Particle filters are essential for state estimation in non-linear and non-Gaussian systems, with performance hinging on effective proposal distributions. This paper presents the implementation of Kalman Filter and No-U-Turn Sampler (NUTS) proposals within the Stone Soup Python framework. The Kalman Filter proposal offers computational efficiency for structured systems, while NUTS enables robust exploration of complex, highdimensional distributions. Benchmark evaluations demonstrate the complementary strengths of these methods, enhancing Stone Soup's capabilities for diverse state estimation challenges.

This paper introduces the Stone Soup Astrodynamics Plugin: a plugin for the open-source tracking and state estimation framework to deal with astrodynamics problems. Stone Soup has provided 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. Here, we detail a Stone Soup plugin for astrodynamics which contains useful functions and tools for state estimation in the orbital domain. The plugin also contains wrappers and integrations with popular packages for space domain problems, the European Space Agency's GODOT framework and the Orekit framework. The plugin adopts Stone Soup's goals of testable, trustworthy code, and contains user documentation and use-case examples. In introducing this plugin, we hope to encourage additional adoption and further contributions to the toolkit, as well as invite feedback for future development.

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.

In this paper, we discuss the derivations, implementations and performance of target tracking algorithms for a single-target single-sensor system estimating the state, covariance and existence probability of a target. Given the target exists, we simulate measurements of the target with a given probability of detection, along with false measurements (clutter) parametrised by a clutter density. We evaluate the performance of the algorithms by computing the area under Receiver Operating Characteristic (ROC) curves against a range of clutter density values. We give particular attention to the effectiveness of correctly inferring the presence or absence of the target. We select the Integrated Probabilistic Data Association Filter (IPDAF) and the Integrated Expected Likelihood Particle Filter (IELPF) algorithms, with the IELPF implementing a near-optimal proposal which uses the current scan of measurements as well as a prior proposal for comparison. Simulation results indicate the performance of the IPDAF exceeds that of the preexisting particle filter implementing a prior proposal, but a novel particle filter using a near-optimal proposal and a modest number of particles outperforms the IPDAF.

In this paper we describe a Sequential Monte Carlo (SMC) sampler that performs joint tracking and destination estimation of a target traveling along a known road network, as its journey progresses. The destination estimation is based on a simplistic model of driver intent, which assumes no prior knowledge of the history of visited destinations. The proposed algorithm is capable of refining the distribution of destinations that can be inferred from an incoming stream of position estimates. We compare the performance achieved by the proposed algorithm with a mainstay Particle Filter, demonstrating how the later suffers greatly from sample impoverishment, therefore necessitating an ever increasing number of particles as the number of possible destinations increases, while showcasing that the issue is significantly mitigated by the proposed SMC Sampler.