Quantisation error modelling of narrowband adaptive arrays using projected perturbation sequences

Although the effect of weight quantisation has been studied for the full least mean square algorithm it has not been studied for perturbation based adaptive arrays. The paper reports on the results of a study of the effects of weight quantisation on the performance of an adaptive antenna array proce...

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Published inProceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing Vol. ii; pp. II/309 - II/312 vol.2
Main Author Ivandich, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1994
Subjects
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ISBN0780317750
9780780317758
ISSN1520-6149
DOI10.1109/ICASSP.1994.389658

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Abstract Although the effect of weight quantisation has been studied for the full least mean square algorithm it has not been studied for perturbation based adaptive arrays. The paper reports on the results of a study of the effects of weight quantisation on the performance of an adaptive antenna array processor that uses perturbation techniques to obtain the gradient required in the least mean square algorithm. The paper presents both analytical and simulation results on the effect of weight quantisation. Perturbation based processors are important because of their simplicity. The study of quantisation is important because real digital systems are subject to limited dynamic range and quantisation.< >
AbstractList Although the effect of weight quantisation has been studied for the full least mean square algorithm it has not been studied for perturbation based adaptive arrays. The paper reports on the results of a study of the effects of weight quantisation on the performance of an adaptive antenna array processor that uses perturbation techniques to obtain the gradient required in the least mean square algorithm. The paper presents both analytical and simulation results on the effect of weight quantisation. Perturbation based processors are important because of their simplicity. The study of quantisation is important because real digital systems are subject to limited dynamic range and quantisation.< >
Author Ivandich, S.
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Snippet Although the effect of weight quantisation has been studied for the full least mean square algorithm it has not been studied for perturbation based adaptive...
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StartPage II/309
SubjectTerms Adaptive arrays
Antenna arrays
Array signal processing
Australia
Least mean square algorithms
Least squares approximation
Narrowband
Perturbation methods
Quantization
Signal processing algorithms
Title Quantisation error modelling of narrowband adaptive arrays using projected perturbation sequences
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Volume ii
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