This paper presents a Multiplicative Extended Kalman Filter (MEKF) framework using a state-of-the-art velocimeter Light Detection and Ranging (LIDAR) sensor for Terrain Relative Navigation (TRN) applications. The newly developed velocimeter LIDAR is capable of providing simultaneous position, Doppler velocity, and reflectivity measurements for every point in the point cloud. This information, along with pseudo-measurements from point cloud registration
techniques, a novel bulk velocity batch state estimation process and inertial measurement data, is fused within a traditional Kalman filter architecture. Results from extensive emulation robotics experiments performed at Texas A&M’s Land, Air, and Space Robotics (LASR) laboratory and Monte Carlo simulations are presented to evaluate the efficacy of the proposed
algorithms.
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CIS-LUNAR ORBIT DETERMINATION SENSITIVITY ANALYSIS WITH REFERENCE TO OBSERVATIONAL GEOMETRY
The sensitivity of the initial condition [epoch state] orbit determination program
with respect to ranging geometry is studied in this paper. Using the nonlinear
least squares orbit determination program with representative dynamics and sensor models, the effects of operational considerations, such as the drop of regular ranging, information are studied. A representation of the linearized error covariance of the epoch state as a function of observation epoch is presented to provide a visual inspection of the relative measures of accuracy of the state. Applications of this analysis are applied to an orbit estimation problem, whereby the orbit of an artificial satellite around the Moon is estimated using range and range rate observations from sites located on the Earth. Information-gain measures are presented to reveal optimal observation epochs and the influence of observational geometry. Comparisons are made between information-gain profiles determined from true measurement data and measurement data generated from a priori estimates.
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- Award ID(s):
- 1946890
- NSF-PAR ID:
- 10318614
- Date Published:
- Journal Name:
- 44th annual AAS Guidance, Navigation and Control Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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