Detection and Parameter Estimation of Strong and Weak LFM Signals in the Fractional Fourier Domain

In order to improve the detection efficiency of the fractional Fourier transform for multicomponent LFM signals, this paper focuses on detection and parameter estimation of strong and weak LFM signals based on the fractional Fourier transform (FRFT). First, respectively introduce the detection and parameter estimation theories of multicomponent LFM signals based on the elimination one by one method and clustering analysis method. And analyze their advantages and disadvantages existing in the detection of strong and weak LFM signals. A novel detection method is presented combining elimination one by one method with clustering analysis method, and a clustering algorithm named broad first search neighbors (BFSN) is introduced to detect the multicomponent LFM signals. It can simultaneously detect several signals with approximative energy. It improves the detection efficiency and it also improve the parameter estimation precision of the stronger signals. The preprocessing by the flat cutting to reduce the points’ number of the input data-set is proposed for the BFSN clustering analysis method. It improves the algorithm’ computation efficiency. And then, use the elimination one by one method to eliminate the strong signals. So it can also eliminate the shading effects of strong signals on weak signals. Finally, simulations verify the effectiveness of the method.