Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach

DOI10.1109/TSP.2018.2866386zbMATH Open1415.94335arXiv1611.03466OpenAlexW2964263262WikidataQ129400460 ScholiaQ129400460MaRDI QIDQ4622399

Sijia Gao, Vikram Krishnamurthy

Publication date: 12 February 2019

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Abstract: The aim of syntactic tracking is to classify spatio-temporal patterns of a target's motion using natural language processing models. In this paper, we generalize earlier work by considering a constrained stochastic context free grammar (CSCFG) for modeling patterns confined to a roadmap. The constrained grammar facilitates modeling specific directions and road names in a roadmap. We present a novel particle filtering algorithm that exploits the CSCFG model for estimating the target's patterns. This meta-level algorithm operates in conjunction with a base-level tracking algorithm. Extensive numerical results using simulated ground moving target indicator (GMTI) radar measurements show substantial improvement in target tracking accuracy.

Full work available at URL: https://arxiv.org/abs/1611.03466

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Detection theory in information and communication theory (94A13)

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