144 citations · 430 across the 22 of their papers we have counts for
26 papers
Generalised Hyperbolic State-space Models for Inference in Dynamic Systems
Yaman Kındap, Simon Godsill
In this work we study linear vector stochastic differential equation (SDE) models driven by the generalised hyperbolic (GH) Lévy process for inference in continuous-time non-Gaussi…
Consensus-based Distributed Variational Multi-object Tracker in Multi-Sensor Network
Qing Li, Runze Gan, Simon Godsill
The growing need for accurate and reliable tracking systems has driven significant progress in sensor fusion and object tracking techniques. In this paper, we design two variationa…
Variational Tracking and Redetection for Closely-spaced Objects in Heavy Clutter: Supplementary Materials
Runze Gan, Qing Li, Simon Godsill
The non-homogeneous Poisson process (NHPP) is a widely used measurement model that allows for an object to generate multiple measurements over time. However, it can be difficult to…
Generalised shot noise representations of stochastic systems driven by non-Gaussian Lévy processes
Marcos Tapia Costa, Ioannis Kontoyiannis, Simon Godsill
We consider the problem of obtaining effective representations for the solutions of linear, vector-valued stochastic differential equations (SDEs) driven by non-Gaussian pure-jump…
Point process simulation of generalised hyperbolic Lévy processes
Yaman Kindap, Simon Godsill
Generalised hyperbolic (GH) processes are a class of stochastic processes that are used to model the dynamics of a wide range of complex systems that exhibit heavy-tailed behavior,…
Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data
Nermin Caber, Bashar I. Ahmad, Jiaming Liang +5
Monitoring drivers' mental workload facilitates initiating and maintaining safe interactions with in-vehicle information systems, and thus delivers adaptive human machine interacti…
Non-Gaussian Process Regression
Yaman Kındap, Simon Godsill
Standard GPs offer a flexible modelling tool for well-behaved processes. However, deviations from Gaussianity are expected to appear in real world datasets, with structural outlier…
Point process simulation of generalised inverse Gaussian processes and estimation of the Jaeger integral
Simon Godsill, Yaman Kındap
In this paper novel simulation methods are provided for the generalised inverse Gaussian (GIG) Lévy process. Such processes are intractable for simulation except in certain special…
Autonomous Tracking and State Estimation with Generalised Group Lasso
Rui Gao, Simo Särkkä, Rubén Claveria-Vega +1
We address the problem of autonomous tracking and state estimation for marine vessels, autonomous vehicles, and other dynamic signals under a (structured) sparsity assumption. The…
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information
Hugh Christensen, Simon Godsill, Richard E Turner
A Hidden Markov Model for intraday momentum trading is presented which specifies a latent momentum state responsible for generating the observed securities' noisy returns. Existing…
A Multi-Target Track-Before-Detect Particle Filter Using Superpositional Data in Non-Gaussian Noise
Nobutaka Ito, Simon Godsill
This paper proposes a novel particle filter for tracking time-varying states of multiple targets jointly from superpositional data, which depend on the sum of contributions of all…
The Lévy State Space Model
Simon Godsill, Marina Riabiz, Ioannis Kontoyiannis
In this paper we introduce a new class of state space models based on shot-noise simulation representations of non-Gaussian Lévy-driven linear systems, represented as stochastic di…
Nonasymptotic Gaussian Approximation for Inference with Stable Noise
Marina Riabiz, Tohid Ardeshiri, Ioannis Kontoyiannis +1
The results of a series of theoretical studies are reported, examining the convergence rate for different approximate representations of -stable distributions. Although they pla…
If and When a Driver or Passenger is Returning to Vehicle: Framework to Infer Intent and Arrival Time
Bashar I. Ahmad, Patrick M. Langdon, Simon J. Godsill +2
This paper proposes a probabilistic framework for the sequential estimation of the likelihood of a driver or passenger(s) returning to the vehicle and time of arrival, from the ava…
Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation with Mutual Coupling Effects
Matthew Hawes, Lyudmila Mihaylova, François Septier +1
The problem of estimating the dynamic direction of arrival of far field signals impinging on a uniform linear array, with mutual coupling effects, is addressed. This work proposes…
Unsupervised Nonlinear Spectral Unmixing based on a Multilinear Mixing Model
Qi Wei, Marcus Chen, Jean-Yves Tourneret +1
In the community of remote sensing, nonlinear mixing models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel nonline…
R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret +2
This paper proposes a robust fast multi-band image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. Following t…
Multi-Band Image Fusion Based on Spectral Unmixing
Qi Wei, Jose Bioucas-Dias, Nicolas Dobigeon +3
This paper presents a multi-band image fusion algorithm based on unsupervised spectral unmixing for combining a high-spatial low-spectral resolution image and a low-spatial high-sp…
A Bayesian Compressed Sensing Kalman Filter for Direction of Arrival Estimation
Matthew Hawes, Lyudmila Mihaylova, Francois Septier +1
In this paper, we look to address the problem of estimating the dynamic direction of arrival (DOA) of a narrowband signal impinging on a sensor array from the far field. The initia…
Bayesian Intent Prediction in Object Tracking Using Bridging Distributions
Bashar I. Ahmad, James K. Murphy, Patrick M. Langdon +1
In several application areas, such as human computer interaction, surveillance and defence, determining the intent of a tracked object enables systems to aid the user/operator and…
How Can Subsampling Reduce Complexity in Sequential MCMC Methods and Deal with Big Data in Target Tracking?
Allan De Freitas, François Septier, Lyudmila Mihaylova +1
Target tracking faces the challenge in coping with large volumes of data which requires efficient methods for real time applications. The complexity considered in this paper is whe…
Rao-Blackwellized particle smoothers for conditionally linear Gaussian models
Fredrik Lindsten, Pete Bunch, Simo Särkkä +2
Sequential Monte Carlo (SMC) methods, such as the particle filter, are by now one of the standard computational techniques for addressing the filtering problem in general state-spa…
A Bayesian Particle Filtering Method For Brain Source Localisation
Xi Chen, Simo Särkkä, Simon Godsill
In this paper, we explore the multiple source localisation problem in the cerebral cortex using magnetoencephalography (MEG) data. We model neural currents as point-wise dipolar so…
Approximations of the Optimal Importance Density using Gaussian Particle Flow Importance Sampling
Pete Bunch, Simon Godsill
Recently developed particle flow algorithms provide an alternative to importance sampling for drawing particles from a posterior distribution, and a number of particle filters base…