Parameter estimate of multi-component LFM signals based on GAPCK

Abstract In this paper, we propose a fast and robust parameter estimate method for multi-component linear frequency modulated (LFM) signals from a finite number of noisy discrete time observations. First, a new kernel called generalized adjustable parameter correlation kernel (GAPCK) is introduced to avoid the coupling terms between time and lag. Then, Matched Fourier transform (MFT) and Robust Energy accumulation (REA) are utilized to obtain the chirp rate estimate of the received LFM signals. This estimate is used to compensate the time-quadratic phase term of the GAPCK, and then Fast Fourier transform (FFT) along time-axis is performed to estimate the constant coefficient. Moreover, the asymptotic statistical properties of parameter estimates are derived. The proposed method has low computational complexity and favorite performance under low signal-to-noise ratio (SNR) due to the low-order non-linearity of the GAPCK. Finally, simulated and real data are provided to verify the robustness and effectiveness of the proposed method.