Nonparametric Bayesian time-series modeling and clustering of time-domain ground penetrating radar landmine responses

Kenneth D. Morton Jr.; Peter A. Torrione; Leslie Collins

doi:10.1117/12.850335

29 April 2010 Nonparametric Bayesian time-series modeling and clustering of time-domain ground penetrating radar landmine responses

Kenneth D. Morton Jr., Peter A. Torrione, Leslie Collins

Author Affiliations +

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 76642H (2010) https://doi.org/10.1117/12.850335
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Time domain ground penetrating radar (GPR) has been shown to be a powerful sensing phenomenology for detecting buried objects such as landmines. Landmine detection with GPR data typically utilizes a feature-based pattern classification algorithm to discriminate buried landmines from other sub-surface objects. In high-fidelity GPR, the time-frequency characteristics of a landmine response should be indicative of the physical construction and material composition of the landmine and could therefore be useful for discrimination from other non-threatening sub-surface objects. In this research we propose modeling landmine time-domain responses with a nonparametric Bayesian time-series model and we perform clustering of these time-series models with a hierarchical nonparametric Bayesian model. Each time-series is modeled as a hidden Markov model (HMM) with autoregressive (AR) state densities. The proposed nonparametric Bayesian prior allows for automated learning of the number of states in the HMM as well as the AR order within each state density. This creates a flexible time-series model with complexity determined by the data. Furthermore, a hierarchical non-parametric Bayesian prior is used to group landmine responses with similar HMM model parameters, thus learning the number of distinct landmine response models within a data set. Model inference is accomplished using a fast variational mean field approximation that can be implemented for on-line learning.

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Kenneth D. Morton Jr., Peter A. Torrione, and Leslie Collins "Nonparametric Bayesian time-series modeling and clustering of time-domain ground penetrating radar landmine responses", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76642H (29 April 2010); https://doi.org/10.1117/12.850335

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KEYWORDS

Autoregressive models

Land mines

Data modeling

Time-frequency analysis

General packet radio service

Statistical modeling

Mining

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