High-Precision Demodulation Method for Surface Acoustic Wave Strain Sensor Based on Hybrid Changing Area Niche Genetic Algorithm

Abstract

The echo signal energy of surface acoustic wave (SAW) strain sensor is weak, resulting in low demodulation accuracy. In order to improve the measurement result, a SAW sensor demodulation method by combining hybrid changing area niche genetic algorithm and MUSIC algorithm was proposed. The proposed method was based on the MUSIC algorithm to construct the power spectrum function of SAW echo signal. A hybrid changing area niche genetic algorithm (CANGA) and particle swarm optimization (PSO) algorithm was developed to improve the spectrum peak search accuracy and reduce the amount calculations of MUSIC algorithm. The power spectrum function of the MUSIC algorithm was used as the fitness function of the hybrid algorithm. The echo frequency was estimated by the variable value corresponding to the optimal solution. The proposed hybrid PSO-CANGA has the characteristics of strong global search ability and fast convergence speed to improve the performance of MUSIC algorithm. The numerical comparisons were performed. The results showed that the hybrid PSO-CANGA gives fastest convergence and the highest estimation accuracy compared to original PSO and NGA. In addition, compared with traditional spectrum estimation algorithms, the proposed algorithm had a minimum estimation error with standard deviation of 0.27KHz. The proposed demodulation method was used to SAW strain system. The nonlinearity of the strain measurement was improved, with a nonlinearity of 0.55%. Simulation and experiment results verified the effectiveness of the proposed algorithm for the demodulation of the SAW strain sensor system. It helps to improve the accuracy of SAW strain measurement.