1 citations · 1 across the 7 of their papers we have counts for
8 papers
Reinforcement Learning Trained Observer Control for Bearings-Only Tracking
Branko Ristic, Sanjeev Arulampalam
This paper develops a deep reinforcement learning based observer control policy for autonomous bearings-only tracking of a moving target. The observer manoeuvre problem is formulat…
Joint Estimation of Sea State and Vessel Parameters Using a Mass-Spring-Damper Equivalence Model
Ranjeet K. Tiwari, Daniel Sgarioto, Peter Graham +3
Real-time sea state estimation is vital for applications like shipbuilding and maritime safety. Traditional methods rely on accurate wave-vessel transfer functions to estimate wave…
A Sequential Game Framework for Target Tracking
Daniel Leal, Ngoc Hung Nguyen, Alex Skvortsov +2
This paper investigates the application of game-theoretic principles combined with advanced Kalman filtering techniques to enhance maritime target tracking systems. Specifically, t…
Credal Valuation Networks for Machine Reasoning Under Uncertainty
Branko Ristic, Alessio Benavoli, Sanjeev Arulampalam
Contemporary undertakings provide limitless opportunities for widespread application of machine reasoning and artificial intelligence in situations characterised by uncertainty, ho…
Game Theory in defence applications: a review
Edwin Ho, Arvind Rajagopalan, Alex Skvortsov +2
This paper presents a succinct review of attempts in the literature to use game theory to model decision making scenarios relevant to defence applications. Game theory has been pro…
Target tracking in the framework of possibility theory: The possibilistic Bernoulli filter
Branko Ristic, Jeremie Houssineau, Sanjeev Arulampalam
The Bernoulli filter is a Bayes filter for joint detection and tracking of a target in the presence of false and miss detections. This paper presents a mathematical formulation of…
Robust TMA using the possibility particle filter
Branko Ristic, Jeremie Houssineau, Sanjeev Arulampalam
The problem is target motion analysis (TMA), where the objective is to estimate the state of a moving target from noise corrupted bearings-only measurements. The focus is on recurs…
Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models
Michael Beard, Ba-Tuong Vo, Ba-Ngu Vo +1
The generalized labeled multi-Bernoulli (GLMB) is a family of tractable models that alleviates the limitations of the Poisson family in dynamic Bayesian inference of point processe…