Stochastic Dynamics of and Collision Prediction for Low Altitude Earth Satellites View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-09

AUTHORS

Adam T. Rich, Kenneth J. Stuart, William E. Wiesel

ABSTRACT

Air drag B∗ factors from Earth satellite element sets often show the characteristic near Gaussian distribution and autocorrelation exponential decay typical of a Gauss-Markov process. Assuming the “most current” set of orbital elements are correct, earlier elements can be used to construct covariance matrices as a function of prediction time into the future. If resolved in cylindrical orbit frame coordinates, these are remarkably structured, essentially showing only in-track error growth. Often the in-track position covariance element growth follows a fourth power in time rule, and is definitely forced by the uncertainty in the air drag factor. This observation is confirmed both by perturbation theory and by modeling stochastic state covariance propagation. Realizing that almost all error growth under the SGP4 model is in track, the Cosmos 2251/Iridium 33 event is reexamined. While a collision prediction from the last elements shows a minimum miss distance of about 700 m, those same elements show a closest approach distance of the orbits of only 32 m. Given large in-track uncertainty, minimum orbit separation may be a much more reliable metric for maneuver decisions. More... »

PAGES

307-320

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40295-018-0129-9

DOI

http://dx.doi.org/10.1007/s40295-018-0129-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104564488


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "United States Air Force", 
          "id": "https://www.grid.ac/institutes/grid.453002.0", 
          "name": [
            "USAF, Washington, D.C., USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rich", 
        "givenName": "Adam T.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "USAF, Los Angeles AFB, El Segundo, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stuart", 
        "givenName": "Kenneth J.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Air Force Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.427848.5", 
          "name": [
            "Department of Aeronautics and Astronautics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wiesel", 
        "givenName": "William E.", 
        "id": "sg:person.07734045071.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07734045071.29"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.asr.2004.02.020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003845143"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asr.2013.03.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008054155"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asr.2006.12.025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011026783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/ja083ia06p02637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025831666"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2004ja010585", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027289436"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asr.2015.05.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047276874"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asr.2017.02.038", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084060205"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2514/4.861741", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095881270"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-09", 
    "datePublishedReg": "2018-09-01", 
    "description": "Air drag B\u2217 factors from Earth satellite element sets often show the characteristic near Gaussian distribution and autocorrelation exponential decay typical of a Gauss-Markov process. Assuming the \u201cmost current\u201d set of orbital elements are correct, earlier elements can be used to construct covariance matrices as a function of prediction time into the future. If resolved in cylindrical orbit frame coordinates, these are remarkably structured, essentially showing only in-track error growth. Often the in-track position covariance element growth follows a fourth power in time rule, and is definitely forced by the uncertainty in the air drag factor. This observation is confirmed both by perturbation theory and by modeling stochastic state covariance propagation. Realizing that almost all error growth under the SGP4 model is in track, the Cosmos 2251/Iridium 33 event is reexamined. While a collision prediction from the last elements shows a minimum miss distance of about 700 m, those same elements show a closest approach distance of the orbits of only 32 m. Given large in-track uncertainty, minimum orbit separation may be a much more reliable metric for maneuver decisions.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s40295-018-0129-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1135898", 
        "issn": [
          "0021-9142", 
          "2195-0571"
        ], 
        "name": "The Journal of the Astronautical Sciences", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "65"
      }
    ], 
    "name": "Stochastic Dynamics of and Collision Prediction for Low Altitude Earth Satellites", 
    "pagination": "307-320", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "dbc6ae567553757b03f00676256c2e9a05c3e3d26415650708b42d62c2b7a2d7"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s40295-018-0129-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1104564488"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s40295-018-0129-9", 
      "https://app.dimensions.ai/details/publication/pub.1104564488"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T13:32", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8659_00000604.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs40295-018-0129-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s40295-018-0129-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s40295-018-0129-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40295-018-0129-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40295-018-0129-9'


 

This table displays all metadata directly associated to this object as RDF triples.

102 TRIPLES      21 PREDICATES      35 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s40295-018-0129-9 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Naf5d8728033d48e4b2de0546f76b6b78
4 schema:citation https://doi.org/10.1016/j.asr.2004.02.020
5 https://doi.org/10.1016/j.asr.2006.12.025
6 https://doi.org/10.1016/j.asr.2013.03.010
7 https://doi.org/10.1016/j.asr.2015.05.038
8 https://doi.org/10.1016/j.asr.2017.02.038
9 https://doi.org/10.1029/2004ja010585
10 https://doi.org/10.1029/ja083ia06p02637
11 https://doi.org/10.2514/4.861741
12 schema:datePublished 2018-09
13 schema:datePublishedReg 2018-09-01
14 schema:description Air drag B∗ factors from Earth satellite element sets often show the characteristic near Gaussian distribution and autocorrelation exponential decay typical of a Gauss-Markov process. Assuming the “most current” set of orbital elements are correct, earlier elements can be used to construct covariance matrices as a function of prediction time into the future. If resolved in cylindrical orbit frame coordinates, these are remarkably structured, essentially showing only in-track error growth. Often the in-track position covariance element growth follows a fourth power in time rule, and is definitely forced by the uncertainty in the air drag factor. This observation is confirmed both by perturbation theory and by modeling stochastic state covariance propagation. Realizing that almost all error growth under the SGP4 model is in track, the Cosmos 2251/Iridium 33 event is reexamined. While a collision prediction from the last elements shows a minimum miss distance of about 700 m, those same elements show a closest approach distance of the orbits of only 32 m. Given large in-track uncertainty, minimum orbit separation may be a much more reliable metric for maneuver decisions.
15 schema:genre research_article
16 schema:inLanguage en
17 schema:isAccessibleForFree false
18 schema:isPartOf N779f0afc9e234983923046e97fffbb82
19 N82519833f40f41e98a8a6402a84d4871
20 sg:journal.1135898
21 schema:name Stochastic Dynamics of and Collision Prediction for Low Altitude Earth Satellites
22 schema:pagination 307-320
23 schema:productId N631479d69b584003bf6a12f3c81419e4
24 N9df68d5ffd5e4f7b9c778bc2943984ce
25 Ndf3756dd54444d27910841018f6dafcf
26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104564488
27 https://doi.org/10.1007/s40295-018-0129-9
28 schema:sdDatePublished 2019-04-10T13:32
29 schema:sdLicense https://scigraph.springernature.com/explorer/license/
30 schema:sdPublisher N6b697762c3c54dbab7eaac1abb949b87
31 schema:url https://link.springer.com/10.1007%2Fs40295-018-0129-9
32 sgo:license sg:explorer/license/
33 sgo:sdDataset articles
34 rdf:type schema:ScholarlyArticle
35 N0065c3dbe95545dfb2269f6706441857 rdf:first sg:person.07734045071.29
36 rdf:rest rdf:nil
37 N3e26d233b6484191a3777a4e56158790 schema:affiliation https://www.grid.ac/institutes/grid.453002.0
38 schema:familyName Rich
39 schema:givenName Adam T.
40 rdf:type schema:Person
41 N631479d69b584003bf6a12f3c81419e4 schema:name doi
42 schema:value 10.1007/s40295-018-0129-9
43 rdf:type schema:PropertyValue
44 N6b697762c3c54dbab7eaac1abb949b87 schema:name Springer Nature - SN SciGraph project
45 rdf:type schema:Organization
46 N72685716571f4ace972da4cdf36b140c rdf:first Na3d8475a2d7646a5a49e85b8104eed37
47 rdf:rest N0065c3dbe95545dfb2269f6706441857
48 N779f0afc9e234983923046e97fffbb82 schema:volumeNumber 65
49 rdf:type schema:PublicationVolume
50 N82519833f40f41e98a8a6402a84d4871 schema:issueNumber 3
51 rdf:type schema:PublicationIssue
52 N9df68d5ffd5e4f7b9c778bc2943984ce schema:name readcube_id
53 schema:value dbc6ae567553757b03f00676256c2e9a05c3e3d26415650708b42d62c2b7a2d7
54 rdf:type schema:PropertyValue
55 Na3d8475a2d7646a5a49e85b8104eed37 schema:affiliation Nf43846d6fc574715ba59f43857c62c84
56 schema:familyName Stuart
57 schema:givenName Kenneth J.
58 rdf:type schema:Person
59 Naf5d8728033d48e4b2de0546f76b6b78 rdf:first N3e26d233b6484191a3777a4e56158790
60 rdf:rest N72685716571f4ace972da4cdf36b140c
61 Ndf3756dd54444d27910841018f6dafcf schema:name dimensions_id
62 schema:value pub.1104564488
63 rdf:type schema:PropertyValue
64 Nf43846d6fc574715ba59f43857c62c84 schema:name USAF, Los Angeles AFB, El Segundo, CA, USA
65 rdf:type schema:Organization
66 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
67 schema:name Mathematical Sciences
68 rdf:type schema:DefinedTerm
69 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
70 schema:name Statistics
71 rdf:type schema:DefinedTerm
72 sg:journal.1135898 schema:issn 0021-9142
73 2195-0571
74 schema:name The Journal of the Astronautical Sciences
75 rdf:type schema:Periodical
76 sg:person.07734045071.29 schema:affiliation https://www.grid.ac/institutes/grid.427848.5
77 schema:familyName Wiesel
78 schema:givenName William E.
79 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07734045071.29
80 rdf:type schema:Person
81 https://doi.org/10.1016/j.asr.2004.02.020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003845143
82 rdf:type schema:CreativeWork
83 https://doi.org/10.1016/j.asr.2006.12.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011026783
84 rdf:type schema:CreativeWork
85 https://doi.org/10.1016/j.asr.2013.03.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008054155
86 rdf:type schema:CreativeWork
87 https://doi.org/10.1016/j.asr.2015.05.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047276874
88 rdf:type schema:CreativeWork
89 https://doi.org/10.1016/j.asr.2017.02.038 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084060205
90 rdf:type schema:CreativeWork
91 https://doi.org/10.1029/2004ja010585 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027289436
92 rdf:type schema:CreativeWork
93 https://doi.org/10.1029/ja083ia06p02637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025831666
94 rdf:type schema:CreativeWork
95 https://doi.org/10.2514/4.861741 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095881270
96 rdf:type schema:CreativeWork
97 https://www.grid.ac/institutes/grid.427848.5 schema:alternateName Air Force Institute of Technology
98 schema:name Department of Aeronautics and Astronautics, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson AFB, OH, USA
99 rdf:type schema:Organization
100 https://www.grid.ac/institutes/grid.453002.0 schema:alternateName United States Air Force
101 schema:name USAF, Washington, D.C., USA
102 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...