Hyundai Industrial Research Institute

Dong gu, South Korea

Hyundai Industrial Research Institute

Dong gu, South Korea
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Shevlyakov G.,Saint Petersburg State Polytechnic University | Shin V.,Gyeongsang National University | Lee S.,Hyundai Industrial Research Institute | Kim K.,Gwangju Institute of Science and Technology
International Journal of Adaptive Control and Signal Processing | Year: 2014

To design highly efficient and robust detectors of a weak signal, an asymptotic approach to stable estimation exploiting redescending score functions is used. Two new indicators of robustness of detection, the detection error sensitivity and detection stability, are introduced. The optimal Neyman-Pearson rules maximizing detection efficiency under the guaranteed level of detection stability are written out. Under heavy-tailed noise distributions, the proposed asymptotically stable detectors based on redescending score functions, namely, the minimum error sensitivity and the radical ones, outperform conventional linear bounded Huber's and redescending Hampel's detectors both on small and large samples. © 2013 John Wiley & Sons, Ltd.


Young Song I.,Hanwha Corporation | Shin V.,Gyeongsang National University | Lee S.,Hyundai Industrial Research Institute | Choi W.,Incheon National University
Journal of the Franklin Institute | Year: 2014

This paper focuses on four fusion algorithms for the estimation of nonlinear cost function (NCF) in a multisensory environment. In multisensory filtering and control problems, NCF represents a nonlinear multivariate functional of state variables, which can indicate useful information of the target systems for automatic control. To estimate the NCF using multisensory information, we propose one centralized and three decentralized estimation fusion algorithms. For multivariate polynomial NCFs, we propose a simple closed-form computation procedure. For general NCFs, the most popular procedure for the evaluation of their estimates is based on the unscented transformation. The effectiveness and estimation accuracy of the proposed fusion algorithms are demonstrated with theoretical and numerical examples. © 2014 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.


Song I.Y.,Hanwha Corporation | Shin V.,Gyeongsang National University | Lee S.,Hyundai Industrial Research Institute | Choi W.,Incheon National University
IET Science, Measurement and Technology | Year: 2014

This study focuses on fusion algorithms for the estimation of a non-linear function of the state vector in a multisensory continuous-time stochastic system. The non-linear function of the state (NFS) represents a non-linear multivariate function of state variables, which can indicate useful information of a target system for control. To estimate a NFS using multisensory information, they propose one centralised and three distributed estimation fusion algorithms. For multivariate polynomial functions, they derive a closed-form estimation procedure. In the general case, an unscented transformation is used for evaluation of the fusion estimate of an NFS. The subsequent application of the proposed fusion estimators to a linear stochastic system within a multisensor environment demonstrates their effectiveness. © The Institution of Engineering and Technology.


Song I.Y.,Hanwha Corporation | Shin V.,Gyeongsang National University | Lee S.,Hyundai Industrial Research Institute | Choi W.,Incheon National University
Mathematical Problems in Engineering | Year: 2014

We propose centralized and distributed fusion algorithms for estimation of nonlinear cost function (NCF) in multisensory mixed continuous-discrete stochastic systems. The NCF represents a nonlinear multivariate functional of state variables. For polynomial NCFs, we propose a closed-form estimation procedure based on recursive formulas for high-order moments for a multivariate normal distribution. In general case, the unscented transformation is used for calculation of nonlinear estimates of a cost functions. To fuse local state estimates, the mixed differential difference equations for error cross-covariance between local estimates are derived. The subsequent application of the proposed fusion estimators for a multisensory environment demonstrates their effectiveness. © 2014 Il Young Song et al.

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