Abstract
Compressive sensing (CS) creates a new framework of signal reconstruction or approximation from a smaller set of incoherent projection compared with the traditional Nyquist-rate sampling theory. Recently, it has been shown that CS can solve some signal processing problems given incoherent measurements without ever reconstructing the signals. Moreover, the number of measurements necessary for most compressive signal processing application such as detection, estimation and classification is lower than that necessary for signal reconstruction. Based on CS, this paper presents a novel identification algorithm of frequency hopping (FH) signals. Given the hop interval, the FH signals can be identified and the hopping frequencies can be estimated with a tiny number of measurements. Simulation results demonstrate that the method is effective and efficient.
Highlights
With so many good advantages such as anti-jam, antiinterception, high security and so on, the technique of frequency hopping spread spectrum (FHSS) has been extensively applied in many areas, especially in military domain
With the good sparsity of FH signals on the local Fourier basis, we show that incoherent measurements can be used to solve the identification problem without ever reconstructing the signal
To demonstrate the feasibility and effectiveness of the proposed algorithm, a wideband FH signal submerged in additive Gaussian white noise (AWGN) is considered to make the simulation experiments
Summary
With so many good advantages such as anti-jam, antiinterception, high security and so on, the technique of frequency hopping spread spectrum (FHSS) has been extensively applied in many areas, especially in military domain. The detection and interception of FH signals can be addressed in several methods of which wide band or channelized receiver, time-frequency distribution, and cyclostationary processing are typical ones [1,2,3,4]. For all the methods above, the extremely large requirement of measurements is one of the most serious disadvantages, which can be a bottleneck in the application of identification of high speed wide band FH signals. There have been some active attempts on signal processing with the advantage of CS for the sparse or compressive signals [5,6,7,8]. It is seldom to be studied on how to develop the potential of CS to make processing of FH signal which is one of the most important sparse or compressive signals
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