This paper describes two new techniques for the joint estimation of source location and propagation speed using measured time difference of arrival (TDOA) for a sensor array. Previous methods for source location either assumed the array consisted of widely separated subarrays, or used an iterative procedure that required a good initial estimate. The first method directly estimates the source location and propagation speed by converting the solution of a system of nonlinear equations to an overdetermined system of linear equations with two supplemental variables. The second method provides improved estimates by using the solution of the first method as the initial condition for further iteration. The Cramer-Rao Bound (CRB) on the joint estimation is derived, and simulations show the new methods compare favorably to the bound.

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.