184 Science in China Ser. F Information Sciences 2004 Vol. 47 No.2 184—198 Detection and parameter estimation of multicomponent LFM signal based on the fractional Fourier transform QI Lin 1, 2 , TAO Ran 1 , ZHOU Siyong 1 & WANG Yue 1 1. Department of Electronic Engineering, Beijing Institute of Technology, Beijing 100081, China; 2. School of Information Engineering, Zhengzhou University, Zhengzhou 450052, China Correspondence should be addressed to QI Lin (email: qilin@bit.edu.cn) Received April 11, 2003 Abstract This paper presents a new method for the detection and parameter estimation of multicomponent LFM signals based on the fractional Fourier transform. For the optimization in the fractional Fourier domain, an algorithm based on Quasi-Newton method is proposed which consists of two steps of searching, leading to a reduction in computation without loss of accuracy. And for multicomponent signals, we further propose a signal separation technique in the fractional Fourier domain which can effectively suppress the interferences on the detection of the weak components brought by the stronger components. The statistical analysis of the estimate errors is also performed which perfects the method theoretically, and finally, simulation results are provided to show the validity of our method. Keywords: LFM signal, parameter estimation, fractional Fourier transform. DOI: 10.1360/02yf0456 Linear frequency modulation (LFM or chirp) signals are widely used in information systems such as radar, sonar, and communications. In these systems, to detect and estimate LFM signals is an important problem. For a long time, various methods based on maximum likelihood estimator are the predominant solutions to this task. Most of these methods can be ascribed to a multivariable optimization algorithm and are usually computationally demanding in implementation [1—3] . The algorithms provided in refs. [4—8] are much more efficient in computation, but not applicable to multicomponent signals. In recent years, with the progress in the research of time-frequency analysis, many techniques based on time-frequency analysis have been proposed to solve this problem [7—9] . In ref. [9], Haimovich provides a parameter estimate algorithm using the short time Fourier transform (STFT) and wavelet transform (WT), but the narrow or time-variant window results in a poor resolution in the time-frequency domain. In ref. [10], signal detection and parameter estimation are implemented via the Wigner-Ville Copyright by Science in China Press 2004