Bayesian Mitigation of Sensor Position Errors to Improve Unexploded Ordnance Detection

• Published in: IEEE geoscience and remote sensing letters Vol. 5; no. 1; pp. 103 - 107
• Main Authors: Tantum, S.L., Yongli Yu, Collins, L.M.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayesian analysis; Bayesian estimation; Bayesian methods; Computer simulation; Electromagnetic coupling; Electromagnetic induction; electromagnetic induction (EMI); Electromagnetic interference; Electromagnetic measurements; Electromagnetic modeling; Estimates; Inversions; Mathematical models; Measurement uncertainty; Monte Carlo integration; Monte Carlo methods; Position measurement; Sensor phenomena and characterization; Sensors; Signal processing; Studies; Uncertainty; unexploded ordnance (UXO) detection; Bayesian estimation; Monte Carlo integration; unexploded ordnance (UXO) detection; electromagnetic induction (EMI)
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2007.912088

Phenomenological modeling coupled with statistical signal processing has been shown to significantly improve capabilities for discriminating unexploded ordnance (UXO) from benign clutter using electromagnetic induction (EMI) sensor data. The general premise underlying the majority of these coupled approaches is that a phenomenological model is fit to the measured data, and the parameters estimated from this model inversion, which characterize the interrogated target, are utilized in subsequent statistical signal processing algorithms to classify the target as either UXO or clutter. A potential limitation of this coupled approach is that the inversion has been shown to be sensitive to uncertainty associated with the sensor positions. When the measurement positions are uncertain, the inversion results are more variable, and consequently, discrimination performance degrades. In this letter, a Bayesian methodology is applied to estimate the desired features from the measured data. This method explicitly acknowledges that uncertainty in the sensor positions exists and incorporates this knowledge to find the maximum-likelihood feature estimates by integrating over the uncertain measurement positions. Due to the high dimensionality of the integration, Monte Carlo integration, a statistical technique to estimate the value of an integral, is employed. Simulation results show that this Bayesian approach in mitigating sensor position uncertainty produces features with lower variability and, therefore, provides improved discrimination performance.