Distributed Gaussian mixture model-based particle filter method for chemical pollution source localization with sensor network

Chemical pollution source localization with statistical estimation algorithm in sensor networks, which was also known as source parameters estimation, has an important significance in fields such as pollution environmental monitoring and control. In this paper, a distributed Gaussian mixture dispersion model based particle filter method was proposed for the chemical pollution source localization problem. At the same time, we designed a composite information objective function for sensor scheduling scheme, which comprised of information utility measurement and energy consumption measurement. At last, in order to balance the source localization accuracy and energy consumption, a dynamical sensor radius adjusting method was given for sensor nodes scheduling. Simulation and experiment results show that the proposed method could determine the position of chemical pollution source, compared to UKF, the distributed Gaussian mixture particle filter method was suggested because it could get a significant reduction in the required numbers of sensor nodes and less energy to achieve the desired performance with less time.

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