Decoding and measurement of frequency‐hopping spread spectrum signals using an adaptive algorithm‐based compressive sensing

Summary Decoding of frequency‐hopping spread spectrum (FHSS) methods to intercept the symbol content in signals and brilliant measurement is presented in this paper. The switching of the frequency channels rapidly using a pseudorandom code makes interception of FHSS signals very difficult. Therefore, a proposed adaptive compressive measurement for decoding the frequency‐hopping receivers that have no prior knowledge of the hopping sequence is the main focus of this work. The proposed methods rely on the instantaneous sparsity of the spectra of FHSS signals, and the transmitted symbols can be recovered from the proposed compressive measurements. Furthermore, the contribution of this work is the design of a proposed adaptive method that is used to design measurement kernels for compressive measurement and decoding based on knowledge enhancement, which includes both prior information enhanced and adaptive methods based on the properties of the intersignals. Simulation results on Gaussian frequency‐shift keying (GFSK) FHSS signals illustrate that the proposed compressive method enhances the detection performance of the received frequencies with acceptable decoding accuracy compared with other techniques such as enhanced orthogonal matching pursuit (EOMP) and an adaptive sampling kernels method with a random compressive ratio. Also, an enhancement of the proposed algorithm is achieved due to enhancement in both the normalized mean square error (NMSE) and the probability of correction error ( P ce ). The comparison between these algorithms is performed using simulation based on MATLAB and receiver operating characteristic (ROC) curves.