THE FORWARD-BACKWARD AVERAGING TECHNIQUE APPLIED TO TLS-ESPRIT PROCESSING

Certain array geometries greatly simplify and enhance high resolution array processing, In this paper, two techniques are used-the ESPRIT al gorithm, which employs two shifted but otherwise identical subarrays, and forward-backward averaging, which can be applied to axis-symmetric al arrays, The former has been shown to provide an efficient solution to bearing estimation while the latter incorporates the a priori knowl edge about the symmetry, effectively increasing the number of data vec tors available and decorrelating coherent or highly correlated signals , A combination of the two techniques implies a special array geometry that includes uniformly spaced linear arrays, The resulting algorithm yields parameter estimates that are constrained on the unit circle, s atisfying the postulated data model provided merely that the arguments of these estimates are distinct, However, if the arguments of some pa rameter estimates coincide in a given scenario, the ESPRIT algorithm d oes not yield different results for distinct signals and these estimat es can be rejected, Perhaps the most significant advantage of combinin g forward-backward averaging with ESPRIT parameter estimation is the s ubstantial reduction in computational complexity that can be achieved, Based on the centro-Hermitian property of the data and noise covarian ce matrices, the computational complexity of the ESPRIT solution is re duced almost by a factor of four and the algorithm can be formulated e ntirely over the field of real numbers.