tailieunhanh - 62 Subspace-Based Direction Finding Methods

Estimating bearings of multiple narrowband signals from measurements collected by an array of sensors has been a very active research problem for the last two decades. Typical applications of this problem are radar, communication, and underwater acoustics. | Gonen E. Mendel . Subspace-Based Direction Finding Methods Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton CRC Press LLC 1999 1999 by CRC Press LLC 62 Subspace-Based Direction Finding Methods Egemen Gonen Globalstar Jerry M. Mendel University of Southern California Los Angeles Introduction Formulation of the Problem Second-Order Statistics-Based Methods Signal Subspace Methods Noise Subspace Methods Spatial Smoothing 9 31 Discussion Higher-Order Statistics-Based Methods Discussion Flowchart Comparison of Subspace-Based Methods References Introduction Estimating bearings of multiple narrowband signals from measurements collected by an array of sensors has been a very active research problem for the last two decades. Typical applications of this problem are radar communication and underwater acoustics. Many algorithms have been proposed to solve the bearing estimation problem. One of the first techniques that appeared was beamforming which has a resolution limited by the array structure. Spectral estimation techniques were also applied to the problem. However these techniques fail to resolve closely spaced arrival angles for low signal-to-noise ratios. Another approach is the maximum-likelihood ML solution. This approach has been well documented in the literature. In the stochastic ML method 29 the signals are assumed to be Gaussian whereas they are regarded as arbitrary and deterministic in the deterministic ML method 37 . The sensor noise is modeled as Gaussian in both methods which is a reasonable assumption due to the central limit theorem. The stochastic ML estimates of the bearings achieve the Cramer-Rao bound CRB . On the other hand this does not hold for deterministic ML estimates 32 . The common problem with the ML methods in general is the necessity of solving a nonlinear multidimensional optimization problem which has a high computational cost and for which there is no guarantee