{"project":{"acronym":"","projectId":88643,"title":"Multitarget Approaches to Robust Navigation","primaryTaxonomyNodes":[{"taxonomyNodeId":10974,"taxonomyRootId":8816,"parentNodeId":10973,"level":3,"code":"TX17.2.1","title":"Onboard Navigation Algorithms","definition":"This area covers algorithms (and their associated flight software) for autonomous onboard estimation of flight path/orbit/trajectory parameters and associated uncertainties from navigation sensor measurements.","exampleTechnologies":"Algorithms for optical navigation, terrain relative navigation, autonomous rendezvous and docking, autonomous hazard detection and avoidance, autonomous space-based navigation (optical or Global Positioning System (GPS) Cislunar), X-ray navigation, Simultaneous Localization and Mapping (SLAM), light detection and ranging (LIDAR)-based navigation, inertial navigation (translation) filter, inertial attitude estimation filter, ascent vehicle filter, Earth-independent deep space navigation, celestial navigation, landmark navigation, X-ray pulsar navigation, vehicle-relative navigation (translation) filter, vehicle-relative attitude filter, swarm navigation, angles-only navigation, double line of sight navigation, small body prox ops and landing filter","hasChildren":false,"hasInteriorContent":true}],"startTrl":2,"currentTrl":3,"endTrl":3,"benefits":"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.","description":"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.","startYear":2016,"startMonth":8,"endYear":2018,"endMonth":12,"statusDescription":"Completed","principalInvestigators":[{"contactId":281091,"canUserEdit":false,"firstName":"Kyle","lastName":"DeMars","fullName":"Kyle J Demars","fullNameInverted":"DeMars, Kyle J","middleInitial":"J","primaryEmail":"demarsk@mst.edu","publicEmail":false,"nacontact":false}],"programDirectors":[{"contactId":84634,"canUserEdit":false,"firstName":"Claudia","lastName":"Meyer","fullName":"Claudia M Meyer","fullNameInverted":"Meyer, Claudia M","middleInitial":"M","primaryEmail":"claudia.m.meyer@nasa.gov","publicEmail":true,"nacontact":false}],"programExecutives":[{"contactId":84634,"canUserEdit":false,"firstName":"Claudia","lastName":"Meyer","fullName":"Claudia M Meyer","fullNameInverted":"Meyer, Claudia M","middleInitial":"M","primaryEmail":"claudia.m.meyer@nasa.gov","publicEmail":true,"nacontact":false}],"programManagers":[{"contactId":183514,"canUserEdit":false,"firstName":"Hung","lastName":"Nguyen","fullName":"Hung D Nguyen","fullNameInverted":"Nguyen, Hung D","middleInitial":"D","primaryEmail":"hung.d.nguyen@nasa.gov","publicEmail":true,"nacontact":false}],"projectManagers":[{"contactId":78655,"canUserEdit":false,"firstName":"Christopher","lastName":"D'Souza","fullName":"Christopher N D'souza","fullNameInverted":"D'Souza, Christopher N","middleInitial":"N","primaryEmail":"chris.dsouza-1@nasa.gov","publicEmail":true,"nacontact":false}],"coInvestigators":[{"contactId":197514,"canUserEdit":false,"firstName":"James","lastName":"Mccabe","fullName":"James S Mccabe","fullNameInverted":"Mccabe, James S","middleInitial":"S","primaryEmail":"james.s.mccabe@nasa.gov","publicEmail":true,"nacontact":false}],"website":"","libraryItems":[],"transitions":[{"transitionId":75898,"projectId":88643,"transitionDate":"2018-12-01","path":"Closed Out","details":"This project has developed a high-performance, 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. This has been achieved via the following novel developments: • Development of a consider framework within the Bayesian paradigm to obtain knowledge of the entire probability density function of interest rather than being restricted to mean and covariance representations. • Development of a square-root formulation of the Bayesian consider framework and characterize sensor failures to produce a numerically robust and stable autonomous navigation filter implementation. • Generalization of the Bayesian consider estimation framework into the multitarget domain to enhance autonomous navigation, hazard avoidance, and rendezvous, docking, and landing capabilities. Reimagine the terrain-aided navigation concept as a multitarget tracking problem and utilize emerging tools based in finite set statistics to rigorously model the challenging stochastic motion and measurement properties of the studied problems. • Unification of the developed technologies using decentralized fusion into a robust 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 has contributed a large step forward with respect to enhancing the navigation capabilities of NASA's space vehicles and planetary landing missions.","infoText":"Closed out","infoTextExtra":"","dateText":"December 2018"}],"responsibleMd":{"acronym":"STMD","canUserEdit":false,"city":"","external":false,"linkCount":0,"organizationId":4875,"organizationName":"Space Technology Mission Directorate","organizationType":"NASA_Mission_Directorate","naorganization":false,"organizationTypePretty":"NASA Mission Directorate"},"program":{"acronym":"STRG","active":true,"description":"
\tThe Space Technology Research Grants Program will accelerate the development of "push" technologies to support the future space science and exploration needs of NASA, other government agencies and the commercial space sector. Innovative efforts with high risk and high payoff will be encouraged. The program is composed of two competitively awarded components.
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