Searching and tracking for mobile targets with multiple mobile sensing platforms is a relevant capability for many real-world scenarios such as search and rescue and surveillance. This paper presents an approach to performing searching and tracking for a single target of interest using multiple mobile sensor systems in urban environments. Formulating the searching and tracking problem as a partially observable Markov decision process, a coverage control objective inspired reward function is designed, to create a single objective function for both searching and tracking behaviours. Furthermore, the approach alleviates differentiability requirements in typical coverage control approaches. Visibility informed Bernoulli filter provides a suitable tracking algorithm estimating both probability that a target exists and its state. Road network information is used to inform the information coverage objective for scenario tailored search behaviour. Monte Carlo tree search is adopted to implement the algorithm non-myopically and balance exploitation of immediate rewards with exploration of potential future gains. Contribution to the open source Stone Soup framework creates several new components for the toolkit, where the flexibility of the approach can be leveraged with the established wealth of tracking and filtering algorithms. Simulated Urban Mobility software package is integrated with Stone Soup to create a realistic urban simulation scenario with two sensing agents. Through Monte Carlo simulations, the algorithm demonstrates efficient search behaviour, good target tracking performance and effective coverage of the target once found.

In recent years, state-space models for highly manoeuvrable objects have been proposed based on non-Gaussian, continuous time, jump-based Lévy processes, the so-called Lévy state-space model [1]–[4]. In these models, the standard Brownian motion driving process for continuous time processes is replaced with a heavy-tailed non-Gaussian alternative. This retains all the flexibility of its Gaussian counterpart in terms of possible dynamical model structures and operations with irregular time stamps or heterogeneous data sources. These models aim to operate in areas such as surveillance of irregularly moving drones or people, and tracking wildlife or biological data. Implementation is relatively straightforward since the Kalman filters of the Brownian motion case can be replaced in the nonGaussian case by mixtures of Kalman filters within a marginalised particle filtering framework [5]. While the Stone Soup tracking software environment includes both Kalman filtering and generic particle filtering, it does not currently allow the combination of these tasks within a marginalised particle filtering framework. We discuss the significant challenges involved in incorporating these models and algorithms into Stone Soup, and present initial simulation results for the new software.

This paper announces version 1.0 of Stone Soup: the open-source tracking and state estimation framework. We highlight key elements of the framework and outline example applications and community activities.Stone Soup is engineered with modularity and encapsulation at its heart. This means that its many components can be put together in any number of ways to build, compare, and assure almost any type of multi-target tracking and fusion algorithm. Since its inception in 2017, it has aimed to provide the target tracking and state estimation community with an open, easy-to-deploy framework to develop and assess the performance of different types of trackers. Now, through repeated application in many use cases, implementation of a wide variety of algorithms, multiple beta releases, and contributions from the community, the framework has reached a stable point.In announcing this release, we hope to encourage additional adoption and further contributions to the toolkit. We also acknowledge and express appreciation for the many contributions of time and expertise donated by the tracking and fusion community.