Abstract: Aiming at the problem of inaccurate information collected by a single sensor, we proposed a data fusion algorithm for confined space detection based on Bayesian estimation. Firstly, by analyzing the composition structure of the detection signal,  filtering, amplitude limiting, step signal removal and other methods were used to solve the problem of signal interference and improve the significance of characteristic parameters. Secondly, based on  the dynamic characteristics of the data fusion architecture, reasonable assumptions were given, and a dynamic Bayesian network model was jointly established by combining a prior network and the transfer network to obtain the fusion objective function. Finally, by introducing  normal distribution  to study the uncertainty of detection value, the detection node was regarded as the likelihood function, and the maximum a posteriori probability after fusion was deived. Taking the fusion weighted average error ratio as the index, multi type detection data fusion was realized by “two-two encounter”. The results of simulation experiments show that the proposed algorithm solves the problem of signal redundancy, the data fusion effect is better, the overall number of fire missed reports is less, and the highest value of the data fusion time is only 2.4 s.

Key words: dynamic Bayesian network, confined space, data fusion, normal distribution