Ohio State University. Division of Geodetic Science
Abstract
This work was supported by a project funded by the US Army Corps of Engineers,
Strategic Environment Research and Development Program, contract number W912HQ-
08-C-0044.This report was also submitted to the Graduate School of the Ohio State
University in partial fulfillment of the PhD degree in Geodetic Science.Unexploded ordnance (UXO) is the explosive weapons such as mines, bombs, bullets,
shells and grenades that failed to explode when they were employed. In North America,
especially in the US, the UXO is the result of weapon system testing and troop training
by the DOD. The traditional UXO detection method employs metal detectors which
measure distorted signals of local magnetic fields. Based on detected magnetic signals,
holes are dug to remove buried UXO. However, the detection and remediation of UXO
contaminated sites using the traditional methods are extremely inefficient in that it is
difficult to distinguish the buried UXO from the noise of geologic magnetic sources or
anthropic clutter items. The reliable discrimination performance of UXO detection
system depends on the employed sensor technology as well as on the data processing
methods that invert the collected data to infer the UXO. The detection systems require
very accurate positioning (or geolocation) of the detection units to detect and discriminate
the candidate UXO from the non-hazardous clutter, greater position and orientation
precision because the inversion of magnetic or EMI data relies on their precise relative
locations, orientation, and depth. The requirements of position accuracy for MEC
geolocation and characterization using typical state-of-the-art detection instrumentation
are classified according to levels of accuracy outlined in: the screening level with position
tolerance of 0.5 m (as standard deviation), area mapping (less than 0.05 m), and
characterize and discriminate level of accuracy (less than 0.02m).
The primary geolocation system is considered as a dual-frequency GPS integrated with a
three dimensional inertial measurement unit (IMU); INS/GPS system. Selecting the
appropriate estimation method has been the key problem to obtain highly precise
geolocation of INS/GPS system for the UXO detection performance in dynamic
environments. For this purpose, the Extended Kalman Filter (EKF) has been used as the
conventional algorithm for the optimal integration of INS/GPS system. However, the
newly introduced non-linear based filters can deal with the non-linear nature of the
positioning dynamics as well as the non-Gaussian statistics for the instrument errors, and
the non-linear based estimation methods (filtering/smoothing) have been developed and
proposed. Therefore, this study focused on the optimal estimation methods for the
highly precise geolocation of INS/GPS system using simulations and analyses of two
Laboratory tests (cart-based and handheld geolocation system).
First, the non-linear based filters (UKF and UKF) have been shown to yield superior
performance than the EKF in various specific simulation tests which are designed similar
to the UXO geolocation environment (highly dynamic and small area). The UKF yields
50% improvement in the position accuracy over the EKF particularly in the curved
sections (medium-grade IMUs case). The UKF also performed significantly better than
EKF and shows comparable improvement over the UKF when the IMU noise probability
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density function is symmetric and non-symmetric. Also, since the UXO detection
survey does not require the real-time operations, each of the developed filters was
modified to accommodate the standard Rauch-Tung-Striebel (RTS) smoothing algorithms.
The smoothing methods are applied to the typical UXO detection trajectory; the position
error was reduced significantly using a minimal number of control points. Finally, these
simulation tests confirmed that tactical-grade IMUs (e.g. HG1700 or HG1900) are
required to bridge gaps of high-accuracy ranging solution systems longer than 1 second.
Second, these result of the simulation tests were validated from the laboratory tests using
navigation-grade and medium-grade accuracy IMUs. To overcome inaccurate a priori
knowledge of process noise of the system, the adaptive filtering methods have been
applied to the EKF and UKF and they are called the AEKS and AUKS. The neural
network aided adaptive nonlinear filtering/smoothing methods (NN-EKS and NN-UKS)
which are augmented with RTS smoothing method were compared with the AEKS and
AUKS. Each neural network-aided, adaptive filter/smoother improved the position
accuracy in both straight and curved sections. The navigation grade IMU (H764G) can
achieve the area mapping level of accuracy when the gap of control points is about 8
seconds. The medium grade IMUs (HG1700 and HG1900) with NN-AUKS can
maintain less than 10cm under the same conditions as above. Also, the neural network
aiding can decrease the difference of position error between the straight and the curved
section. Third, in the previous simulation test, the UPF performed better than the other
filters. However since the UPF needs a large number of samples to represent the a
posteriori statistics in high-dimensional space, the RBPF can be used as an alternative to
avoid the inefficiency of particle filter. The RBPF is tailored to precise geolocation for
UXO detection using IMU/GPS system and yielded improved estimation results with a
small number of samples. The handheld geolocation system using HG1900 with a
nonlinear filter-based smoother can achieve the discrimination level of accuracy if the
update rate of control points is less than 0.5Hz and 1Hz for the sweep and swing
respectively. Also, the sweep operation is more preferred than the swing motion
because the position accuracy of the sweep test was better than that of the swing test