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-de fined 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 bene t 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.
The tracking and state estimation community is broad, with diverse interests. These range from algorithmic research and development, applications to solve specific problems, to systems integration. Yet until recently, in contrast to similar communities, few tools for common development and testing were widespread. This was the motivation for the development of Stone Soup - the open source tracking and state estimation framework. The goal of Stone Soup is to conceive the solution of any tracking problem as a machine. This machine is built from components of varying degrees of sophistication for a particular purpose. The encapsulated nature and modularity of these components allow efficiency and reuse. Metrics give confidence in evaluation. The open nature of the code promotes collaboration. In April 2019, the Stone Soup initial beta version (v0.1b) was released, and though development continues apace, the framework is stable, versioned and subject to review. In this paper, we summarise the key features of and enhancements to Stone Soup - much advanced since the original beta release - and highlight several uses to which Stone Soup has been applied. These include a drone data fusion challenge, sensor management, target classification, and multi-object tracking in video using TensorFlow object detection. We also detail introductory and tutorial information of interest to a new user.
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