Detection of Target Maneuver Onset

Ru, Jifeng; Jilkov, Vesselin P.; Li, X. Rong; Bashi, Anwer

doi:10.1109/taes.2009.5089540

Cited by 37 publications

(1 citation statement)

References 30 publications

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“…Conventional works commonly adopt the acceleration magnitude change from zero to nonzero as the maneuver indication [2,3]. However, a large amount of simulation studies and flight tests have demonstrated that maneuvering in a fixed direction (constant acceleration, denoted as type I maneuver) does not, usually, pose a real challenge to the interceptor guidance system.…”

Section: Introductionmentioning

confidence: 99%

Target maneuver discrimination using ISAR image in interception

Fan

Xiao

Fan

et al. 2016

EURASIP J. Adv. Signal Process.

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Discrimination for target maneuver magnitude and direction switching during the endgame is significant for interception performance improvement. Inverse synthetic aperture radar (ISAR) images carry the information related to target motion parameters. It is feasible to use them to discriminate the maneuver. An imaging model in interception is first formulated. The principle of maneuver discrimination using the ISAR images is then fully explored. A novel and practical discriminator is developed with a rigorous analysis of the scenario characteristics. The discriminator parameter selection and some important factors affecting the discrimination performance are discussed comprehensively. Finally, a simulation environment with software tools capable of generating target-realistic ISAR images is developed. The simulation results confirm the rationality of the design procedure and demonstrate that the proposed discriminator performs better than the classical innovation-based maneuver discriminator.

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Section: Introductionmentioning

confidence: 99%

Target maneuver discrimination using ISAR image in interception

Fan

Xiao

Fan

et al. 2016

EURASIP J. Adv. Signal Process.

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Estimation of dual‐mode nonlinear stochastic systems with unknown parameters

Liang

et al. 2022

Intl J Robust & Nonlinear

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This article proposes the problem of state estimation for dual-mode nonlinear stochastic systems with unknown parameters, that is, the possible state evolution is described by unknown switch between normal and abnormal modes, and the mismatch term of abnormal mode deviating from the normal one is parameterized as a linear stochastic transformation of state with unknown transformation matrix and modeling error covariance to generalize various abnormal state evolution. For this complex estimation problem involving unknown states, modes, and parameters (i.e., transformation matrix and covariance), a Bayesian learning based filter is first derived to update the joint posterior of state and unknown parameters in abnormal mode by exploiting sequential Monte Carlo, conjugate prior and marginalization. Then a switching output strategy of normal/abnormal mode filters based on likelihood-ratio test is designed to reduce the conservatism of abnormal mode filter to normal mode state estimation.Finally, simulation study on a benchmark of maneuvering target tracking shows the superiority of the proposed method compared with existing ones.

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