Robust adaptive beamforming method based on data correlation constraint

Abstract

The invention provides a robust adaptive beamforming method based on data correlation constraints. The method comprises the following steps: calculating a sample covariance matrix, and carrying out eigenvalue decomposition on the sample covariance matrix; reconstructing an expected signal covariance matrix by using the Capon spatial spectrum to solve an optimization problem about a steering vectorof the interested signal; and calculating an optimal steering vector, and then obtaining an optimal weight vector. By establishing the optimization problem related to the sensitivity of the beamformer, signal self-cancellation can be avoided, and the weight vector is prevented from converging to an interference component in a sample covariance. And in addition, in the solving process of the optimization problem, the calculation complexity is reduced through ingenious transformation, so that the algorithm not only improves the robustness of the system, but also reduces the calculation complexity.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to a robust adaptive beamforming method based on data correlation constraints. Background technique [0002] As an important research direction of array signal processing, beamforming technology is widely used in wireless communication, radar, sonar, seismic survey and radio astronomy and other fields. Its essence is to perform weighted spatial filtering on each array element, so that the main lobe of the beam is aligned to enhance the desired signal (Signal of interest, SOI), and the side lobes and nulls are aligned to the interference signal, thereby improving the signal-to-interference-plus-noise ratio (Signal of interest). to Interference plus Noise Ratio, SINR). The common Capon beamformer can get a good output signal-to-noise ratio when the covariance matrix and steering vector are known accurately. There is a certain error in the covariance matrix and the steering vector, s...

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Problems solved by technology

However, this constraint method cannot make full use of real-time observations to adjust the range of constraints adaptively. In the references Huang L, Zhang J, Xu X, et al. "Robust Adaptive Beamforming With a NovelInterference-Plus-Noise Covariance Matrix Reconstruction Method" In .IEEETransactions on Signal Processing, vol.63, no.7, pp.1643-1650, 2015, the constraints based on the correlation of observation data are fully considered, that is, the covariance matrix of interference and noise is reconstructed based on observation data, and then reused Constrained by the covariance matrix of the structure, but the computational complexity of this algorithm is extremely high

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