Simulation of Multi-Sensor Information Fusion Algorithm under Fuzzy Set
Typically,the data fusion from multiple sensors involves a large amount of data.Affected by factors such as noise,incompleteness and inaccuracy,the uncertainty of the fusion result also increases.To address this,a multi-sensor information fusion algorithm based on fuzzy sets was proposed.Firstly,Locality Preserving Projection(LPP)was utilized to process multi-sensor information.Then,Principal Component Analysis(PCA)was employed to separate reflection signals with larger eigenvalues and remove random noise with smaller eigenvalues.Next,the mem-bership function was used to calculate the credibility provided by different sensors.Moreover,both the support and credibility were converted into basic probability assignment functions.Finally,the Dempster-Shafer(D-S)theory was introduced to achieve the optimization of multi-sensor information fusion.Simulation analysis results show that the proposed method can obtain high-precision and efficient fusion results.The peak signal-to-noise ratio reaches over 60dB,and the signal-to-noise ratio remains above 12dB.The longest fusion time is only 2.01ms.Therefore,the fusion performance is effectively optimized.
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