A novel odoQuan Zhangr source localization system based on particle filtering and information entropy

Abstract So far, gas leakage caused by natural or human factors has led to serious consequences in terms of social security. Previous strategies for locating the odor sources appear to be either defective or incomplete. For enhancing the success rate and rapidity, this paper aims to present a novel and complete strategy in search of lurking gas sources. Particle filtering and information entropy are both employed to track the plume information. To improve the tracking efficiency in this process, a novel objective function is designed by considering the entropy gains of the suspected targets as well as the repeated exploration scores. Considering the pseudo sourced caused by obstacles, a statistics-based source determine algorithm is proposed to confirm the source’s authenticity, while the artificial potential field method is subsequently applied to eliminate the distractions introduced by the pseudo sources. Simulations and on-site tests are both carried out while results showed that the proposed scheme is competent to complete sources localization task in the scene that contains randomly distributed obstacles and pseudo source.

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