A new multistage Wiener filter is introduced which utilizes a decomposition based on orthogonal projections. A reduced-rank Wiener filter is developed based on this new structure which is not basis oriented, but evolv...
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A new multistage Wiener filter is introduced which utilizes a decomposition based on orthogonal projections. A reduced-rank Wiener filter is developed based on this new structure which is not basis oriented, but evolves a basis which is a function of the multistage decomposition. The performance of this new Wiener filtering structure is evaluated using a comparative computer analysis model. It is demonstrated that the low-complexity multistage reduced-rank Wiener filter is capable of outperforming the more complex eigendecomposition-based methods.
This paper introduces low-complexity frequency domain approximations of optimal partially adaptive sensor array processors for space communications. Space-segment sensor arrays, for military and other critical communi...
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This paper introduces low-complexity frequency domain approximations of optimal partially adaptive sensor array processors for space communications. Space-segment sensor arrays, for military and other critical communications links, require adaptivity to provide ECCM and mitigate interference. The size, weight, and power restrictions of such platforms, however, prohibit full adaptivity. The design of low-complexity reduced-rank processors are investigated in this paper. It is demonstrated that the performance of full-rank adaptive arrays is obtainable by low-rank processors through the use of frequency domain implementations of a cross-spectral metric.
This paper is concerned with reducing the rank of the adaptive weight vector in radar array signalprocessing. The motivation for reducing the rank is that modern space-time processing requires many more weights than ...
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