Fast Path Localization on Graphs via Multiscale Viterbi Decoding

TL;DR

This paper introduces a fast, scalable algorithm for localizing a moving agent's path on a graph, balancing accuracy and computational efficiency, with broad applicability to real-world tracking problems.

Contribution

It proposes a novel multiscale Viterbi decoding method that reduces complexity and provides universal error bounds applicable to all graphs and partition schemes.

Findings

Achieves 100x speedup over maximum likelihood methods.

Maintains comparable localization accuracy with significantly reduced computation.

Provides numerical bounds on localization error applicable to all graph types.

Abstract

We consider a problem of localizing a path-signal that evolves over time on a graph. A path-signal can be viewed as the trajectory of a moving agent on a graph in several consecutive time points. Combining dynamic programming and graph partitioning, we propose a path-localization algorithm with significantly reduced computational complexity. We analyze the localization error for the proposed approach both in the Hamming distance and the destination's distance between the path estimate and the true path using numerical bounds. Unlike usual theoretical bounds that only apply to restricted graph models, the obtained numerical bounds apply to all graphs and all non-overlapping graph-partitioning schemes. In random geometric graphs, we are able to derive a closed-form expression for the localization error bound, and a tradeoff between localization error and the computational complexity. Finally, we compare the proposed technique with the maximum likelihood estimate under the path constraint in terms of computational complexity and localization error, and show significant speedup (100 times) with comparable localization error (4 times) on a graph from real data. Variants of the proposed technique can be applied to tracking, road congestion monitoring, and brain signal processing.