## 3 Solution Approach  
The proposed solution for the UAV-based source localization assumes the following conditions:  
1. The targeted environment is limited to a rectangular area in the horizontal space;
2. There is only one source releasing the pollutant;
3. The UAV starts the mission in an arbitrary position on the targeted environment, without information of the plume of pollutant;
4. The UAV has one sensor for the pollutant measurements;
5. It is possible to estimate the vector of the winds in the horizontal plane;
6. The variations of the winds do not affect considerably the control of positioning (navigation) of the UAV;
7. The flying time of the vehicle and the capacity of memory and processing onboard of the vehicle are limited.  
Critical features to be considered in the design of the algorithm for the source localization are:  
1. The algorithm generates waypoints to guide the UAV towards the pollutant source location;
2. Minimal distances among the waypoints generated by the algorithm are required, hereafter called waypoints resolution. This property will help the UAV to use some strategy of path planning and trajectory planning, to navigate among the waypoints, when the environment has obstacles. Also, as the conventional GPS devices present low resolution, the UAV outdoor navigation could be difficult if the waypoints generated by the algorithm are very close between them; and
3. The precise localization of the source could turn unfeasible in many times, or even the mission can fail in reaching the pollutant plume. Despite this fact, given the captured pollutant and wind information, the system must be able to provide the most probable areas of location of the source.  
Measurements of air pollutants are imprecise and depend on sensor quality. The strategy here presented uses these measurements in two approaches--the raw measurement of the sensor, which is the measurement of the pollutant captured by the sensor, and the binary information of the sensor, that is, the raw sensor data are processed to get detection or non-detection information. The two approaches are processed in parallel, making the algorithm more robust.  
To get binary information, which will be used by the probabilistic component, the air pollution measurement is processed to define whether it is a detection or non-detection of the pollutant. In our simulated environment, we set a fixed threshold--a value of the pollutant measurement higher than the threshold is a detection, and a value under the threshold is a non-detection. Also, in a real environment, to determine if a measurement is a detection or is not, the measurement scale of the sensor must be considered.  
The proposed approach combines two strategies; first, the metaheuristic technique helps to trace the plume concentration towards the source, using the recent and instantaneous raw information of the sensor. The second component is the probabilistic one, which allows our strategy to find the pollutant plume and helps the metaheuristic technique to concentrate the search in areas where the source is probably located. This component uses the binary historical information of the sensor and the wind effects to create and update a probabilistic map of the source localization in the targeted environment.