Abstract
In this study, we exploit a granular-based modeling scheme to realize the number of sources detection under nonideal conditions (in low Signal-to-Noise Ratio levels and small snapshots scenarios), in which the idea of information granules and Granular Computing is integrated with the fuzzy set theory. In the developed scheme, a collection of eigenvalues, which is calculated by the array output correlation matrix, is constructed as a time series. Then, the principle of justifiable granularity criterion is introduced to split the time series into two regions so as to establish a set of upper and lower bounds of prototypes for the signal and noise subspaces. Subsequently, the Fuzzy C-Means based encoding and decoding mechanism is employed to optimize the prototypes. During this process, the encoding mechanism is used to produce a pair of information granules, and the decoding mechanism is used to evaluate the quality of information granules. After several rounds of iteration, an optimized prototype vector and a partition matrix are generated. Finally, the structure of the time series is exposed (encoded into) by an optimized prototype vector and a partition matrix, and the number of sources can be determined conveniently through the partition matrix. Simulation results show that the proposed method outperforms the commonly used methods under nonideal conditions.
Published Version
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