Open Source Tools for Bayesian Search

When searching for a target whose state is unknown, it is desirable to implement an appropriate search method to maximise efficiency through the minimisation of an associated cost function. The posterior distribution over the target state returned by Bayesian search provides just such a function. Nevertheless, finding the best algorithm for a given task is often non-trivial; a common approach is to build a model that accurately represents the scenario and to compare the efficacy of competing algorithms. This requires a toolkit that is easy to adapt and is able to demonstrate a range of sensor characteristics, target behaviours and search schemes. This paper shows how Stone Soup, an open source state estimation and tracking framework, can be an effective tool for Bayesian search. It demonstrates how user-defined search scenarios can be incorporated into Stone Soup's sensor management capability to model Bayesian search algorithms and compare them against heuristic methods. Several examples are provided to demonstrate this. The benefit of using Stone Soup is that the implementer of Bayesian search need not exert significant energy understanding or reinventing algorithms for modelling all aspects of sensor management. Instead, they can focus on their area of expertise, building up an appropriate model, and use the relevant tools in Stone Soup to implement the search algorithms. This paper lays the foundations for more complex search scenarios to be modelled using Stone Soup, offering more realism to the user.