Search and Rescue Optimal Planning System

The Graphical User Interface uses the Environmental Systems Research Institute (ESRI) Geographic Information System (ArcGIS) and has been altered to include U.S. Coast Guard specific applications such as the SAR Tools Extension and SAROPS Extension. The applications have a wizard-based interface and work within the ArcGIS layered environment. Vector and raster charts are available for display as well as search plans, search patterns, search area environmental data, and probability maps. Finally, the GUI provides reports on all search operations.[1]

The Environmental Data Server (EDS) collects and stores environmental information for use within SAROPS. Local SAROPS servers around the United States request environmental information from the EDS based upon the area of interest. Different environmental products are cataloged on the server ranging from observational systems to modeling products. Observations include sea surface temperature, air temperature, visibility, wave height, global/region tides and currents to name a few. High-resolution model output from operational forecast models like the hybrid coordinate ocean model (HYCOM) and Global NRL Coastal Ocean (NCOM) provide temporally and spatially varying wind and current information. Lastly, the EDS is capable of providing objective analysis tools and aggregation. The list of available products is always changing as researchers in the Navy, local universities and research centers continually improve the accuracy and reliability of products and make them available on a consistent basis.[2]

• Probability of Containment (POC): The likelihood of the search object being contained within the boundaries of some area. It is possible to achieve 100% POC by making the area larger and larger until all possible locations are covered.
• Probability of Detection (POD): The likelihood of detecting an object or recognizing the search object. Different aircraft, environmental conditions and search object types can give a different probability of detection. Generally, the probability of detection decreases with increasing distance from the search object.
• Probability of Success (POS): The likelihood that a search object will be found. POS depends upon the POC and the POD. POS = POC x POD [6]

The simulator wizard makes use of multiple pages of scenario descriptions that are entered by the user in order to compute the possible distress positions and times, subsequent search object drift trajectories, and the effect of completed searches on the search object probabilities. The simulator captures uncertainty in positions, time environmental inputs and leeway parameters. Upon receiving all of the information pertinent to the case, the simulator, using Markov's Monte Carlo method, simulates the drift of up to 10,000 particles for each scenario. For every 20 minutes of drift, the simulator accounts for changes in water current, wind leeway and leeway divergence. The simulator displays the results as a probability density map that can be animated over the drift duration. Figure 1 depicts this type of map.[1] The ensemble trajectory model, random walk and random flight model governing equations are fully explained in Breivik and Allen (2008) and Spaulding, et al. (2005) that is located within O'Donnell, et al. (2005).[7][8] In short, the goal of the simulator is to maximize the probability of success.

The optimal planning wizard takes the probability map information as well as another set of user inputs such as the type of resources, on scene conditions and sweep width values to develop search areas that maximize the POS. The search areas can be adjusted by the SAR controller to further maximize POS. Armed with the best possible fit given available resources, the SAR controller can then transmit the search pattern to the search assets. If the search object is not found on the first search, the optimal planning wizard will account for previous unsuccessful searches when recommending subsequent searches.[1]