48 citations · 190 across the 24 of their papers we have counts for
31 papers
Robust Multi-Sensor Multi-Target Tracking Using Possibility Labeled Multi-Bernoulli Filter
Han Cai, Chenbao Xue, Jeremie Houssineau +1
With the increasing complexity of multiple target tracking scenes, a single sensor may not be able to effectively monitor a large number of targets. Therefore, it is imperative to…
Decentralised possibilistic inference with applications to target tracking
Jeremie Houssineau, Han Cai, Murat Uney +1
Fusing and sharing information from multiple sensors over a network is a challenging task, partly due to the absence of a foundational rule for fusing probability distributions tha…
Robust Bayesian inference in complex models with possibility theory
Jeremie Houssineau, David J. Nott
We propose a general solution to the problem of robust Bayesian inference in complex settings where outliers may be present. In practice, the automation of robust Bayesian analyses…
On Unbiased Score Estimation for Partially Observed Diffusions
Jeremy Heng, Jeremie Houssineau, Ajay Jasra
We consider the problem of statistical inference for a class of partially-observed diffusion processes, with discretely-observed data and finite-dimensional parameters. We construc…
Uncertainty modelling and computational aspects of data association
Jeremie Houssineau, Jiajie Zeng, Ajay Jasra
A novel solution to the smoothing problem for multi-object dynamical systems is proposed and evaluated. The systems of interest contain an unknown and varying number of dynamical o…
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…
Elements of asymptotic theory with outer probability measures
Jeremie Houssineau, Neil K. Chada, Emmanuel Delande
Outer measures can be used for statistical inference in place of probability measures to bring flexibility in terms of model specification. The corresponding statistical procedures…
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…
On Large Lag Smoothing for Hidden Markov Models
Jeremie Houssineau, Ajay Jasra, Sumeetpal S. Singh
In this article we consider the smoothing problem for hidden Markov models (HMM). Given a hidden Markov chain and observations , our objectiv…
On the loss of Fisher information in some multi-object tracking observation models
Jeremie Houssineau, Ajay Jasra, Sumeetpal S. Singh
The concept of Fisher information can be useful even in cases where the probability distributions of interest are not absolutely continuous with respect to the natural reference me…
Physics and Human-Based Information Fusion for Improved Resident Space Object Tracking
Emmanuel Delande, Jeremie Houssineau, Moriba Jah
Maintaining a catalog of Resident Space Objects (RSOs) can be cast in a typical Bayesian multi-object estimation problem, where the various sources of uncertainty in the problem -…
Fusion of finite set distributions: Pointwise consistency and global cardinality
Murat Üney, Jérémie Houssineau, Emmanuel Delande +2
A recent trend in distributed multi-sensor fusion is to use random finite set filters at the sensor nodes and fuse the filtered distributions algorithmically using their exponentia…
A linear algorithm for multi-target tracking in the context of possibility theory
Jeremie Houssineau
We present a modelling framework for multi-target tracking based on possibility theory and illustrate its ability to account for the general lack of knowledge that the target-track…
Parameter estimation with a class of outer probability measures
Jeremie Houssineau
We explore the interplay between random and deterministic phenomena using a representation of uncertainty based on the measure-theoretic concept of outer measure. The meaning of th…
Multilevel Monte Carlo for Smoothing via Transport Methods
Jeremie Houssineau, Ajay Jasra, Sumeetpal S. Singh
In this article we consider recursive approximations of the smoothing distribution associated to partially observed stochastic differential equations (SDEs), which are observed dis…
Sequential Monte Carlo algorithms for a class of outer measures
Jeremie Houssineau, Branko Ristic
Closed-form stochastic filtering equations can be derived in a general setting where probability distributions are replaced by some specific outer measures. In this article, we stu…
Identification of multi-object dynamical systems: consistency and Fisher information
Jeremie Houssineau, Sumeetpal S. Singh, Ajay Jasra
Learning the model parameters of a multi-object dynamical system from partial and perturbed observations is a challenging task. Despite recent numerical advancements in learning th…
A second-order PHD filter with mean and variance in target number
Isabel Schlangen, Emmanuel D. Delande, Jeremie Houssineau +1
The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters are popular solutions to the multi-target tracking problem due to their low complexity and ability to e…
Smoothing and filtering with a class of outer measures
Jeremie Houssineau, Adrian N. Bishop
Filtering and smoothing with a generalised representation of uncertainty is considered. Here, uncertainty is represented using a class of outer measures. It is shown how this repre…
Unbiased Multi-index Monte Carlo
Dan Crisan, Pierre Del Moral, Jeremie Houssineau +1
We introduce a new class of Monte Carlo based approximations of expectations of random variables such that their laws are only available via certain discretizations. Sampling from…
Bayesian data assimilation based on a family of outer measures
Jeremie Houssineau, Daniel E. Clark
A flexible representation of uncertainty that remains within the standard framework of probabilistic measure theory is presented along with a study of its properties. This represen…
On a representation of partially-distinguishable populations
Jeremie Houssineau, Daniel E. Clark
A representation of heterogeneous stochastic populations that are composed of sub-populations with different levels of distinguishability is introduced together with an analysis of…
Spawning Models for the CPHD Filter
Daniel S. Bryant, Emmanuel D. Delande, Steven Gehly +3
In its classical form, the Cardinalized Probability Hypothesis Density (CPHD) filter does not model the appearance of new targets through spawning, yet there are applications for w…
A few calculus rules for chain differentials
Daniel E. Clark, Jeremie Houssineau, Emmanuel D. Delande
This paper summarizes the core definitions and results regarding the chain differential for functions in locally convex topological vector spaces. In addition, it provides a few el…