Research on Chirp Signal Denoising Algorithm Based on IoT

Abstract

With the continuous development of IoT, smart grid receives widespread attention. Aiming at the problem of poor performance of the Rake receiver in a wideband micro-power wireless communication system under a low signal-to-noise ratio, the non-stationarity of the Chirp signal used in the system and the adaptability of the non-stationary signal of the denoising method are proposed. A complementary empirical mode decomposition (CEEMD) combined with wavelet threshold denoising algorithm to improve the receiver's signal-to-noise ratio. The CEEMD algorithm can not only handle non-stationary signals well, but also overcome the modal aliasing phenomenon. However, using only the CEEMD algorithm, some effective information will be lost when removing high-noise high-frequency IMF components. Therefore, this paper combines CEEMD decomposition with wavelet threshold denoising, and performs wavelet threshold denoising processing on high-frequency IMF components decomposed by CEEMD to extract useful information from high-frequency components. Through matlab software simulation, the signal-to-noise ratio has been improved by about 1dB.