No abstract

Global Positioning System (GPS) has been used as a primary source of navigation in land and airborne applications. However, challenging environments cause GPS signal blockage or degradation, and prevent reliable and seamless positioning and navigation using GPS only. Therefore, multi-sensor based navigation systems have been developed to overcome the limitations of GPS by adding some forms of augmentation. The next step towards assured robust navigation is to combine information from multiple ground-users, to further improve the chance of obtaining reliable navigation and positioning information. Collaborative (or cooperative) navigation can improve the individual navigation solution in terms of both accuracy and coverage, and may reduce the system's design cost, as equipping all users with high performance multi-sensor positioning systems is not cost effective. Generally, 'Collaborative Navigation' uses inter-nodal range measurements between platforms (users) to strengthen the navigation solution. In the collaborative navigation approach, the inter-nodal distance vectors from the known or more accurate positions to the unknown locations can be established. Therefore, the collaborative navigation technique has the advantage in that errors at the user's position can be compensated by other known (or more accurate) positions of other platforms, and may result in the improvement of the navigation solutions for the entire group of users. In this paper, three statistical network-based collaborative navigation algorithms, the Restricted Least-Squares Solution (RLESS), the Stochastic Constrained Least-Squares Solution (SCLESS) and the Best Linear Minimum Partial Bias Estimation (BLIMPBE) are proposed and compared to the Kalman filter. The proposed statistical collaborative navigation algorithms for network solution show better performance than the Kalman filter.

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.

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.

It is widely known fact that the precise position and orientation is necessary for the UXO detection and discrimination. The primary geolocation system is a dualfrequency GPS receiver integrated with a three dimensional inertial measurement unit (IMU). This study focused on the optimal data processing techniques (wavelet de-noising, nonlinear based filtering, post-processing smoothing) which have been developed and proposed for the high precise geolocation of IMU/GPS system. We mounted two IMUs (HG1700 and HG1900) and one GPS receiver on NRL's vehicle-towed system which already has three Trimble GPS and one IMU (Crossbow 400C). The positions from HG1700 and HG1900 are estimated between control points separated in time by 2 and 4 seconds and compared to 1 Hz GPS control.