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Tail-cutting weighted fusion algorithm for multi-sensor measurement data

2013-09-22
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In order to perform robust fusion estimation of pipe wall thickness measurement data obtained in a random disturbance environment, a tail-cut weighted fusion algorithm for multi-sensor measurement data is proposed. The optimal weighting factor is constructed using the tail-cut mean concept in data detection technology, thereby obtaining the robust fusion estimation value of multi-sensor measurement data. Fusion examples show that the algorithm can effectively improve the robustness of system measurement, and has the characteristics of high accuracy and simple operation. Keywords Multisensor Data fusion Signal processing Pipeline Nondestructive testing ing Abstract A trimmed mean- based weighed fusion algorithm is developed for the robust fusion of the wall loss measurement data with a stochastic noise from multisensor1 The trimmed mean method in experimental data analysis is used to build the objective weighed facts, and the robust fused data of multisensor data is obtained as a result1 The fusion results show that the robustness of the measurement system is evidently enhanced, and the al2 go rithm has specific advantages over some conventional algo rithms such as high- accuracy, and high- efficiency1 Key words Multisensor Data fusion Signal processing Pipeline Nondestructive testing ing

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