Online Target Localization using Adaptive Belief Propagation in the HMM Framework

TL;DR

This paper introduces an adaptive belief propagation approach within an HMM framework for online target localization, improving computational efficiency and flexibility in complex, high-resolution scenarios using a Bayesian method that does not rely on Gaussian assumptions.

Contribution

It presents a novel adaptive sample space-based Viterbi algorithm with belief propagation inspired by k-d Tree structures, enhancing efficiency in high-resolution target localization.

Findings

Significant reduction in computational resources required.

Effective localization in large and high-resolution spaces.

Robust performance demonstrated with UWB sensor network experiments.

Abstract

This paper proposes a novel adaptive sample space-based Viterbi algorithm for target localization in an online manner. The method relies on discretizing the target's motion space into cells representing a finite number of hidden states. Then, the most probable trajectory of the tracked target is computed via dynamic programming in a Hidden Markov Model (HMM) framework. The proposed method uses a Bayesian estimation framework which is neither limited to Gaussian noise models nor requires a linearized target motion model or sensor measurement models. However, an HMM-based approach to localization can suffer from poor computational complexity in scenarios where the number of hidden states increases due to high-resolution modeling or target localization in a large space. To improve this poor computational complexity, this paper proposes a belief propagation in the most probable belief space with a low to high-resolution sequentially, reducing the required resources significantly. The proposed method is inspired by the k-d Tree algorithm (e.g., quadtree) commonly used in the computer vision field. Experimental tests using an ultra-wideband (UWB) sensor network demonstrate our results.