An adaptive multisensor data fusion system based on wavelet denoising and neural network

Quan Liu; Xiaomei Zhang

doi:10.1117/12.633261

5 November 2005 An adaptive multisensor data fusion system based on wavelet denoising and neural network

Quan Liu, Xiaomei Zhang

Proceedings Volume 5998, Sensors for Harsh Environments II; 59980K (2005) https://doi.org/10.1117/12.633261
Event: Optics East 2005, 2005, Boston, MA, United States

Abstract

The purpose of this paper is to present a novel three-layer adaptive multisensor data fusion system, which is appropriate to the harsh environment. In order to overcome the noise in the data collected by the sensors in the harsh environment, the first layer of the system is the data pretreatment layer. In this layer, the data collected by the sensor array is denoised by the wavelet threshold algorithm, which provides reliable data to the next data fusion Layer. Taking use of the good error tolerance and self-studying performance of NN (neural network), the data from the first layer is fused by the second layer--- data fusion layer based on NN. The third layer is the feedback layer, in which the output signal is feedback to the second layer. The adaptive algorithm will adjust the weights of the units in the NN, which implements the adaptive ability of the whole system. The experimental results presented in the paper indicate that the system proposed here implements data fusion effectively, its fusion precision is improved compared with the traditional fusion system, and has many advantages like strong adaptive ability, high SNR (signal-to-noise ratio) and low distortion, etc.

Citation Download Citation

Quan Liu and Xiaomei Zhang "An adaptive multisensor data fusion system based on wavelet denoising and neural network", Proc. SPIE 5998, Sensors for Harsh Environments II, 59980K (5 November 2005); https://doi.org/10.1117/12.633261

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