Iterative filter with finite measurements for suddenly maneuvering targets

Shunyi Zhao; Choon Ki Ahn; Yuriy S. Shmaily; Peng Shi; Ramesh K. Agarwal

doi:10.2514/1.G002797

Iterative filter with finite measurements for suddenly maneuvering targets

Shunyi Zhao, Choon Ki Ahn, Yuriy S. Shmaily, Peng Shi, Ramesh K. Agarwal

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

The finite impulse response (FIR) structure is extended to Markov linear systems (JMLSs) based on the optimal unbiased FIR filters developed for a general time-variant state-space system and the IMM algorithm. The proposed algorithm degenerates to the IMM approach as the estimation horizon length approaches infinity. Researchers introduce the IMM filter briefly and present the problem ormulation. The researchers demonstrate that it is known that the optimal Bayesian estimation for JMLSs in the minimum mean-square error sense is practical intractable because its computational complexity grows exponentially with the time step.

Original language	English
Pages (from-to)	2316-2322
Number of pages	7
Journal	Journal of Guidance, Control, and Dynamics
Volume	40
Issue number	9
DOIs	https://doi.org/10.2514/1.G002797
Publication status	Published - 2017

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325.

ASJC Scopus subject areas

Control and Systems Engineering
Aerospace Engineering
Space and Planetary Science
Electrical and Electronic Engineering
Applied Mathematics

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10.2514/1.G002797

Cite this

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title = "Iterative filter with finite measurements for suddenly maneuvering targets",

abstract = "The finite impulse response (FIR) structure is extended to Markov linear systems (JMLSs) based on the optimal unbiased FIR filters developed for a general time-variant state-space system and the IMM algorithm. The proposed algorithm degenerates to the IMM approach as the estimation horizon length approaches infinity. Researchers introduce the IMM filter briefly and present the problem ormulation. The researchers demonstrate that it is known that the optimal Bayesian estimation for JMLSs in the minimum mean-square error sense is practical intractable because its computational complexity grows exponentially with the time step.",

author = "Shunyi Zhao and Ahn, {Choon Ki} and Shmaily, {Yuriy S.} and Peng Shi and Agarwal, {Ramesh K.}",

note = "Funding Information: This work was supported in part by the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325.",

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doi = "10.2514/1.G002797",

language = "English",

volume = "40",

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journal = "Journal of Guidance, Control, and Dynamics",

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T1 - Iterative filter with finite measurements for suddenly maneuvering targets

AU - Zhao, Shunyi

AU - Ahn, Choon Ki

AU - Shmaily, Yuriy S.

AU - Shi, Peng

AU - Agarwal, Ramesh K.

N1 - Funding Information: This work was supported in part by the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325.

PY - 2017

Y1 - 2017

N2 - The finite impulse response (FIR) structure is extended to Markov linear systems (JMLSs) based on the optimal unbiased FIR filters developed for a general time-variant state-space system and the IMM algorithm. The proposed algorithm degenerates to the IMM approach as the estimation horizon length approaches infinity. Researchers introduce the IMM filter briefly and present the problem ormulation. The researchers demonstrate that it is known that the optimal Bayesian estimation for JMLSs in the minimum mean-square error sense is practical intractable because its computational complexity grows exponentially with the time step.

AB - The finite impulse response (FIR) structure is extended to Markov linear systems (JMLSs) based on the optimal unbiased FIR filters developed for a general time-variant state-space system and the IMM algorithm. The proposed algorithm degenerates to the IMM approach as the estimation horizon length approaches infinity. Researchers introduce the IMM filter briefly and present the problem ormulation. The researchers demonstrate that it is known that the optimal Bayesian estimation for JMLSs in the minimum mean-square error sense is practical intractable because its computational complexity grows exponentially with the time step.

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M3 - Article

AN - SCOPUS:85028314878

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VL - 40

SP - 2316

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JO - Journal of Guidance, Control, and Dynamics

JF - Journal of Guidance, Control, and Dynamics

IS - 9

ER -

Iterative filter with finite measurements for suddenly maneuvering targets

Abstract

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