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. (1393). THE PRINCIPAL COMPONENT INVERSE ALGORITHM FOR DETECTION IN THE PRESENCE OF REVERBERATION USING AUTOREGRESSIVE MODEL. , 38(E1), 91-97. doi: 10.22099/ijste.2014.2100
. "THE PRINCIPAL COMPONENT INVERSE ALGORITHM FOR DETECTION IN THE PRESENCE OF REVERBERATION USING AUTOREGRESSIVE MODEL". , 38, E1, 1393, 91-97. doi: 10.22099/ijste.2014.2100
. (1393). 'THE PRINCIPAL COMPONENT INVERSE ALGORITHM FOR DETECTION IN THE PRESENCE OF REVERBERATION USING AUTOREGRESSIVE MODEL', , 38(E1), pp. 91-97. doi: 10.22099/ijste.2014.2100
. THE PRINCIPAL COMPONENT INVERSE ALGORITHM FOR DETECTION IN THE PRESENCE OF REVERBERATION USING AUTOREGRESSIVE MODEL. , 1393; 38(E1): 91-97. doi: 10.22099/ijste.2014.2100

THE PRINCIPAL COMPONENT INVERSE ALGORITHM FOR DETECTION IN THE PRESENCE OF REVERBERATION USING AUTOREGRESSIVE MODEL

Iranian Journal of Science and Technology Transactions of Electrical Engineering
مقاله 6، دوره 38، E1، شهریور 2014، صفحه 91-97    اصل مقاله (184.86 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22099/ijste.2014.2100
چکیده
Reverberation noise in active sonar leads to a very complicated situation for target
detection. Reverberation is often modeled as the autoregressive model. In this paper, the
autoregressive model is considered for reverberation and the Principal Component Inverse (PCI)
algorithm is used to separate target echo signal from reverberation. This consideration helps us to
propose a new method to improve computational complexity for the rank determination of the
observation matrix via singular value decomposition. It is shown that this new method is efficient
on real data to separate target echo signal from reverberation.
کلیدواژه‌ها
Reverberation cancellation؛ autoregressive model؛ principal component inverse (PCI) algorithm
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