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Scientific paper - Original scientific paper
Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach
Sensors, 23 (2023), 8; 4148. https://doi.org/10.3390/s23084148
Jurdana, Vedran; Lopac, Nikola; Vrankic, Miroslav

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Jurdana, V., Lopac, N. & Vrankic, M. (2023). Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach. Sensors, 23. (8), 4148. doi: 10.3390/s23084148

Jurdana, Vedran, et al. "Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach." Sensors, vol. 23, no. 8, 2023, pp. 4148. https://doi.org/10.3390/s23084148

Jurdana, Vedran, Nikola Lopac and Miroslav Vrankic. "Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach." Sensors 23, no. 8 (2023): 4148. https://doi.org/10.3390/s23084148

Jurdana, V., Lopac, N. and Vrankic, M. (2023) 'Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach', Sensors, 23(8), pp. 4148. doi: 10.3390/s23084148

Jurdana V, Lopac N, Vrankic M. Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach. Sensors [Internet]. 2023 April 20 [cited 2025 January 12];23(8):4148. doi: 10.3390/s23084148

V. Jurdana, N. Lopac and M. Vrankic, "Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach", Sensors, vol. 23, no. 8, pp. 4148, April 2023. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:187:659092. [Accessed: 12 January 2025]

Metadata
Title (english)Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach
AuthorVedran Jurdana
AuthorNikola Lopac
AuthorMiroslav Vrankic
Author's institutionUniversity of Rijeka
Faculty of Maritime Studies
Author's institutionUniversity of Rijeka
Faculty of Engineering
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Traffic and Transport Technology
Abstract (english)
Compressive sensing (CS) of the signal ambiguity function (AF) and enforcing the sparsity constraint on the resulting signal time-frequency distribution (TFD) has been shown to be an efficient method for time-frequency signal processing. This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the density-based spatial clustering algorithm. Moreover, an appropriate criterion for the performance of the method is formalized, i.e., component concentration and preservation, as well as interference suppression, are measured utilizing the information obtained from the short-term and the narrow-band Rényi entropies, while component connectivity is evaluated using the number of regions with continuously-connected samples. The CS-AF area selection and reconstruction algorithm parameters are optimized using an automatic multi-objective meta-heuristic optimization method, minimizing the here-proposed combination of measures as objective functions. Consistent improvement in CS-AF area selection and TFD reconstruction performance has been achieved without requiring a priori knowledge of the input signal for multiple reconstruction algorithms. This was demonstrated for both noisy synthetic and real-life signals.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleSensors
Numberingvol. 23, no. 8, p. 4148, 4148
e-ISSN1424-8220
DOIhttps://doi.org/10.3390/s23084148
URN:NBNurn:nbn:hr:187:659092
Publication2023-04-20
Document URLhttps://www.mdpi.com/1424-8220/23/8/4148
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-12-20 13:11:42