Parameter estimation of frequency hopping signals based on adaptive mesh

The base mismatch problem of existing sparse reconstruction algorithms for frequency hopping signals leads to poor sparse representation ability of discrete dictionaries, which seriously affects the performance of sparse reconstruction algorithms. In view of this situation, a variational Bayesian sparse reconstruction algorithm based on adaptive mesh is proposed. The method realizes self-renewal of the dictionary by continuously weighting clustering and scaling processing on the dictionary, which makes the parameter mesh more refined. The simulation results show that the proposed method has good anti-noise and cross-term suppression ability. At the same time, the base mismatch of the sparse reconstruction algorithm is alleviated, and the time-frequency focusing is further improved. Under the condition of lower signal-to-noise ratio, the time-frequency matrix with higher time-frequency resolution can be obtained, and the time-hopping detection and estimation of parameters such as hopping period and frequency can be accomplished more accurately. © 2019, Editorial Office of Systems Engineering and Electronics. All right reserved.