Signal Detection Based on Power-Spectrum Sub-Band Energy Ratio
Abstract
:1. Introduction
2. Statistical Characteristics of the PSER
2.1. Statistical Characteristics of GWN
2.2. Statistical Characteristics of Power Spectrum Bin for GWN
2.2.1. Statistical Characteristics of One Power Spectrum Bin
2.2.2. Statistical Characteristics of Sum of Multiple Power Spectra Bins
2.3. Identifying Probability Distribution for the PSER
2.4. Statistical Characteristics of the PSER under H0
2.4.1. Probability Distribution for
2.4.2. Probability Distribution for
2.5. Statistical Characteristics of the PSER under H1
2.5.1. Probability Distribution for
2.5.2. Probability Distribution for
2.5.3. Relationship between and Signal-to-Noise Ratio (SNR)
3. Signal Detection Based on the PSER
3.1. Principle
3.2. Performance Comparison of the PSER with Other Energy Detection Methods
3.2.1. Time-Domain Energy Detection
3.2.2. Local-Spectrum Energy Detection
3.2.3. Theoretical Detection Probabilities Comparison
3.3. Detection Performance of the PSER under Noise Uncertainty
3.3.1. Detection Performance of TDED under Noise Uncertainty
3.3.2. Detection Performance of LSED under Noise Uncertainty
3.3.3. Detection Performance of PESR under Noise Uncertainty
3.3.4. Detection Performance Comparison of Three Methods under Noise Uncertainty
4. Experiments
4.1. Simulations
4.1.1. Narrow-Band Signal
4.1.2. Broadband Signal
High-Local SNR
Low-Local SNR
Comparison of Mean Square Error
4.2. Vibration Signal Detection
4.2.1. Background Noise
4.2.2. Tapping Signal
4.2.3. Detection
5. Discussion
5.1. Rationale for Rectangular Window Function Selection
5.2. Rationality of CDF for the PSER
5.3. Calculation of CDF for the PSER
5.4. Detection Performance of the PSER Depends on Sub-Band Energy Ratio Coefficient
5.5. Advantages of the PSER
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Legese Hailemariam, Z.; Lai, Y.-C.; Chen, Y.-H.; Wu, Y.-H.; Chang, A. Social-Aware Peer Discovery for Energy Harvesting-Based Device-To-Device Communications. Sensors 2019, 19, 2304. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pei-Han, Q.; Zan, L.; Jiang-Bo, S.; Rui, G. A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems. Chin. Phys. B 2014, 23, 537–547. [Google Scholar]
- Akram, J.; Eaton, D.W. A review and appraisal of arrival-time picking methods for downhole microseismic data. Geophysics 2016, 81, 71–91. [Google Scholar] [CrossRef]
- Islam, M.R.; Uddin, J.; Kim, J. Acoustic Emission Sensor Network Based Fault Diagnosis of Induction Motors Using a Gabor Filter and Multiclass Support Vector Machines. Ad Hoc Sens. Wirel. Ne 2016, 34, 273–287. [Google Scholar]
- Mei, F.; Hu, C.; Li, P.; Zhang, J. Study on main Frequency precursor characteristics of Acoustic Emission from Deep buried Dali Rock explosion. Arab. J. Geoences 2019, 12, 645. [Google Scholar] [CrossRef]
- Preisig, J.C.; Johnson, M.P. Signal detection for communications in the underwater acoustic environment. IEEE J. Oceanic. Eng. 2001, 26, 572–585. [Google Scholar] [CrossRef]
- Wang, C.; Chang, X.; Liu, Y.; Chen, S. Mechanistic Characteristics of Double Dominant Frequencies of Acoustic Emission Signals in the Entire Fracture Process of Fine Sandstone. Energies 2019, 12, 3959. [Google Scholar] [CrossRef] [Green Version]
- Xu, C.; Guan, J.; Bao, M.; Lu, J.; Ye, W. Pattern recognition based on enhanced multifeature parameters for vibration events in φ-OTDR distributed optical fiber sensing system. Microw. Opt. Technol. Lett. 2017, 59, 3134–3141. [Google Scholar] [CrossRef]
- Huang, G.; Law, A.W.; Guo, X. Taylor Dispersion of Contaminants by Dual-peak Spectral Random Waves. China Ocean Eng. 2019, 33, 537–543. [Google Scholar] [CrossRef]
- Yang, Y.; Zeng, Q.; Yin, G.; Wan, L. Vibration Test of Single Coal Gangue Particle Directly Impacting the Metal Plate and the Study of Coal Gangue Recognition Based on Vibration Signal and Stacking Integration. IEEE Access 2019, 7, 106783–106804. [Google Scholar] [CrossRef]
- Lu, C.; Liu, Y.; Wang, H.; Liu, P. Microseismic signals of double-layer hard and thick igneous strata separation and fracturing. Int. J. Coal. Geol. 2016, 160, 28–41. [Google Scholar] [CrossRef]
- Kong, B.; Wang, E.; Li, Z.; Lu, W. Study on the Feature of Electromagnetic Radiation under Coal Oxidation and Temperature Rise Based on Multifractal Theory. Fractals 2019, 27, 1950038. [Google Scholar] [CrossRef]
- Koopmans, L.H. The Spectral Analysis of Time Series; Elsevier: Amsterdam, The Netherlands, 1974. [Google Scholar]
- Groth, E.J. Probability distributions related to power spectra. Astrophys. J. Suppl. 1975, 29, 285–302. [Google Scholar] [CrossRef]
- Johnson, P.E.; Long, D.G. The probability density of spectral estimates based on modified periodogram averages. IEEE T Signal Proces. 1999, 47, 1255–1261. [Google Scholar] [CrossRef]
- Martínez, D.M.; Andrade, A.G. Performance evaluation of welch’s periodogram-based energy detection for spectrum sensing. IET Commun. 2013, 7, 1117–1125. [Google Scholar] [CrossRef]
- Gao, R.; Li, Z.; Qi, P.; Li, H. A Robust Cooperative Spectrum Sensing Method in Cognitive Radio Networks. IEEE Commun. Lett. 2014, 18, 1987–1990. [Google Scholar] [CrossRef]
- Bomfin, R.C.D.V.; Guimaraes, D.A.; de Souza, R.A.A. On the Probability of False Alarm of the Power Spectral Density Split Cancellation Method. IEEE Wirel. Commun. Lett. 2016, 5, 164–167. [Google Scholar] [CrossRef]
- Bomfin, R.C.D.V.; de Souza, R.A.A.; Guimaraes, D.A. Circular Folding Cooperative Power Spectral Density Split Cancellation Algorithm for Spectrum Sensing. IEEE Commun. Lett. 2017, 21, 250–253. [Google Scholar] [CrossRef]
- Gurugopinath, S.; Akula, R.; Murthy, C.R.; Prasanna, R.; Amrutur, B. Design and implementation of spectrum sensing for cognitive radios with a frequency-hopping primary system. Phys. Commun. Amst. 2015, 17, 172–184. [Google Scholar]
- Gupta, M.S.; Kumar, K. Progression on spectrum sensing for cognitive radio networks: A survey, classification, challenges and future research issues. J. Netw. Comput. Appl. 2019, 143, 47–76. [Google Scholar] [CrossRef]
- Arjoune, Y.; Kaabouch, N. A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions. Sensors 2019, 19, 126. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kabeel, A.A.; Hussein, A.H.; Khalaf, A.A.M.; Hamed, H.F.A. A utilization of multiple antenna elements for matched filter based spectrum sensing performance enhancement in cognitive radio system. AEU Int. J. Electron. C 2019, 107, 98–109. [Google Scholar] [CrossRef]
- Chatziantoniou, E.; Allen, B.; Velisavljevic, V.; Karadimas, P.; Coon, J. Energy Detection Based Spectrum Sensing Over Two-Wave With Diffuse Power Fading Channels. IEEE T Veh. Technol. 2017, 66, 868–874. [Google Scholar] [CrossRef] [Green Version]
- Reyes, H.; Subramaniam, S.; Kaabouch, N.; Hu, W.C. A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks. Comput. Electr. Eng. 2016, 52, 319–327. [Google Scholar] [CrossRef] [Green Version]
- Johnson, N.L.; Kotz, S.; Balakrishnan, N. Continuous Univariate Distributions, Volume 2; John Wiley & Sons: New York, NY, USA, 1995. [Google Scholar]
- Gil, A.; Segura, J.; Temme, N.M. Efficient algorithms for the inversion of the cumulative central beta distribution. Numer. Algorithms 2017, 74, 77–91. [Google Scholar] [CrossRef] [Green Version]
- Temme, N.M. Asymptotic inversion of the incomplete beta function. J. Comput. Appl. Math. 1992, 41, 145–157. [Google Scholar] [CrossRef] [Green Version]
- Sarker, M.B.I. Energy Detector Based Spectrum Sensing by Adaptive Threshold for Low SNR in CR Networks. In Proceedings of the 2015 24th Wireless and Optical Communication Conference, Taipei, Taiwan, 23–24 October 2015; pp. 118–122. [Google Scholar]
- Tandra, R.; Sahai, A. SNR Walls for Signal Detection. IEEE J. Stsp. 2008, 2, 4–17. [Google Scholar] [CrossRef] [Green Version]
Probability | PSER | LSED | TDED |
Pf | 0.0075 × 10−4 | 0.6535 × 10−4 | 0.0308 × 10−4 |
Pd | 0.0615 × 10−4 | 0.7531 × 10−4 | 0.0441 × 10−4 |
Bandwidth (Hz) | Probability | PSER | LSED | TDED |
35–65 | Pf | 0.0508 × 10−4 | 0.4921 × 10−4 | 0.0614 × 10−4 |
Pd | 0.5019 × 10−4 | 0.6254 × 10−4 | 0.0886 × 10−4 | |
21–40 | Pf | 0.0250 × 10−4 | 0.7161 × 10−4 | 0.0603 × 10−4 |
Pd | 0.4483 × 10−4 | 0.9455 × 10−4 | 0.0801 × 10−4 |
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Li, H.; Hu, Y.; Wang, S. Signal Detection Based on Power-Spectrum Sub-Band Energy Ratio. Electronics 2021, 10, 64. https://doi.org/10.3390/electronics10010064
Li H, Hu Y, Wang S. Signal Detection Based on Power-Spectrum Sub-Band Energy Ratio. Electronics. 2021; 10(1):64. https://doi.org/10.3390/electronics10010064
Chicago/Turabian StyleLi, Han, Yanzhu Hu, and Song Wang. 2021. "Signal Detection Based on Power-Spectrum Sub-Band Energy Ratio" Electronics 10, no. 1: 64. https://doi.org/10.3390/electronics10010064
APA StyleLi, H., Hu, Y., & Wang, S. (2021). Signal Detection Based on Power-Spectrum Sub-Band Energy Ratio. Electronics, 10(1), 64. https://doi.org/10.3390/electronics10010064