Xuezhi Wang, Christopher Gilliam, Allison Kealy, John Close,, Bill Moran
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引用次数: 3
Abstract
Robust aiding of inertial navigation systems in GNSS-denied environments is critical for the removal of accumulated navigation error caused by the drift and bias inherent in inertial sensors. One way to perform such an aiding uses matching of geophysical measurements, such as gravimetry, gravity gradiometry or magnetometry, with a known geo-referenced map. Although simple in concept, this map-matching procedure is challenging: The measurements themselves are noisy, their associated spatial location is uncertain, and the measurements may match multiple points within the map (i.e., non-unique solution). In this paper, we propose a probabilistic multiple-hypotheses tracker to solve the map-matching problem and allow robust inertial navigation aiding. Our approach addresses the problem both locally, via probabilistic data association, and temporally by incorporating the underlying platform kinematic constraints into the tracker. The estimated platform position from the output of map matching is then integrated into the navigation state using an unscented Kalman filter. Additionally, we present a statistical measure of local map information density — the map feature variability — and use it to weight the output covariance of the proposed algorithm. The effectiveness and robustness of the proposed algorithm are demonstrated using a navigation scenario involving gravitational map matching.
期刊介绍:
NAVIGATION is a quarterly journal published by The Institute of Navigation. The journal publishes original, peer-reviewed articles on all areas related to the science, engineering and art of Positioning, Navigation and Timing (PNT) covering land (including indoor use), sea, air and space applications. PNT technologies of interest encompass navigation satellite systems (both global and regional), inertial navigation, electro-optical systems including LiDAR and imaging sensors, and radio-frequency ranging and timing systems, including those using signals of opportunity from communication systems and other non-traditional PNT sources. Articles about PNT algorithms and methods, such as for error characterization and mitigation, integrity analysis, PNT signal processing and multi-sensor integration, are welcome. The journal also accepts articles on non-traditional applications of PNT systems, including remote sensing of the Earth’s surface or atmosphere, as well as selected historical and survey articles.