained from estimated sample covariance matrix by eigendecomposition. To alleviate the computational burden and achieve real-time processing, a fast reduced rank adaptive beamforming based on minimum variance beamformer(FRRMVB) is proposed. FRRMVB takes a set of data vectors as a rough and fast estimate of the interference subspace. And the rank reducing matrix is obtained as the augmentation of the estimated interference subspace and the steering vector of the desired signal. The FRRMVB is very simpler in design and programming, and requires less computational load than the conventional RRMVB adaptive beamformer. Moreover, it has good performance even with small sample size, and has better performance than HTP algorithm. FRRMVB can be used to deal with the real-time adaptive array processing for uncontinuous wave radar. For continuous wave radar, a new fast reduced rank adaptive beamforming algorithm based on GSC(FRRGSC) is proposed. It uses a set of intermediate data vectors in the below branch of GSC to construct the rank reducing matrix. Furthermore, in order to achieve the robustness, all available snapshots have been used to obtain the rank reducing matrix. The FRRGSC method is very simpler in design and programming, and requires less computational load than the conventional reduced rank adaptive beamformer. Simulation results demonstrate the validity of this new method.4. In practical array systems, traditional adaptive beamforming algorithms are known to degrade if some of exploited assumptions on the environment, sources or antenna array become wrong or imprecise. A new efficient partitioned-parallel robust recursive linearly constrained minimum variance (PRRLCMV) algorithm is proposed. An implementation scheme of the PRRLCMV algorithm based on a distributed-parallel-processing system is also present. It can be easily executed in a distributed-parallel-processing fashion, sequentially and in parallel. As a result, the PRRLCMV algorithm provides an effective solution that can alleviate the bottleneck of high-rate data transmission and reduce the computational cost. Moreover, PRRLCMV can significantly reduce the degradation due to various array errors.5. A novel modified projection method is proposed, which is robust against the signal steering vector mismatch and covariance matrix uncertainty. First, an enhanced covariance matrix estimate based on a shrinkage method is obtained. Then, the desired signal subspace is estimated from the eigenvectors of the enhanced covariance matrix, and a new calibrated steering vector of the desired signal is obtained in sequence by projecting the presumed one onto the new estimated desired signal subspace. Compared with the traditional projection method, it does not need to estimate the number of sources, and works well even at low signal-to-noise ratio.