A5060067638- Target Tracking and Data Fusion in Sensor Networks
- Inertial Sensor and Navigation
- Robotics and Sensor-Based Localization
- Space Satellite Systems and Control
- Spacecraft Dynamics and Control
- Planetary Science and Exploration
- Air Traffic Management and Optimization
- Distributed Sensor Networks and Detection Algorithms
- Underwater Vehicles and Communication Systems
- Maritime Navigation and Safety
- Statistical Mechanics and Entropy
- Indoor and Outdoor Localization Technologies
- Advanced Optical Sensing Technologies
- Astro and Planetary Science
- Geophysics and Gravity Measurements
Draper Laboratory
2022-2025
Missouri University of Science and Technology
2018-2021
There has been a renewed focus in exploration of the lunar surface and maximizing scientific potential such missions is made possible part by minimizing time required to set up operations; that is, reducing transit on increasing landing precision with respect intended target. Established Safe Precise Landing–Integrated Capability Evolution (SPLICE) project requirements necessitate navigation filter architecture underlying models developed specifically these needs mind. To date, test flights...
There is currently renewed interest in robotic and crewed landers for a return to the lunar surface. Advanced guidance navigation algorithms are essential accurately delivering cargo crew safely moon successfully. This paper reports overall performance of an integrated set flown on terrestrial suborbital rocket up altitude approximately 100km. The algorithm consists onboard extended Kalman Filter (EKF) that ingests multiple sensor measurements, one which output from terrain relative (TRN)...
This study investigated the ability of a navigation filter to process multiple terrain-based sensors, such as slant-range, slant-speed, and terrain relative during descent-to-landing scenario estimate state landing vehicle. The filtering technique leveraged was based upon factorized form multiplicative extended Kalman filter, measurements were fused with star camera inertial measurement unit sensor returns position, velocity, attitude Monte Carlo simulations carried out assess performance...
This paper develops novel covariance and square-root factor formulations of a consider-neglect Kalman filter for navigation applications. The proposed partitions system parameters into three distinct categories: those to be estimated by the filter, whose contribution are considered without being explicitly estimated, with sufficiently low effect on such that their can neglected altogether. Discussion appropriate selection is provided specific attention given descent-to-landing navigation....
A new formulation of the Gaussian particle flow filter is presented using an information theoretic approach. The developed information-based form advances framework in two ways: it imparts physical meaning to dynamics and provides ability easily include modifications for a non-Bayesian update. An orbit determination simulation with high initial uncertainty used demonstrate consistent, robust performance situations where extended Kalman fails.
This work presents a new formulation of the Gaussian particle flow filter derived using an information theoretic approach. The developed addresses two problems with flow: lack inherent meaning in parameters and inability to easily include modifications for non-Bayesian update. Equivalency between is established linear, example. An orbit determination simulation high initial uncertainty used demonstrate consistent, robust performance situations where extended Kalman fails.
This paper develops a novel square-root formulation of the consider/neglect Kalman filter for navigation applications. The proposed partitions system parameters into three distinct categories: those to be estimated by filter, whose contribution can considered without explicitly estimating them, and with sufficiently low effect on such that their neglected altogether. Discussion appropriate selection consider neglect is provided specific attention given descent-to-landing navigation. Monte...