Distributed High-Degree Cubature Information Filter With Embedded Hybrid Consensus
| Autor: | Yu Liu, Jun Liu, You He, Ding Ziran, Kai Dong |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
General Computer Science Computer science Computation Gaussian Stability (learning theory) 02 engineering and technology spherical-radial rule Consistency (database systems) symbols.namesake 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering General Materials Science consensus estimation Information filtering system General Engineering 020206 networking & telecommunications Nonlinear system Cubature information filter distributed filtering nonlinear filtering symbols numerical integration lcsh:Electrical engineering. Electronics. Nuclear engineering Algorithm Wireless sensor network lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 110400-110413 (2019) |
| ISSN: | 2169-3536 |
| Popis: | To settle the problem of distributed nonlinear state estimation in sensor networks with naive nodes, a novel distributed high-degree cubature information filter with embedded hybrid consensus (DHCIF) is proposed. The multi-dimensional Gaussian weighted integrals involved in the filtering process are approximated by the fifth-degree cubature rule. A novel scheme, with consideration for the predicted measurement errors, is applied to compute the information contribution. With parallel consensus performed on both prior and measurement information, the proposed DHCIF is derived. The stability analysis with regard to consistency and boundedness of estimation errors for the proposed DHCIF is also developed. Finally, the effectiveness and advantage of the proposed DHCIF is validated by a typical maneuvering target tracing scenario. The experimental results indicate that the proposed DHCIF outperforms the existing algorithms in the aspects of estimation accuracy, consistency and consensus at the cost of a little more extra computation burden. |
| Databáze: | OpenAIRE |
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