Chee-Hyun Park

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In wireless communications, knowledge of the signal-to-noise ratio is required in diverse communication applications. In this paper, we derive the variance of the maximum likelihood esti-mator in the data-aided and non-data-aided schemes for determining the optimal shrinkage factor. The shrinkage factor is usually the constant that is multiplied by the(More)
a r t i c l e i n f o a b s t r a c t Shrinkage factor Minimum mean squared error Minimum bias Weighted least squares Time-of-arrival In this paper, we propose two novel source localization methods; one is the shrinkage estimator with the minimum mean squared error criterion, and the other is the shrinkage estimator with the minimum bias criterion. The mean(More)
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract— This paper presents two closed-form localization algorithms, a general algorithm and a co-located algorithm, for distributed multiple-input multiple-output (MIMO) radar systems. In(More)
BACKGROUND Current oscillometric blood pressure measurement devices generally provide only single-point estimates for systolic and diastolic blood pressures and rarely provide confidence ranges for these estimates. A novel methodology to obtain confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates from a(More)
In diverse engineering problems including wireless communications, the estimate of the signal-to-noise ratio (SNR) is required. In this study, the authors develop a shrinkage-based SNR estimator in the data-aided and non-data-aided schemes for higher M-ary phase-shift-keying (M ≥ 8) and quadrature amplitude modulations. The observed Cramér-Rao lower bound(More)
Breakdown point Hodges–Lehmann estimator Influence function Robust statistics a b s t r a c t In this paper, we propose an NLOS source localization method that utilizes the robust statistics, namely, the α-trimmed mean and Hodges–Lehmann estimator. The root mean squared error average of the proposed methods is similar to that of the other estimators such as(More)