Open source tools for Bayesian search

Michael Harris; Nicola Perree; James Wright; Jordi Barr

doi:10.1117/12.3012763

7 June 2024 Open source tools for Bayesian search

Michael Harris, Nicola Perree, James Wright, Jordi Barr

Proceedings Volume 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII; 1305707 (2024) https://doi.org/10.1117/12.3012763
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Michael Harris, Nicola Perree, James Wright, and Jordi Barr "Open source tools for Bayesian search", Proc. SPIE 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII, 1305707 (7 June 2024); https://doi.org/10.1117/12.3012763

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available