Signal Processing and Modelling of Smart Sensors for Multi-Parameter Water Quality Detection
Aiming at the multi-parameter water quality detection intelligent sensor is susceptible to the environment,noise,light source and other interference factors,the experiment proposes a combination of recursive average filtering algorithm and wavelet transform algorithm of the sensor signal processing method.The method first uses the recursive average filtering algorithm to pre-process the periodic interference factors of the sensor input signal,and then uses the wavelet transform algorithm to further reduce the signal noise of the sensor signal.The wavelet transform algorithm is able to refine the collected signals by stretching and translating,so as to achieve the localisation of time and frequency,and ultimately achieve the purpose of time refinement for the high-frequency part and frequency refinement for the low-frequency part.The experimental results show that under the same threshold conditions,the more the amount of noise reduction,the more significant the noise suppression effect.The effectiveness of the proposed method is thus verified.
multi-parameter water quality detectionsensor signal processingrecursive average filtering algorithmwavelet transform algorithm
NETL
NSTL
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