Loosely coupled GNSS/INS integration based on an auto regressive model in a data gap environment View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Dashuai Chai, Guoliang Chen, Shengli Wang, Xiushan Lu

ABSTRACT

A data gap of GNSS and INS may occur when data are collected by a vehicle. To obtain the pose information when this data gap appears, we use a combined auto regressive (AR) model for the forecasting of INS data so that the Strap-down Inertial Navigation System can still work. A forward process is initially implemented to forecast INS data using an AR model, and then inverse prediction is performed. Finally, the raw INS data are determined using forward and inverse results with different weights. The measurement data are applied to this method and the commercial software Inertial Explorer 8.60 (IE). The experimental result shows that the errors from the filtered results of the IE for loosely coupled and tightly coupled approaches reach the meter level after the data of the GNSS and INS are retrieved, and the error is at the meter level for conventional loosely coupled approach. Conversely, the maximum error from the proposed method is at the decimeter level. The smoother results have also been affected for the loosely coupled and tightly coupled approach of the IE before this data gap of GNSS and INS appears. However, a centimeter-level result can still be obtained via piecewise smoothing for the proposed method. The data gaps of 5 s and 10 s for GNSS and INS are simulated. These experiments show that the maximum errors of the smoother results are 0.4374 m and 4.0443 m for the proposed algorithm and these errors are better than the results for the loosely coupled and tightly coupled approach of the IE and the conventional loosely coupled approach. More... »

PAGES

1-25

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40328-018-0238-8

DOI

http://dx.doi.org/10.1007/s40328-018-0238-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1109915122


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/2008/0909", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geomatic Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "China University of Mining and Technology", 
          "id": "https://www.grid.ac/institutes/grid.411510.0", 
          "name": [
            "School of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), 221116, Xuzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chai", 
        "givenName": "Dashuai", 
        "id": "sg:person.013400322307.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013400322307.47"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "China University of Mining and Technology", 
          "id": "https://www.grid.ac/institutes/grid.411510.0", 
          "name": [
            "School of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), 221116, Xuzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Guoliang", 
        "id": "sg:person.010160540316.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010160540316.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong University of Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.412508.a", 
          "name": [
            "Institute of Ocean Engineering, Shandong University of Science and Technology, 266590, Qingdao, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Shengli", 
        "id": "sg:person.010745065467.57", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010745065467.57"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong University of Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.412508.a", 
          "name": [
            "Institute of Ocean Engineering, Shandong University of Science and Technology, 266590, Qingdao, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lu", 
        "givenName": "Xiushan", 
        "id": "sg:person.014367275547.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014367275547.29"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.3390/s121217372", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000264379"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-981-10-0934-1_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002355353", 
          "https://doi.org/10.1007/978-981-10-0934-1_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s037346331600031x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004075219"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10291-006-0050-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009138335", 
          "https://doi.org/10.1007/s10291-006-0050-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10291-010-0186-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015046937", 
          "https://doi.org/10.1007/s10291-010-0186-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/s120405134", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015724706"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.inffus.2010.01.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015789554"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1515/jag-2014-0021", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019725017"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.tust.2013.03.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024323949"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1179/1752270615y.0000000047", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027834804"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep08328", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033698849", 
          "https://doi.org/10.1038/srep08328"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0373463313000623", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038613333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10291-010-0198-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039777675", 
          "https://doi.org/10.1007/s10291-010-0198-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/s0373463314000307", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042165102"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/s16070944", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046851068"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/87.852915", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061241909"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/taes.2012.6178052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061485757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tits.2010.2052805", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061657695"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmech.2006.882988", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061692246"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpwrd.2016.2577140", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061775383"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1360/n972014-00789", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065073798"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5081/jgps.2.2.109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072570542"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/plans.2010.5507307", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093597498"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccais.2012.6466637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095455542"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cja.2017.12.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100091613"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "A data gap of GNSS and INS may occur when data are collected by a vehicle. To obtain the pose information when this data gap appears, we use a combined auto regressive (AR) model for the forecasting of INS data so that the Strap-down Inertial Navigation System can still work. A forward process is initially implemented to forecast INS data using an AR model, and then inverse prediction is performed. Finally, the raw INS data are determined using forward and inverse results with different weights. The measurement data are applied to this method and the commercial software Inertial Explorer 8.60 (IE). The experimental result shows that the errors from the filtered results of the IE for loosely coupled and tightly coupled approaches reach the meter level after the data of the GNSS and INS are retrieved, and the error is at the meter level for conventional loosely coupled approach. Conversely, the maximum error from the proposed method is at the decimeter level. The smoother results have also been affected for the loosely coupled and tightly coupled approach of the IE before this data gap of GNSS and INS appears. However, a centimeter-level result can still be obtained via piecewise smoothing for the proposed method. The data gaps of 5 s and 10 s for GNSS and INS are simulated. These experiments show that the maximum errors of the smoother results are 0.4374 m and 4.0443 m for the proposed algorithm and these errors are better than the results for the loosely coupled and tightly coupled approach of the IE and the conventional loosely coupled approach.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s40328-018-0238-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136254", 
        "issn": [
          "2213-5812", 
          "2213-5820"
        ], 
        "name": "Acta Geodaetica et Geophysica", 
        "type": "Periodical"
      }
    ], 
    "name": "Loosely coupled GNSS/INS integration based on an auto regressive model in a data gap environment", 
    "pagination": "1-25", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "e3f0a1b5179986b34c1606790af0cd1997d6be1dd163f9bb9f59c98e030780fa"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s40328-018-0238-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1109915122"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s40328-018-0238-8", 
      "https://app.dimensions.ai/details/publication/pub.1109915122"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T08:06", 
    "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/v90/0000000265_0000000265/records_67361_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs40328-018-0238-8"
  }
]
 

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/s40328-018-0238-8'

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/s40328-018-0238-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40328-018-0238-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40328-018-0238-8'


 

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

159 TRIPLES      21 PREDICATES      50 URIs      17 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s40328-018-0238-8 schema:about anzsrc-for:09
2 anzsrc-for:0909
3 schema:author Nb91599062d904059a6378b2a4b4dcb4a
4 schema:citation sg:pub.10.1007/978-981-10-0934-1_8
5 sg:pub.10.1007/s10291-006-0050-8
6 sg:pub.10.1007/s10291-010-0186-4
7 sg:pub.10.1007/s10291-010-0198-0
8 sg:pub.10.1038/srep08328
9 https://doi.org/10.1016/j.cja.2017.12.011
10 https://doi.org/10.1016/j.inffus.2010.01.003
11 https://doi.org/10.1016/j.tust.2013.03.007
12 https://doi.org/10.1017/s0373463313000623
13 https://doi.org/10.1017/s0373463314000307
14 https://doi.org/10.1017/s037346331600031x
15 https://doi.org/10.1109/87.852915
16 https://doi.org/10.1109/iccais.2012.6466637
17 https://doi.org/10.1109/plans.2010.5507307
18 https://doi.org/10.1109/taes.2012.6178052
19 https://doi.org/10.1109/tits.2010.2052805
20 https://doi.org/10.1109/tmech.2006.882988
21 https://doi.org/10.1109/tpwrd.2016.2577140
22 https://doi.org/10.1179/1752270615y.0000000047
23 https://doi.org/10.1360/n972014-00789
24 https://doi.org/10.1515/jag-2014-0021
25 https://doi.org/10.3390/s120405134
26 https://doi.org/10.3390/s121217372
27 https://doi.org/10.3390/s16070944
28 https://doi.org/10.5081/jgps.2.2.109
29 schema:datePublished 2018-12
30 schema:datePublishedReg 2018-12-01
31 schema:description A data gap of GNSS and INS may occur when data are collected by a vehicle. To obtain the pose information when this data gap appears, we use a combined auto regressive (AR) model for the forecasting of INS data so that the Strap-down Inertial Navigation System can still work. A forward process is initially implemented to forecast INS data using an AR model, and then inverse prediction is performed. Finally, the raw INS data are determined using forward and inverse results with different weights. The measurement data are applied to this method and the commercial software Inertial Explorer 8.60 (IE). The experimental result shows that the errors from the filtered results of the IE for loosely coupled and tightly coupled approaches reach the meter level after the data of the GNSS and INS are retrieved, and the error is at the meter level for conventional loosely coupled approach. Conversely, the maximum error from the proposed method is at the decimeter level. The smoother results have also been affected for the loosely coupled and tightly coupled approach of the IE before this data gap of GNSS and INS appears. However, a centimeter-level result can still be obtained via piecewise smoothing for the proposed method. The data gaps of 5 s and 10 s for GNSS and INS are simulated. These experiments show that the maximum errors of the smoother results are 0.4374 m and 4.0443 m for the proposed algorithm and these errors are better than the results for the loosely coupled and tightly coupled approach of the IE and the conventional loosely coupled approach.
32 schema:genre research_article
33 schema:inLanguage en
34 schema:isAccessibleForFree false
35 schema:isPartOf sg:journal.1136254
36 schema:name Loosely coupled GNSS/INS integration based on an auto regressive model in a data gap environment
37 schema:pagination 1-25
38 schema:productId N095f125666c04f4ea2276f648473bfca
39 N46c00034f74e40339ae9203af90f4dd0
40 Neee8fceef4ef42169b27ffaf2bed778b
41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109915122
42 https://doi.org/10.1007/s40328-018-0238-8
43 schema:sdDatePublished 2019-04-11T08:06
44 schema:sdLicense https://scigraph.springernature.com/explorer/license/
45 schema:sdPublisher N042e183d3ba54cea84422772b5c6d373
46 schema:url https://link.springer.com/10.1007%2Fs40328-018-0238-8
47 sgo:license sg:explorer/license/
48 sgo:sdDataset articles
49 rdf:type schema:ScholarlyArticle
50 N042e183d3ba54cea84422772b5c6d373 schema:name Springer Nature - SN SciGraph project
51 rdf:type schema:Organization
52 N095f125666c04f4ea2276f648473bfca schema:name readcube_id
53 schema:value e3f0a1b5179986b34c1606790af0cd1997d6be1dd163f9bb9f59c98e030780fa
54 rdf:type schema:PropertyValue
55 N46c00034f74e40339ae9203af90f4dd0 schema:name doi
56 schema:value 10.1007/s40328-018-0238-8
57 rdf:type schema:PropertyValue
58 N4ca01c597be841af954d7ae91c85f1de rdf:first sg:person.010745065467.57
59 rdf:rest Nfffe54da80cb44f5a5985b7344b203e9
60 Nb91599062d904059a6378b2a4b4dcb4a rdf:first sg:person.013400322307.47
61 rdf:rest Nf544a984b5f648baac00b40e184129f3
62 Neee8fceef4ef42169b27ffaf2bed778b schema:name dimensions_id
63 schema:value pub.1109915122
64 rdf:type schema:PropertyValue
65 Nf544a984b5f648baac00b40e184129f3 rdf:first sg:person.010160540316.44
66 rdf:rest N4ca01c597be841af954d7ae91c85f1de
67 Nfffe54da80cb44f5a5985b7344b203e9 rdf:first sg:person.014367275547.29
68 rdf:rest rdf:nil
69 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
70 schema:name Engineering
71 rdf:type schema:DefinedTerm
72 anzsrc-for:0909 schema:inDefinedTermSet anzsrc-for:
73 schema:name Geomatic Engineering
74 rdf:type schema:DefinedTerm
75 sg:journal.1136254 schema:issn 2213-5812
76 2213-5820
77 schema:name Acta Geodaetica et Geophysica
78 rdf:type schema:Periodical
79 sg:person.010160540316.44 schema:affiliation https://www.grid.ac/institutes/grid.411510.0
80 schema:familyName Chen
81 schema:givenName Guoliang
82 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010160540316.44
83 rdf:type schema:Person
84 sg:person.010745065467.57 schema:affiliation https://www.grid.ac/institutes/grid.412508.a
85 schema:familyName Wang
86 schema:givenName Shengli
87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010745065467.57
88 rdf:type schema:Person
89 sg:person.013400322307.47 schema:affiliation https://www.grid.ac/institutes/grid.411510.0
90 schema:familyName Chai
91 schema:givenName Dashuai
92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013400322307.47
93 rdf:type schema:Person
94 sg:person.014367275547.29 schema:affiliation https://www.grid.ac/institutes/grid.412508.a
95 schema:familyName Lu
96 schema:givenName Xiushan
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014367275547.29
98 rdf:type schema:Person
99 sg:pub.10.1007/978-981-10-0934-1_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002355353
100 https://doi.org/10.1007/978-981-10-0934-1_8
101 rdf:type schema:CreativeWork
102 sg:pub.10.1007/s10291-006-0050-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009138335
103 https://doi.org/10.1007/s10291-006-0050-8
104 rdf:type schema:CreativeWork
105 sg:pub.10.1007/s10291-010-0186-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015046937
106 https://doi.org/10.1007/s10291-010-0186-4
107 rdf:type schema:CreativeWork
108 sg:pub.10.1007/s10291-010-0198-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039777675
109 https://doi.org/10.1007/s10291-010-0198-0
110 rdf:type schema:CreativeWork
111 sg:pub.10.1038/srep08328 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033698849
112 https://doi.org/10.1038/srep08328
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/j.cja.2017.12.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100091613
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/j.inffus.2010.01.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015789554
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.tust.2013.03.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024323949
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1017/s0373463313000623 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038613333
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1017/s0373463314000307 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042165102
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1017/s037346331600031x schema:sameAs https://app.dimensions.ai/details/publication/pub.1004075219
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1109/87.852915 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061241909
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1109/iccais.2012.6466637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095455542
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1109/plans.2010.5507307 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093597498
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1109/taes.2012.6178052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061485757
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1109/tits.2010.2052805 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061657695
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1109/tmech.2006.882988 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061692246
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1109/tpwrd.2016.2577140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061775383
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1179/1752270615y.0000000047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027834804
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1360/n972014-00789 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065073798
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1515/jag-2014-0021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019725017
145 rdf:type schema:CreativeWork
146 https://doi.org/10.3390/s120405134 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015724706
147 rdf:type schema:CreativeWork
148 https://doi.org/10.3390/s121217372 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000264379
149 rdf:type schema:CreativeWork
150 https://doi.org/10.3390/s16070944 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046851068
151 rdf:type schema:CreativeWork
152 https://doi.org/10.5081/jgps.2.2.109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072570542
153 rdf:type schema:CreativeWork
154 https://www.grid.ac/institutes/grid.411510.0 schema:alternateName China University of Mining and Technology
155 schema:name School of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), 221116, Xuzhou, China
156 rdf:type schema:Organization
157 https://www.grid.ac/institutes/grid.412508.a schema:alternateName Shandong University of Science and Technology
158 schema:name Institute of Ocean Engineering, Shandong University of Science and Technology, 266590, Qingdao, China
159 rdf:type schema:Organization
 




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


...