BLIND BEAMFORMING ON A RANDOMLY DISTRIBUTED SENSOR ARRAY SYSTEM

We consider a digital signal processing sensor array system, based on randomly distributed sensor nodes, for surveillance and source localiz ation applications. In most array processing the sensor array geometry is fixed and known and the steering array vector/manifold information is used in beam-formation. In this system, array calibration may be i mpractical due to unknown placement and orientation of the sensors wit h unknown frequency/spatial responses. This paper proposes a blind bea mforming technique, using only the measured sensor data, to form eithe r a sample data or a sample correlation matrix. The maximum power coll ection criterion is used to obtain array weights from the dominant eig envector associated with the largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach uses a generaliza tion of Szego's theory of the asymptotic distribution of eigenvalues o f the Toeplitz form. An efficient blind beamforming time delay estimat e of the dominant source is proposed. Source localization based on a l east squares (LS) method for time delay estimation is also given. Resu lts based on analysis, simulation, and measured acoustical sensor data show the effectiveness of this beamforming technique for signal enhan cement and space-time filtering.