On observability and optimal gain design for distributed linear filtering and prediction
This paper presents a new approach to distributed linear filtering and prediction. The problem under consideration consists of a random dynamical system observed by a multi-agent network of sensors where the network is sparse. Inspired by the consensus+innovations type of distributed estimation appr...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Das, Subhro |
| description | This paper presents a new approach to distributed linear filtering and
prediction. The problem under consideration consists of a random dynamical
system observed by a multi-agent network of sensors where the network is
sparse. Inspired by the consensus+innovations type of distributed estimation
approaches, this paper proposes a novel algorithm that fuses the concepts of
consensus and innovations. The paper introduces a definition of distributed
observability, required by the proposed algorithm, which is a weaker assumption
than that of global observability and connected network assumptions combined
together. Following first principles, the optimal gain matrices are designed
such that the mean-squared error of estimation is minimized at each agent and
the distributed version of the algebraic Riccati equation is derived for
computing the gains. |
| doi_str_mv | 10.48550/arxiv.2203.03521 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2203_03521</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2203_03521</sourcerecordid><originalsourceid>FETCH-LOGICAL-a671-d91874f095e1875132c1a584826094780c592cf31e972a987d9b59f6d55309023</originalsourceid><addsrcrecordid>eNotj7tuwyAYhVk6VEkfoFN5AbtcjIExinqTImXJkM36bcD6JYotTKPm7Zu6nc5ZzqfzEfLIWd0Ypdgz5G-81EIwWTOpBL8n52OiU7_4fIEeI5YrheToNBf8hEhHwESdX3BMNEyZOlxKxv6reEcjJg-ZBozFZ0zjOpyzdzgUnNKW3AWIi3_4zw05vb6c9u_V4fj2sd8dKmg1r5zlRjeBWeVvRXEpBg7KNEa0zDbasEFZMQTJvdUCrNHO9sqG1iklmWVCbsjTH3ZV6-Z8-52v3a9ityrKHwuXS3U</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>On observability and optimal gain design for distributed linear filtering and prediction</title><source>arXiv.org</source><creator>Das, Subhro</creator><creatorcontrib>Das, Subhro</creatorcontrib><description>This paper presents a new approach to distributed linear filtering and
prediction. The problem under consideration consists of a random dynamical
system observed by a multi-agent network of sensors where the network is
sparse. Inspired by the consensus+innovations type of distributed estimation
approaches, this paper proposes a novel algorithm that fuses the concepts of
consensus and innovations. The paper introduces a definition of distributed
observability, required by the proposed algorithm, which is a weaker assumption
than that of global observability and connected network assumptions combined
together. Following first principles, the optimal gain matrices are designed
such that the mean-squared error of estimation is minimized at each agent and
the distributed version of the algebraic Riccati equation is derived for
computing the gains.</description><identifier>DOI: 10.48550/arxiv.2203.03521</identifier><language>eng</language><subject>Computer Science - Information Theory ; Computer Science - Learning ; Computer Science - Systems and Control ; Mathematics - Information Theory</subject><creationdate>2022-03</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,778,883</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2203.03521$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2203.03521$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Das, Subhro</creatorcontrib><title>On observability and optimal gain design for distributed linear filtering and prediction</title><description>This paper presents a new approach to distributed linear filtering and
prediction. The problem under consideration consists of a random dynamical
system observed by a multi-agent network of sensors where the network is
sparse. Inspired by the consensus+innovations type of distributed estimation
approaches, this paper proposes a novel algorithm that fuses the concepts of
consensus and innovations. The paper introduces a definition of distributed
observability, required by the proposed algorithm, which is a weaker assumption
than that of global observability and connected network assumptions combined
together. Following first principles, the optimal gain matrices are designed
such that the mean-squared error of estimation is minimized at each agent and
the distributed version of the algebraic Riccati equation is derived for
computing the gains.</description><subject>Computer Science - Information Theory</subject><subject>Computer Science - Learning</subject><subject>Computer Science - Systems and Control</subject><subject>Mathematics - Information Theory</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7tuwyAYhVk6VEkfoFN5AbtcjIExinqTImXJkM36bcD6JYotTKPm7Zu6nc5ZzqfzEfLIWd0Ypdgz5G-81EIwWTOpBL8n52OiU7_4fIEeI5YrheToNBf8hEhHwESdX3BMNEyZOlxKxv6reEcjJg-ZBozFZ0zjOpyzdzgUnNKW3AWIi3_4zw05vb6c9u_V4fj2sd8dKmg1r5zlRjeBWeVvRXEpBg7KNEa0zDbasEFZMQTJvdUCrNHO9sqG1iklmWVCbsjTH3ZV6-Z8-52v3a9ityrKHwuXS3U</recordid><startdate>20220307</startdate><enddate>20220307</enddate><creator>Das, Subhro</creator><scope>AKY</scope><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20220307</creationdate><title>On observability and optimal gain design for distributed linear filtering and prediction</title><author>Das, Subhro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a671-d91874f095e1875132c1a584826094780c592cf31e972a987d9b59f6d55309023</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Information Theory</topic><topic>Computer Science - Learning</topic><topic>Computer Science - Systems and Control</topic><topic>Mathematics - Information Theory</topic><toplevel>online_resources</toplevel><creatorcontrib>Das, Subhro</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Das, Subhro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On observability and optimal gain design for distributed linear filtering and prediction</atitle><date>2022-03-07</date><risdate>2022</risdate><abstract>This paper presents a new approach to distributed linear filtering and
prediction. The problem under consideration consists of a random dynamical
system observed by a multi-agent network of sensors where the network is
sparse. Inspired by the consensus+innovations type of distributed estimation
approaches, this paper proposes a novel algorithm that fuses the concepts of
consensus and innovations. The paper introduces a definition of distributed
observability, required by the proposed algorithm, which is a weaker assumption
than that of global observability and connected network assumptions combined
together. Following first principles, the optimal gain matrices are designed
such that the mean-squared error of estimation is minimized at each agent and
the distributed version of the algebraic Riccati equation is derived for
computing the gains.</abstract><doi>10.48550/arxiv.2203.03521</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2203.03521 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2203_03521 |
| source | arXiv.org |
| subjects | Computer Science - Information Theory Computer Science - Learning Computer Science - Systems and Control Mathematics - Information Theory |
| title | On observability and optimal gain design for distributed linear filtering and prediction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T13%3A01%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=On%20observability%20and%20optimal%20gain%20design%20for%20distributed%20linear%20filtering%20and%20prediction&rft.au=Das,%20Subhro&rft.date=2022-03-07&rft_id=info:doi/10.48550/arxiv.2203.03521&rft_dat=%3Carxiv_GOX%3E2203_03521%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |