Distributed extended object tracking information filter over sensor networks

This work aims to design a distributed extended object tracking system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network. To this end, we resort to the multiplicative error model (MEM) that allows the extent parameters of perpen...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of robust and nonlinear control Ročník 33; číslo 2; s. 1122 - 1149
Hlavní autoři: Li, Zhifei, Liang, Yan, Xu, Linfeng, Ma, Shuli
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 25.01.2023
Témata:
distributed consensus estimate
extended object tracking
Iterative methods
Kinematics
Mathematical models
Nodes
nonlinear control system
Parameters
Sensors
sequential processing
Tracking systems
wireless sensor networks
ISSN:1049-8923, 1099-1239
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This work aims to design a distributed extended object tracking system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network. To this end, we resort to the multiplicative error model (MEM) that allows the extent parameters of perpendicular axis‐symmetric objects to have individual uncertainty. To incorporate the MEM into the information filter (IF) style, we use the moment‐matching technique to derive two pair linear models with only additive noise. The separation is merely in a fashion, and the cross‐correlation between states is preserved as parameters in each other's model. As a result, the closed‐form expressions are transferred into an alternating iteration of two linear IFs. With the two models, a centralized IF is proposed wherein the measurements are converted into a summation of innovation parts. Later, under a sensor network with the communication nodes and sensor nodes, we present two distributed IFs through the consensus on information and consensus on measurement schemes, respectively. Moreover, we prove the estimation errors of the proposed filter are exponentially bounded in the mean square. The benefits are testified by numerical experiments in comparison to state‐of‐the‐art filters in literature.
Bibliografie:Funding information
National Natural Science Foundation of China, Grant/Award Numbers: 61771399; 61873205; 62273283
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6425