Blind beamforming on a randomly distributed sensor array system

TLDR

A digital signal processing sensor array with randomly distributed nodes is considered, where sensor geometry and responses are unknown, making traditional calibration impractical. The paper proposes a blind beamforming method that uses only measured data to estimate the dominant source’s time delay without prior array knowledge. Weights are derived from the dominant eigenvector of the sample correlation matrix via a maximum‑power criterion, justified by a generalized Szegő theorem, and a least‑squares approach is used for time‑delay estimation. Analysis, simulation, and acoustic measurements demonstrate that the technique effectively enhances signals and performs space‑time filtering.

Abstract

We consider a digital signal processing sensor array system, based on randomly distributed sensor nodes, for surveillance and source localization applications. In most array processing the sensor array geometry is fixed and known and the steering array vector/manifold information is used in beamformation. In this system, array calibration may be impractical due to unknown placement and orientation of the sensors with unknown frequency/spatial responses. This paper proposes a blind beamforming technique, using only the measured sensor data, to form either a sample data or a sample correlation matrix. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector associated with the largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach uses a generalization of Szego's (1958) theory of the asymptotic distribution of eigenvalues of the Toeplitz form. An efficient blind beamforming time delay estimate of the dominant source is proposed. Source localization based on a least squares (LS) method for time delay estimation is also given. Results based on analysis, simulation, and measured acoustical sensor data show the effectiveness of this beamforming technique for signal enhancement and space-time filtering.