Cyclic Detectors in the Fraction-of-Time Probability Framework
The signal detection problem for cyclostationary signals is addressed within the fraction-of-time probability framework, where statistical functions are constructed starting from a single time series, without introducing the concept of stochastic process. Single-cycle detectors and quadratic-form de...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Inventions |
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| Online Access: | https://www.mdpi.com/2411-5134/8/6/152 |
| _version_ | 1797380599672995840 |
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| author | Dominique Dehay Jacek Leśkow Antonio Napolitano Timofey Shevgunov |
| author_facet | Dominique Dehay Jacek Leśkow Antonio Napolitano Timofey Shevgunov |
| author_sort | Dominique Dehay |
| collection | DOAJ |
| description | The signal detection problem for cyclostationary signals is addressed within the fraction-of-time probability framework, where statistical functions are constructed starting from a single time series, without introducing the concept of stochastic process. Single-cycle detectors and quadratic-form detectors based on measurements of the Fourier coefficients of the almost-periodically time-variant cumulative distribution and probability density functions are proposed. The adopted fraction-of-time approach provides both methodological and implementation advantages for the proposed detectors. For single-cycle detectors, the decision statistic is a function of the received signal and the threshold is derived using side data under the null hypothesis. For quadratic-form detectors, the decision statistic can be expressed as a function of the received signal without using side data, at the cost of some performance degradation. The threshold can be derived analytically. Performance analysis is carried out using Monte Carlo simulations in severe noise and interference environments, where the proposed detectors provide better performance with respect to the analogous detectors based on second- and higher-order cyclic statistic measurements. |
| first_indexed | 2024-03-08T20:39:39Z |
| format | Article |
| id | doaj.art-3b6b1fec710d4d8e8a38b2e287a7636a |
| institution | Directory Open Access Journal |
| issn | 2411-5134 |
| language | English |
| last_indexed | 2024-03-08T20:39:39Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Inventions |
| spelling | doaj.art-3b6b1fec710d4d8e8a38b2e287a7636a2023-12-22T14:16:32ZengMDPI AGInventions2411-51342023-11-018615210.3390/inventions8060152Cyclic Detectors in the Fraction-of-Time Probability FrameworkDominique Dehay0Jacek Leśkow1Antonio Napolitano2Timofey Shevgunov3IRMAR-UMR CNRS 6625, University Rennes, 35000 Rennes, FranceInstitute of Mathematics, Krakow University of Technology, 31-155 Cracow, PolandDepartment of Engineering, University of Napoli “Parthenope”, 80143 Napoli, ItalyGraduate School of Business, HSE University, 101000 Moscow, RussiaThe signal detection problem for cyclostationary signals is addressed within the fraction-of-time probability framework, where statistical functions are constructed starting from a single time series, without introducing the concept of stochastic process. Single-cycle detectors and quadratic-form detectors based on measurements of the Fourier coefficients of the almost-periodically time-variant cumulative distribution and probability density functions are proposed. The adopted fraction-of-time approach provides both methodological and implementation advantages for the proposed detectors. For single-cycle detectors, the decision statistic is a function of the received signal and the threshold is derived using side data under the null hypothesis. For quadratic-form detectors, the decision statistic can be expressed as a function of the received signal without using side data, at the cost of some performance degradation. The threshold can be derived analytically. Performance analysis is carried out using Monte Carlo simulations in severe noise and interference environments, where the proposed detectors provide better performance with respect to the analogous detectors based on second- and higher-order cyclic statistic measurements.https://www.mdpi.com/2411-5134/8/6/152cyclostationarityweak-signal detectionfraction-of-time probability |
| spellingShingle | Dominique Dehay Jacek Leśkow Antonio Napolitano Timofey Shevgunov Cyclic Detectors in the Fraction-of-Time Probability Framework Inventions cyclostationarity weak-signal detection fraction-of-time probability |
| title | Cyclic Detectors in the Fraction-of-Time Probability Framework |
| title_full | Cyclic Detectors in the Fraction-of-Time Probability Framework |
| title_fullStr | Cyclic Detectors in the Fraction-of-Time Probability Framework |
| title_full_unstemmed | Cyclic Detectors in the Fraction-of-Time Probability Framework |
| title_short | Cyclic Detectors in the Fraction-of-Time Probability Framework |
| title_sort | cyclic detectors in the fraction of time probability framework |
| topic | cyclostationarity weak-signal detection fraction-of-time probability |
| url | https://www.mdpi.com/2411-5134/8/6/152 |
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