Intra-Pulse Modulation Recognition for Fractional Bandlimited Signals Based on a Modified MWC-Based Digital Receiver

Abstract

In this paper, we present a modified modulated wideband converter (MWC)-based digital receiver for fractional bandlimited signals, and further propose an intra-pulse modulation recognition method in discrete time fractional Fourier domain (DTFrFD) for the intercepted signals. The proposed digital receiver can move the cross-channel signal to the baseband and since the nonzero part of its available spectrum is narrower in DTFrFD than in discrete time Fourier domain (DTFD), so a better separation can be achieved by the proposed digital receiver than by original MWC-based receiver. Then, with the data acquired from the digital receiver, we propose an intra-pulse modulation recognition method based on the optimal transformation order and the spectral kurtosis (SK) in DTFrFD for the six types of fractional bandlimited signals. In this algorithm, the optimal transformation order is tested to distinguish the encoded signals from the non-encoded signals, and then the SK is tested to determine the intra-pulse modulation type specially. The computational complexity of the proposed method is much lower than search-based methods. Meanwhile, since the SK of Gaussian signals is zero in DTFrFD, the proposed method shows better robustness than the original MWC discrete compressive sampling structure against SNR variation. Simulation results confirm the obtained analytical results.INDEX TERMS Wideband digital receiver, modulated wideband converter, fractional Fourier transform, intra-pulse modulation recognition.

Compressed Sampling in Shift-Invariant Spaces Associated With FrFT

“…The FrFT is a powerful mathematical tool in the fields of ultra-wideband communication, radar and time-variant filtering and so on [2][3][4]. Due to the importance of the FrFT in signal and image processing, sampling theories have been developed from the traditional frequency domain (FD) to the fractional Fourier domain (FrFD) for many years.…”

“…In simple terms, CS is applied to determine a length N vector x from M < N linear measurements, where x is known to be K-sparse in some bases. Many efficient sampling and recovery algorithms have been studied for CS associated with the FrFT [3], but those methods are simply extended from the FD without analysis of the relationship between the sampling spaces and compressed methods. Our goal is to combine the CS and SISs to proposed a more general sampling model with a sufficiently low analog sampling rate.…”