Multitarget Approaches to Robust Navigation

Metadata Updated: November 12, 2020

The performance, stability, and statistical consistency of a vehicle's navigation algorithm are vitally important to the success and safety of its mission. Autonomous decision-making procedures employed on these missions rely upon these navigation solutions, and, in order for the autonomous vehicle to make appropriate decisions, the vehicle must be well-informed. With this in mind, the goal of this work is to develop a high-performance, robust navigation framework that applies to a wide variety of active NASA projects, such as Orion, Osiris-REX, and ALHAT, to name a few. Specifically, this project will develop a robust navigation framework using novel, state-of-the-art multitarget tracking techniques to perform autonomous multitarget navigation for spacecraft, planetary landers, and rovers in the presence of unmodeled effects, imperfect data, and sensor anomalies. The objectives for achieving this include:

1) Develop a consider-based framework under the Bayesian single-target paradigm that accounts for imperfect sensing and unmodeled system effects in the interest of obtaining a more complete statistical description of the true distribution of the state of a vehicle.

2) Develop a square-root formulation of the developed Bayesian consider framework in the interest of numerical robustness and stability.

3) Lift the Bayesian consider techniques into the multitarget domain to enhance autonomous navigation, hazard avoidance, and rendezvous, docking, and landing capabilities by enabling multi-feature detection, tracking, identification, and classification.

4) Unify the results of the previous three objectives into a single, robust multitarget navigation framework that can be applied to a wide variety of autonomous navigation problems to ensure safe and successful operation of future NASA missions while reducing the cost of algorithm development.

The completion of these objectives will produce a multitarget navigation framework with a broad relevance and an applicability to navigation for NASA's space vehicles and planetary landing missions. This research naturally produces a valuable analysis tool to evaluate the feasibility of different vehicle sensing configurations in a simulation environment, since the model-based approach comes with a strong degree of generality. The framework's wide applicability stands to reduce the development time of navigation architectures and thus reduce associated costs, while still permitting reliance on accurate, statistically descriptive navigation solutions.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Publisher Space Technology Mission Directorate
Unique Identifier Unknown
Identifier TECHPORT_88643
Data First Published 2020-08-01
Data Last Modified 2020-01-29
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Homepage URL
Program Code 026:027
Source Datajson Identifier True
Source Hash b7cc6e8dfcc54ef124a0ae910f4965d356a6afcc
Source Schema Version 1.1

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