Source term estimation of a hazardous release remains a topic of significant interest in the robotics and state estimation communities, with application to many safety critical scenarios including gas or nuclear release, locating suspicious smells or response to emergency incidents. Limited sensing resources and time constraints mean that deciding on how to act in order to improve efficiency of estimation is also of significant interest. This paper has two main focuses: a sequential Monte Carlo technique for performing source term estimation from gas concentration measurements taken on a mobile sensor platform and a Monte Carlo tree search (MCTS) framework to perform sensor motion planning to maximise Kullback-Leibler divergence (KLD). Both algorithms are implemented in the open source tracking and estimation framework: Stone Soup, creating several key contributions to this Python based toolkit. The presented algorithm demonstrates superior performance when compared to a greedy myopic alternative when considering source position estimation error, release rate error and successful rate performance measures.

Automating sensing processes is of high interest to both the target tracking and the control community. Active sensing is focused on solving this task, usually with information based or task driven selection of optimal sensing actions. This paper presents an active sensing formulation that combines task based, in the form of standoff tracking, and information based active sensing by implementing the dual control for exploitation and exploration (DCEE) concept to control a mobile sensor platform with a limited field-of-view. The DCEE based cost function is integrated into the Monte Carlo tree search (MCTS) framework for non-myopic decision making. Using the Bernoulli particle filter for single target tracking with bearing-only measurements, the DCEE observer control method is benchmarked against the popular Rényi divergence information metric with two different parameterisations. Whilst the Rényi divergence performs marginally better when considering existence estimation, spatial results clearly demonstrate that our formulation is able to outperform the benchmark algorithm with improved target localisation performance resulting from outmanoeuvring of the target.

In this paper, a distributed method is proposed for training multiple generative adversarial networks (GANs) with private data sets via a game-theoretic approach. To facilitate the requirement of privacy protection, distributed training algorithms offer a promising solution to learn global models without sample exchanges. Existing studies have mainly concentrated on training neural networks using pure cooperation strategies, which are not suitable for GANs. This paper develops a new framework for distributed GANs, where two groups of discriminators and generators are involved in a zero-sum game. Under connected graphs, such a framework is reformulated as a constrained minmax optimisation problem. Then, a fully distributed training algorithm is proposed without exchanging any private data samples. The convergence of the proposed algorithm is established via advanced consensus and optimisation techniques. Simulation studies are presented to validate the effectiveness of the proposed framework and algorithm.