Improved Filter Strategies for Precise Geolocation of Unexploded Ordnance using IMU/GPS Integration

Abstract

Efficient and precise geolocation can be achieved by integrating a ranging system, such as GPS, with inertial sensors in order to bridge short outages, enhance accuracy degradation, and increase the temporal resolution in the ranging system. Optimal integration depends on appropriate filter methods that can accommodate the particular short-term dynamics experienced by platforms, such as UXO ground-based detection systems. The traditional extended Kalman filter was designed to integrate data from a linearized system excited by Gaussian noise. We compared this filter to modern filters that obviate these prerequisites, including the unscented Kalman filter, the particle filter, and adaptive variations thereof, using simulated IMU/ranging systems that follow a typical trajectory with both straight and curved segments. The unscented filter performed significantly better than the extended Kalman filter, particularly over the curved segments, yielding up to 50% improvement in the position accuracy using medium-grade inertial measurement units. Similar improvement was obtained for the unscented particle filter, and its adaptive variant, over the unscented Kalman filter (which performed comparably to the extended Kalman filter) when the statistical distribution of the IMU noise was non-symmetric (i.e., essentially non-Gaussian). While the few-centimetre geolocation accuracy goal for highly dynamic UXO characterization applications remains a challenge if tactical grade IMUs are integrated with a significantly degraded ranging system, using filters appropriate to the inherent nonlinear dynamics and potential non-Gaussian nature of the sensor noise tend to reduce overall errors compared to the traditional filter.

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

“…However, the RBPS utilized only 20 samples (particles) for the nonlinear (particle) filter part. Although there is still room for some position accuracy improvement by increasing the number of particles, it will not yield significant improvements (Lee and Jekeli, 2009). Therefore, the RBPS can produce (slightly) better or comparable results compared to the UPS with only 10% of the number of particles used by the UPS.…”

“…For this purpose, the Extended Kalman Filter (EKF) has been used as the conventional algorithm to optimally integrate inertial and GPS positioning data (Farrell and Barth, 1998; Rogers, 2000; Jekeli, 2000; Titterton and Weston, 2004). However, newly introduced non-linear filters such as the Unscented Kalman Filter (UKF) and the Unscented Particle Filter (UPF) can deal better with the non-linear nature of the positioning dynamics as well as the potential non-Gaussian statistics of the instrument errors (Lee and Jekeli, 2009).…”

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

“…The initial states and covariance have negligible influence as the filter processes more and more data. The UT parameters only affect the higher order terms of the non-linear model and have little impact on the accuracy of the estimated position (Lee and Jekeli, 2009). Therefore, usually an innovation-based covariance matching algorithm is employed in order to tune the Q and R matrices of the Kalman filter.…”

Can Low-Cost Road Vehicles Positioning Systems Fulfil Accuracy Specifications of New ADAS Applications?

“…In this sense, the use of Kalman filtering is a very widespread solution in order to improve the positioning obtained using different measurement methods (Labrech et al, 2004; Rezaei and Sengupta, 2007; Toledo-Moreo et al, 2007; Xu et al, 2008, Jwo and Lai, 2009). Lee and Jekeli (2009) implemented and compared four different filtering algorithms: the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the unscented particle filter (UPF), and the adaptive unscented particle filter (AUPF). Baselga et al (2009) propose a data-filtering scheme to apply to inertial measurement systems raw data prior to the integration with GPS.…”