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...

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Veröffentlicht in:International journal of robust and nonlinear control Jg. 33; H. 2; S. 1122 - 1149
Hauptverfasser: Li, Zhifei, Liang, Yan, Xu, Linfeng, Ma, Shuli
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 25.01.2023
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:Funding information
National Natural Science Foundation of China, Grant/Award Numbers: 61771399; 61873205; 62273283
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6425