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Title: Partially adaptive array signal processing with application to airborne radar
Author: Scott, Iain
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1995
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An adaptive array is a signal processor used in conjunction with a set of antennae to provide a versatile form of spatial filtering. The processor combines spatial samples of a propagating field with a variable set of weights, typically chosen to reject interfering signals and noise. In radar, the spatial filtering capability of the array facilitates cancellation of hostile jamming signals and aids in the suppression of clutter. In many applications, the practical usefulness of an adaptive array is limited by the complexity associated with computing the adaptive weights. In a partially adaptive beamformer only a subset of the available degrees of freedom are used adaptively, where adaptive degree of freedom denotes the number of unconstrained or free weights that must be computed. The principal benefits associated with reducing the number of adaptive degrees of freedom are reduced computational burden and improved adaptive convergence rate. The computational cost of adaptive algorithms is generally either directly proportional to the number of adaptive weights or to the square or cube of the number of adaptive weights. In radar it is often mandatory that the number of adaptive weights be reduced with large antenna arrays because of the algorithms computational requirement. The number of data vectors needed for the adaptive weights to converge to their optimal values is also proportional to the number of adaptive weights. Thus, in some applications, adaptive response requirements dictate reductions in the number of adaptive weights. Both of these aspects are investigated in this thesis. The primary disadvantage of reducing the number of adaptive weights is a degradation in the steady-state interference cancellation capability. This degradation is a function of which adaptive degrees of freedom are utilised and is the motivation for the partially adaptive design techniques detailed in this thesis. A new technique for selecting adaptive degrees of freedom is proposed. This algorithm sequentially selects adaptive weights based on an output mean square error criterion. It is demonstrated through simulation that for a given partially adaptive dimension this approach leads to improved steady-state performance, in mean square error terms, over popular eigenstructure approaches. Additionally, the adaptive structure which results from this design method is computationally efficient, yielding a reduction of around 80% in the number of both complex multiplications and additions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available