https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Tue, 21 Apr 2026 13:37:01 GMT 2026-04-21T13:37:01Z http://hdl.handle.net/10985/8919 Analysis of multicomponent LFM signals by Teager-Huang-Hough Transform; Analyse des signaux multicomposante à modulation de fréquence linéaire par la transformation de Teager-Huang-Hough BOUCHIKHI, Abdelkhalek; BOUDRAA, Abdel-Ouahab; CEXUS, Jean-Christophe; CHONAVEL, Thierry A novel detection approach of linear FM (LFM) signals, with single or multiple components, in the time-frequency plane of Teager-Huang (TH) transform is presented. The detection scheme that combines TH transform and Hough transform is referred to as Teager-Huang-Hough (THH) transform. The input signal is mapped into the time-frequency plane by using TH transform followed by the application of Hough transform to recognize time-frequency components. LFM components are detected and their parameters are estimated from peaks and their locations in the Hough space. Advantages of THH transform over Hough transform of Wigner-Ville distribution (WVD) are: 1) cross-terms free detection and estimation, and 2) good time and frequency resolutions. No assumptions are made about the number of components of the LFM signals and their models. THH transform is illustrated on multicomponent LFM signals in free and noisy environments and the results compared with WVD-Hough and pseudo-WVD-Hough transforms. Wed, 01 Jan 2014 00:00:00 GMT http://hdl.handle.net/10985/8919 2014-01-01T00:00:00Z BOUCHIKHI, Abdelkhalek BOUDRAA, Abdel-Ouahab CEXUS, Jean-Christophe CHONAVEL, Thierry A novel detection approach of linear FM (LFM) signals, with single or multiple components, in the time-frequency plane of Teager-Huang (TH) transform is presented. The detection scheme that combines TH transform and Hough transform is referred to as Teager-Huang-Hough (THH) transform. The input signal is mapped into the time-frequency plane by using TH transform followed by the application of Hough transform to recognize time-frequency components. LFM components are detected and their parameters are estimated from peaks and their locations in the Hough space. Advantages of THH transform over Hough transform of Wigner-Ville distribution (WVD) are: 1) cross-terms free detection and estimation, and 2) good time and frequency resolutions. No assumptions are made about the number of components of the LFM signals and their models. THH transform is illustrated on multicomponent LFM signals in free and noisy environments and the results compared with WVD-Hough and pseudo-WVD-Hough transforms. http://hdl.handle.net/10985/8999 Psi_B-energy operator and cross-power spectral density BOUDRAA, Abdel-Ouahab; CHONAVEL, Thierry; CEXUS, Jean-Christophe In this paper we consider the hermitian extension of the cross-Psi_B-energy operator that we will denote by Psi_H. In addition, cross energy terms are formalized through multivariate signals representation. We investigate the connection between the interaction energy function of Psi_H and the cross-power spectral density (CPSD)of two complex valued signals. In particular, this link permits to use this operator for estimating the CPSD. We illustrate the interest of Psi_H as a similarity between a pair of signals in frequency domain on synthetic and real data. Wed, 01 Jan 2014 00:00:00 GMT http://hdl.handle.net/10985/8999 2014-01-01T00:00:00Z BOUDRAA, Abdel-Ouahab CHONAVEL, Thierry CEXUS, Jean-Christophe In this paper we consider the hermitian extension of the cross-Psi_B-energy operator that we will denote by Psi_H. In addition, cross energy terms are formalized through multivariate signals representation. We investigate the connection between the interaction energy function of Psi_H and the cross-power spectral density (CPSD)of two complex valued signals. In particular, this link permits to use this operator for estimating the CPSD. We illustrate the interest of Psi_H as a similarity between a pair of signals in frequency domain on synthetic and real data.