Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Model Order Selection for Short Data: An Exponential Fitting Test (EFT) | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 71953 11 pages doi 2007 71953 Research Article Model Order Selection for Short Data An Exponential Fitting Test EFT Angela Quinlan 1 Jean-Pierre Barbot 2 Pascal Larzabal 2 and Martin Haardt3 1 Department of Electronic and Electrical Engineering University of Dublin Trinity College Ireland 2 SATIE Laboratory Ecole Normale Superieure de Cachan 61 avenue du President Wilson 94235 Cachan Cedex France 3 Communications Research Laboratory Ilmenau University of Technology . Box 100565 98684 Ilmenau Germany Received 29 September 2005 Revised 31 May 2006 Accepted 4 June 2006 Recommended by Benoit Champagne High-resolution methods for estimating signal processing parameters such as bearing angles in array processing or frequencies in spectral analysis may be hampered by the model order if poorly selected. As classical model order selection methods fail when the number of snapshots available is small this paper proposes a method for noncoherent sources which continues to work under such conditions while maintaining low computational complexity. For white Gaussian noise and short data we show that the profile of the ordered noise eigenvalues is seen to approximately fit an exponential law. This fact is used to provide a recursive algorithm which detects a mismatch between the observed eigenvalue profile and the theoretical noise-only eigenvalue profile as such a mismatch indicates the presence of a source. Moreover this proposed method allows the probability of false alarm to be controlled and predefined which is a crucial point for systems such as RADARs. Results of simulations are provided in order to show the capabilities of the algorithm. Copyright 2007 Angela Quinlan et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original .
