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- W2143975911 abstract "Both fully adaptive and partially adaptive processing algorithms can be implemented using linear and nonlinear conjugate gradient (CG) methods. This approach can be computationally attractive when a family of continuous time-varying adaptive problems must be solved. An iterative approach that starts with the previous solution can converge quickly to the new solution. This paper considers linear and nonlinear CG methods for adaptive processing. The example used is space-time adaptive processing (STAP) and clutter interference subspace tracking within the context of an airborne radar with a rotating array. It is seen that the linear CG method for adaptive filtering in this context can be several times less costly than sample matrix inversion if the interference subspace has sufficient eigenvalue structure, and that a nonlinear CG algorithm is capable of tracking the principal clutter interference subspace." @default.
- W2143975911 created "2016-06-24" @default.
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- W2143975911 date "2002-12-24" @default.
- W2143975911 modified "2023-09-25" @default.
- W2143975911 title "Linear and nonlinear conjugate gradient methods for adaptive processing" @default.
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- W2143975911 doi "https://doi.org/10.1109/icassp.1996.544225" @default.
- W2143975911 hasPublicationYear "2002" @default.
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