Geo-location Requirements for UXO Discrimination (Guidance Document)

Bell, Thomas H.

doi:10.21236/ada486861

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…Classification based on inverted geophysical data requires centimeter-level sensor positioning when surveying a target (Bell, 2005). Because of the presence of trees, GPS cannot guarantee such accuracy.…”

Section: Geolocationmentioning

confidence: 99%

Man-Portable Vector EMI Sensor for Full UXO Characterization

Lhomme¹

2012

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Standard Form 298 (Rev. 8/98) REPORT DOCUMENTATION PAGEPrescribed by ANSI Std. Z39.18 Form Approved OMB No. 0704-0188The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to the Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION. 1. REPORT DATE (DD-MM-YYYY)2 Approved for public release; distribution is unlimitedThe Man-Portable Vector electromagnetic induction sensor is a new-generation instrument designed to extend classification of unexploded ordnance (UXO) to sites where vegetation or terrain limit access to vehicle-based advanced geophysical platforms. The MPV participated in the ESTCP live-site demonstration at former Camp Beale, California in June 2011, where data were collected in cued interrogation mode over 911 anomalies at a site with variable forest density and sloped terrain. Data were distributed to ESTCP partners for analysis and processed as part of this study. Demonstration objectives were attained. All buried targets of interest were detected and recommended for excavation through geophysical inversion and statistical classification. The prioritized dig list achieved over 80% reduction in unnecessary clutter excavation. Sensor positioning solely relied on the MPV-dedicated, prototype beacon positioning system. The method proved to be reliable and accurate within classification requirements (1-2 cm accuracy). The MPV technology will be involved in further ESTCP demonstrations in 2012.

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Section: Geolocationmentioning

confidence: 99%

Man-Portable Vector EMI Sensor for Full UXO Characterization

Lhomme¹

2012

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“…These detection systems require very accurate positioning (or geolocation) and orientation in order to discriminate the candidate Unexploded Ordnance from non-hazardous clutter when inverting magnetic or EMI data. For example, Bell (2005) argued that centimetre-level position accuracy was required for successful detection and discrimination using EM61 Hand Held (EM61-HH) data. Billings et al (2007) showed good discrimination performance given position errors of the order of 2 to 5 cm.…”

Section: Introductionmentioning

confidence: 99%

Rao-Blackwellized Unscented Particle Filter for a Handheld Unexploded Ordnance Geolocation System using IMU/GPS

Lee

Jekeli

2011

J. Navigation

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The existence of Unexploded Ordnance (UXO) is a serious environmental hazard, especially in areas being converted from military to civilian use. The detection and discrimination performance of UXO detectors depends on the sensor technology as well as on the processing methodology that inverts the data to infer UXO. The detection systems, typically electromagnetic induction (EMI) devices, require very accurate positioning (or geolocation) in order to discriminate candidate UXO from non-hazardous items. For this paper, a handheld geolocation system based on a tactical-grade IMU, such as the HG1900, was tested in the laboratory over a small, metre-square area in sweep and swing modes. A camera position system was used to emulate GPS or alternative ground-based external ranging systems that control positioning errors. The proposed integration algorithm is a combination of linear filtering (Extended Kalman Filter) and nonlinear, also non-Gaussian filtering (Unscented Particle Filter) in the form of the Rao-Blackwellized Particle Filter (RBPF). The test results show that the position accuracy was improved by applying nonlinear filterbased smoothing techniques in both the straight and curved sections of the sweep and swing trajectories. K E Y W O R D S 1. Kalman Filter. 2. Rao-Blackwellized Particle Filter. 3. UXO. 4. Hand-held Geolocation system.

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“…From this standpoint, GPS is representative of all ranging systems. However, the temporal resolution of GPS kinematic positioning (1 Hz, typically) is less than for some other systems, such as laser ranging, which diminishes the efficiency of UXO detection systems that pass over target sites at high speed (Bell, 2005).…”

Section: Introductionmentioning

confidence: 99%

Neural Network Aided Adaptive Filtering and Smoothing for an Integrated INS/GPS Unexploded Ordnance Geolocation System

Lee

Jekeli

2010

J. Navigation

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The precise geolocation of buried unexploded ordnance (UXO) is a significant component of the detection, characterization, and remediation process. Traditional geolocation methods associated with these procedures are inefficient in helping to distinguish buried UXO from relatively harmless geologic magnetic sources or anthropic clutter items such as exploded ordnance fragments and agricultural or industrial artefacts. The integrated INS/GPS geolocation system can satisfy both high spatial resolution and robust, uninterrupted positioning requirements for successful UXO detection and characterization. To maximize the benefits from this integration, non-linear filtering strategies (such as the unscented Kalman filter) have been developed and tested using laboratory data. In addition, adaptive filters and smoothers have been designed to address variable or inaccurate a priori knowledge of the process noise of the system during periods of GPS unavailability. In this paper, we study and compare the improvement in the geolocation accuracy when the neural network approach is applied to aid the adaptive versions of the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The test results show that the neural network based filters can improve overall position accuracy and can homogenize the performance of the integrated system over a range of relatively quiet to dynamic environments. Navigation-grade and medium-grade IMUs were compared and, with standard smoothing applied to the new filters, geolocation accuracy of 5 cm (13 cm) was achieved with the navigation-(medium-) grade unit within 8-second intervals that lack external control, which is at or close to the area-mapping accuracy requirement for UXO detection. K E Y

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Geo-location Requirements for UXO Discrimination (Guidance Document)

Cited by 7 publications

References 13 publications

Man-Portable Vector EMI Sensor for Full UXO Characterization

Man-Portable Vector EMI Sensor for Full UXO Characterization

Rao-Blackwellized Unscented Particle Filter for a Handheld Unexploded Ordnance Geolocation System using IMU/GPS

Neural Network Aided Adaptive Filtering and Smoothing for an Integrated INS/GPS Unexploded Ordnance Geolocation System

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