Anti-Interference Technology of Surface Acoustic Wave Sensor Based on K-Means Clustering Algorithm

Various types of interference signals are available in the working environment of passive wireless surface acoustic wave (SAW) sensors. Among these kinds of interference, co-channel interference is difficult to suppress. To solve this problem, a SAW sensor anti-interference technology was proposed to improve the reliability of the SAW sensor. Wavelet denoising method was used to denoise SAW resonator (SAWR) response, which can maintain the envelope characteristics of the SAW response. The entropy energy model of the SAW response signal was established, and the signal envelope was extracted from the proposed entropy energy function. The waveform envelope and the entropy energy curve were adopted as the signal characteristics to form two-dimensional points. The K-Means algorithm was used to classify the two-dimensional points to distinguish the SAW response from sinusoidal interference. Simulation results showed that the SAW response can be detected with a rate of more than 85% when the signal-to-noise ratio was greater than 4 dB, whereas the false detection rate of the sinusoidal interference signal was less than 8%. Finally, the proposed algorithm was used to detect the actual SAW response and sinusoidal interference signal. The experimental results showed that the proposed method can clearly distinguish the SAW response from the co-channel interference signal. Moreover, the proposed method can be used as the anti-interference technology to improve the stability of the SAW sensor.