Latent Parameter Estimation in Fusion Networks Using Separable Likelihoods

TL;DR

This paper introduces a separable pseudo-likelihood approach for estimating latent parameters in multi-sensor state space models, enabling scalable network self-calibration even with many objects and measurement ambiguities.

Contribution

It proposes a more accurate separable pseudo-likelihood method and a Bayesian inference framework using belief propagation for scalable latent parameter estimation.

Findings

Demonstrates effectiveness of the method for network self-calibration

Shows improved accuracy over previous approaches

Validates approach with experiments on non-cooperative targets

Abstract

Multi-sensor state space models underpin fusion applications in networks of sensors. Estimation of latent parameters in these models has the potential to provide highly desirable capabilities such as network self-calibration. Conventional solutions to the problem pose difficulties in scaling with the number of sensors due to the joint multi-sensor filtering involved when evaluating the parameter likelihood. In this article, we propose a separable pseudo-likelihood which is a more accurate approximation compared to a previously proposed alternative under typical operating conditions. In addition, we consider using separable likelihoods in the presence of many objects and ambiguity in associating measurements with objects that originated them. To this end, we use a state space model with a hypothesis based parameterisation, and, develop an empirical Bayesian perspective in order to evaluate separable likelihoods on this model using local filtering. Bayesian inference with this likelihood is carried out using belief propagation on the associated pairwise Markov random field. We specify a particle algorithm for latent parameter estimation in a linear Gaussian state space model and demonstrate its efficacy for network self-calibration using measurements from non-cooperative targets in comparison with alternatives.