Various identification methods have been applied in the field of signal detection, and satisfied results are obtained. However, there is no good method to detect the randomly occurring small-signal with uncertain frequency, amplitude and phase in broad frequency band. In this paper, Hierarchical clustering algorithms and fuzzy-clustering algorithm are investigated to determine the efficiency of recognition, utilizing feature values of signal. Hierarchical clustering algorithm clusters the sample information and the to-be detected information. A comparative analysis of classes between the sample information and the to-be-detected information has been conducted. The new classes are obtained which correspond to the feature values of randomly occurring small-signal. In the signal recognition process, the fuzzy-clustering algorithm is used to eliminate the effects of both short-time random noise and the frequency or intensity change of the noise. The membership grade determines the credibility of detected new signal. Experiment results show that randomly occurring small-signal with uncertain frequency can be recognized in a complicated environment, and the test result will be better if the signal is multi-frequency information.

The paper introduces a novel method to detect the initial time of a weak sinusoidal signal embedded in non-stationary noise and studies the performance of the method through numerical simulations. Furthermore, a proposed nonlinear function combined with time–frequency analysis can be used to effectively improve the signal-to-noise (SNR). Simulation results show that the method can rapidly detect the initial time of the weak analytical signal with a SNR lower than −54 dB and a time-domain resolution of only 1 second.

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