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 Detection and Parameter Estimation of Multicomponent LFM Signal Based on the Cubic Phase Function | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 743985 7 pages doi 2008 743985 Research Article Detection and Parameter Estimation of Multicomponent LFM Signal Based on the Cubic Phase Function Yong Wang and Yi-Cheng Jiang Harbin Institute of Technology Research Institute of Electronic Engineering Technology Harbin 150001 China Correspondence should be addressed to Yong Wang wangyong6012@ Received 27 September 2007 Revised 17 January 2008 Accepted 5 March 2008 Recommended by Jar-Ferr Yang A new algorithm for the detection and parameters estimation of LFM signal is presented in this paper. By the computation of the cubic phase function CPF of the signal it is shown that the CPF is concentrated along the frequency rate law of the signal and the peak of the CPF yields the estimate of the frequency rate. The initial frequency and amplitude can be obtained by the dechirp technique and fast Fourier transform. And for multicomponent signal the CLEAN technique combined with the CPF is proposed to detect the weak components submerged by the stronger components. The statistical performance is analyzed and the simulation results are shown simultaneously. Copyright 2008 Y. Wang and . Jiang. 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 work is properly cited. 1. INTRODUCTION Linear frequency-modulated LFM or chirp signals are frequently encountered in applications such as radar sonar bioengineering and so forth. The amplitude initial frequency and chirp rate are the basic parameters which denote the characteristic of the LFM signal and the estimation of them is an important problem in the signal processing community. Several estimation procedures have been proposed but most are based on the maximum likelihood ML principle 1 2 . These methods can be ascribed to a
