Aiming at the issue of the traditional stochastic resonance only applicable to deal with low-frequency signals, a high-frequency weak signal detection method based on scale transformation is proposed in this paper. The high frequency weak signal mixed with noise is scaled to a low frequency signal. The signal conforms to the adiabatic elimination theory. So when it acts on stochastic resonance systems, the stochastic resonance can arise. The original high frequency weak signal mixed with noise can be retrieved by scaled up by the same ratio. To deal with the unknown frequency mixed with noise, the high frequency mixed signal is scaled down continuously to achieve a suitable matching parameters for the stochastic system. According to the change of resonance spectral peak value, the unknown frequency can be found from the mixed signal. This method is effective for future application.

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System sensitivity and range resolution are a pair of contradictory performance during target detection. In this paper, pulse compression is used to resolve the contradiction between range resolution and sensitivity. A frequency leak occurs in the compressed signal, so that weak targets of the matched filter output may be masked by adjacent strong target side lobes. It is common to suppress the side lobes by adding a window function. Through MATLAB simulation, it is found that when the signal-noise ratio is large, after adding the window function, the small target submerged by the large target side lobes cannot be detected. In this paper, an adaptive pulse compression (APC) technique based on minimum mean square error (MMSE) is used to suppress side lobes. The simulation proves that APC algorithm suppresses side lobes more effectively.

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