Search-efficient methods of detection of cyclostationary signals
Conventional signal processing methods that exploit cyclostationarity for the detection of weak signals in noise require fine resolution in cycle frequency for long integration time. Hence, in cases of weak-signal detection and broadband search, problems in implementation, such as excessive computat...
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| Veröffentlicht in: | IEEE transactions on signal processing 1996-05, Vol.44 (5), p.1214-1223 |
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| container_title | IEEE transactions on signal processing |
| container_volume | 44 |
| creator | Yeung, G.K. Gardner, W.A. |
| description | Conventional signal processing methods that exploit cyclostationarity for the detection of weak signals in noise require fine resolution in cycle frequency for long integration time. Hence, in cases of weak-signal detection and broadband search, problems in implementation, such as excessive computational complexity and storage and search arise. This paper introduces two new search-efficient methods of cycle detection, namely the autocorrelated cyclic autocorrelation (ACA) and the autocorrelated cyclic periodogram (ACP) methods. For a given level of performance reliability, the ACA and ACP methods allow much larger resolution width in cycle frequency to be used in their implementations, compared to the conventional methods of cyclic spectral analysis. Thus, the amount of storage and search can be substantially reduced. Analyses of the two methods, performance comparison, and computer simulation results are presented. |
| doi_str_mv | 10.1109/78.502333 |
| format | Article |
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Hence, in cases of weak-signal detection and broadband search, problems in implementation, such as excessive computational complexity and storage and search arise. This paper introduces two new search-efficient methods of cycle detection, namely the autocorrelated cyclic autocorrelation (ACA) and the autocorrelated cyclic periodogram (ACP) methods. For a given level of performance reliability, the ACA and ACP methods allow much larger resolution width in cycle frequency to be used in their implementations, compared to the conventional methods of cyclic spectral analysis. Thus, the amount of storage and search can be substantially reduced. 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Hence, in cases of weak-signal detection and broadband search, problems in implementation, such as excessive computational complexity and storage and search arise. This paper introduces two new search-efficient methods of cycle detection, namely the autocorrelated cyclic autocorrelation (ACA) and the autocorrelated cyclic periodogram (ACP) methods. For a given level of performance reliability, the ACA and ACP methods allow much larger resolution width in cycle frequency to be used in their implementations, compared to the conventional methods of cyclic spectral analysis. Thus, the amount of storage and search can be substantially reduced. Analyses of the two methods, performance comparison, and computer simulation results are presented.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/78.502333</doi><tpages>10</tpages></addata></record> |
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| identifier | ISSN: 1053-587X |
| ispartof | IEEE transactions on signal processing, 1996-05, Vol.44 (5), p.1214-1223 |
| issn | 1053-587X 1941-0476 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Autocorrelation Computational complexity Computer vision Detection, estimation, filtering, equalization, prediction Exact sciences and technology Frequency estimation Information, signal and communications theory Performance analysis Radiometry Signal and communications theory Signal detection Signal processing Signal resolution Signal, noise Spectral analysis Telecommunications and information theory |
| title | Search-efficient methods of detection of cyclostationary signals |
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