Robust Frequency-Hopping Spectrum Estimation Based on Sparse Bayesian Method
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 illustration (TFR) is developed to achieve strong estimation. Galvanized by the sparse Bayesian learning algorithm, the matter is formulated hierarchically to induce sparsity. Besides 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 will be greatly improved by this specific data-sharing theme 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 utilised to get strong clustering of the latent parameters for higher estimation performance. Since the problem is formulated in a very full Bayesian framework, labor-intensive parameter tuning process will be avoided. Another superiority of the approach is that high-resolution instantaneous frequency estimation can be directly obtained without any refinement of the TFR. Results of numerical experiments show that the proposed algorithm can achieve superior performance particularly in low signal-to-noise ratio eventualities compared with other recently reported ones.
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