Nyquist Zone Index and Chirp Rate Estimation of LFM Signal Intercepted by Nyquist Folding Receiver Based on Random Sample Consensus and Fractional Fourier Transform

Abstract

The Nyquist folding receiver (NYFR) can achieve a high-probability interception of an ultra-wideband (UWB) signal with fewer devices, while the output of the NYFR is converted into a hybrid modulated signal of the local oscillator (LO) and the received signal, which requires the matching parameter estimation methods. The linear frequency modulation (LFM) signal is a typical low probability of intercept (LPI) radar signal. In this paper, an estimation method of both the Nyquist Zone (NZ) index and the chirp rate for the LFM signal intercepted by NYFR was proposed. First, according to the time-frequency characteristics of the LFM signal, the accurate NZ and the rough chirp rate was estimated based on least squares (LS) and random sample consensus (RANSAC). Then, the information of the LO was removed from the hybrid modulated signal by the known NZ, and the precise chirp rate was obtained by using the fractional Fourier transform (FrFT). Moreover, a fast search method of FrFT optimal order was presented, which could obviously reduce the computational complexity. The simulation demonstrated that the proposed method could precisely estimate the parameters of the hybrid modulated output signal of the NYFR.

Information Recovery for Signals Intercepted by Dual-Channel Nyquist Folding Receiver with Adjustable Local Oscillator

“…To assess the proposed algorithm, we conducted Monte Carlo simulations for 500 times with each SNR and calculated the probability of correct determination (PCD) of NZ index using the in Table 1. We also compared the proposed method with the autocorrelation method [3], STFT method [4] and the spectrum peak search method [6]. For different methods, the input signal is the same, and so is the SNR.…”

Machine Learning Algorithms for Identification of Low Signal-to-Noise Ratio Radar Signals

“…Many current approaches are based on the sampling theorem and fractional broadening [5]. In prior work, this problem has been investigated using various signal processing methods for LFM parametric estimation, such as the Maximum likelihood estimation (MLE), Dechirp parameter estimation, the Wigner-Ville distribution (WVD) time-frequency analysis and the Fractional Fourier transform (FrFT).…”