Robust frequency-hopping spectrum estimation based on sparse bayesian method

Lifan Zhao, Lu Wang, Guoan Bi, Liren Zhang, Haijian Zhang

    Research output: Contribution to journalArticlepeer-review

    64 Citations (Scopus)


    This paper considers the problem of estimating multiple frequency hopping signals with unknown hopping pattern. By segmenting the received signals into overlapped measurements and leveraging the property that frequency content at each time instant is intrinsically parsimonious, a sparsity-inspired high-resolution time-frequency representation (TFR) is developed to achieve robust estimation. Inspired by the sparse Bayesian learning algorithm, the problem is formulated hierarchically to induce sparsity. In addition to the sparsity, the hopping pattern is exploited via temporal-aware clustering by exerting a dependent Dirichlet process prior over the latent parametric space. The estimation accuracy of the parameters can be greatly improved by this particular information-sharing scheme and sharp boundary of the hopping time estimation is manifested. Moreover, the proposed algorithm is further extended to multi-channel cases, where task-relation is utilized to obtain robust clustering of the latent parameters for better estimation performance. Since the problem is formulated in a full Bayesian framework, labor-intensive parameter tuning process can be avoided. Another superiority of the approach is that high-resolution instantaneous frequency estimation can be directly obtained without further refinement of the TFR. Results of numerical experiments show that the proposed algorithm can achieve superior performance particularly in low signal-to-noise ratio scenarios compared with other recently reported ones.

    Original languageEnglish
    Article number6910302
    Pages (from-to)781-793
    Number of pages13
    JournalIEEE Transactions on Wireless Communications
    Issue number2
    Publication statusPublished - Feb 2015


    • Dirichlet process
    • multiple frequency-hopping signals
    • sparse Bayesian learning
    • stick-breaking process
    • timefrequency representation

    ASJC Scopus subject areas

    • Computer Science Applications
    • Electrical and Electronic Engineering
    • Applied Mathematics


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