Study On Beamforming Algorithm Based On STOA In Low Snapshots

Array signal processing algorithms under research are almost based on thehypothesis that the number of snapshots is greater than the number of arraysensors. The high computational complexity of the algorithm, complex hardwaresystems and communication with low real-time is disadvantageous for the actualsignal processing system in this case. Therefore, conformal beam while reducingthe number of snapshots is necessary.Covariance matrix generates deficient rank problem in the conventionalbeamforming algorithms when the number of array elements is more than thenumber of snapshots. Beamforming algorithm of small snapshots becomes thefocus of attention to most scholars in recent years.Research of beamforming under small snapshots is converted into researchof the covariance matrix estimation under small snapshots when covariancematrix estimation has been the basis in the field of array signal processing.Linear shrinkage covariance estimation under small snapshots and real estimateis not consistent. In order to solve this problem, we combine Tapering covariancematrix estimation and linear shrinkage estimates into STOA algorithm. Thisalgorithm meets the consistency, and the estimation performan ce is superior toTapering estimation and linear shrinkage estimates.Its performance under small snapshots is better than GLC algorithm throughthe indicators of SINR and the desired signal power estimation when STOA isapplied to Capon beamforming. STOA’s Capon beamforming algorithm based onsmall snapshots reduces the computational complexity and is useful to improvereal-time communication compared with most of algorithms based on the casethat the number of snapshots is greater than the number of array sensors in thearray signal processing.