Centre for Smart Emissions Sensing Technologies, Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada.
Department of Mechanical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2023 May 16;23(10):4799. doi: 10.3390/s23104799.
There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster-Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover's distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow.
在具有湍流流体流动的环境中，自主多气味源定位 (MOSL) 有两种主要算法：独立后验 (IP) 和 Dempster-Shafer (DS) 理论算法。这两种算法都使用某种形式的占据网格映射来映射给定位置是源的概率。它们有可能应用于使用移动点传感器来协助定位发射源。然而，目前尚不清楚这两种算法的性能和局限性，在应用之前需要更好地了解它们在各种条件下的有效性。为了解决这一知识差距，我们测试了这两种算法对不同环境和气味搜索参数的响应。使用大地移动距离来衡量算法的定位性能。结果表明，与 DS 理论算法相比，IP 算法通过在没有源的位置最小化源归属，从而在正确识别源位置方面表现更好。DS 理论算法也正确识别了实际源，但错误地将排放归因于许多没有源的位置。这些结果表明，在具有湍流流体流动的环境中，IP 算法为解决 MOSL 问题提供了一种更合适的方法。
