A hybrid algorithm for fast parameter estimation of LFM signal
Jun Song, Ni Sun, Gao Yue · 2017
According to the characteristics of linear frequency modulation (LFM) signal, the new algorithm determines the optimal delay time and delay length in the autocorrelation sequence based on the sequence convolution method. The sinusoidal parameter estimation algorithm of time autocorrelation sequence is also improved. Under the conditions of different initial frequency, coefficient of frequency modulation, amplitude and other parameters, the more accurate coefficient of frequency modulation k and initial frequency f0 are obtained in the hybrid algorithm, at the same time, the computational complexity of the algorithm is low. The simulation results show that the accuracy of LFM signal estimation is still close to the Cramer-Rao Bound (CRB) at lower signal-to-noise ratio, which reflects the effectiveness and stability of the hybrid algorithm.