Chee-Hyun Park

Closed-Form Localization for Distributed MIMO Radar Systems Using Time Delay Measurements

This paper presents two closed-form localization algorithms, a general algorithm and a colocated algorithm, for distributed multiple-input multiple-output (MIMO) radar systems. In distributed MIMO radar systems, range sum measurements are used to estimate the location parameter. For this, the range sum error minimization is actually employed to be degenerated into two cases for time-of-arrival (TOA) passive localization, one by employing the distance estimate between the target and the receiver (the general algorithm) and the other by subtracting the distance measurement between the target and the transmitter after the time-delay estimation (the colocated algorithm). The resulting positioning accuracy of the general and colocated techniques is found to perform better than that of the existing closed-form weighted least squares (WLS) algorithm and attain the Cramér-Rao lower bound (CRLB).

Improved Gaussian Mixture Regression Based on Pseudo Feature Generation Using Bootstrap in Blood Pressure Estimation

Although the systolic and diastolic blood pressure ratios (SBPRs and DBPRs) based on the conventional maximum amplitude algorithm (MAA) are assumed to be fixed; this assumption is not valid. In this paper, we present an improved Gaussian mixture regression (IGMR) approach that can accurately measure blood pressure. The SBPR and DBPR are estimated by using the IGMR technique. Specifically, the number of features samples in the clustered feature space is increased using the nonparametric bootstrap technique to create the pseudo feature. The pseudo feature vector is much more matched than the original feature for the Gaussian mixture model (GMM) to fit individual BP characteristics in the training stage. By using the classified targeting clusters, we eventually estimate the SBPR and DBPR based on the IGMR technique at the test stage. The mean error (ME) and standard deviation of the error (SDE), and mean absolute error (MAE) of the SBP and DBP estimates obtained with the SBPR and DBPR using the proposed technique approaches are superior to the ME, SDE, and MAE of the estimates obtained using the conventional methods. The difference in the SDE between the proposed technique and the conventional MAA technique for the SBP and DBP turned out to be 3.67 and 3.08 mmHg in the simulation.

Shrinkage estimation-based source localization with minimum mean squared error criterion and minimum bias criterion

Abstract 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 squared error performance of the two-step weighted least squares deteriorates in the large noise variance regimes. In order to improve the two-step weighted least squares in the large noise variance regimes, the shrinkage factor is multiplied by the two-step weighted least squares estimator, and then the novel estimator is determined such that the mean squared error and squared bias are minimized. Simulation results show that the mean squared error performances of the proposed methods are better than those of the two-step weighted least squares method as well as the minimax estimator in a regime with large measurement noise variances.

Estimated confidence interval from single blood pressure measurement based on algorithmic fusion

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 single oscillometric blood pressure measurement is presented. METHODS The proposed methodology utilizes the multiple regression technique to fuse optimally a set of SBP and DBP estimates obtained through different algorithms. However, the set of SBP and DBP estimates is a small number to determine the CI of each individual subject. To address this issue, the weighted bootstrap approach based on the multiple regression technique was used to generate a pseudo sample set for the SBP and the DBP. In this paper, the multiple regression technique can estimate the best-fitting surface of an efficient function that relates the input sample set as an independent vector to the auscultatory nurse measurement as a dependent vector to estimate regression coefficients. Consequently, the coefficients are assigned to an eight-sample set obtained from the fusion of different algorithms as optimally weighted parameters. CIs are also estimated using the conventional methods on the set of fused SBP and DBP estimates for comparison purposes. RESULTS The proposed method was applied to an experimental dataset of 85 patients. The results indicated that the proposed approach provides better blood pressure estimates than the existing algorithms and, in addition, is able to provide CIs for a single measurement. CONCLUSIONS The CIs derived from the proposed scheme are much smaller than those calculated by conventional methods except for the pseudo maximum amplitude-envelope algorithm for both the SBP and the DBP, probably because of the decrease in the standard deviation through the increase in the pseudo measurements using the weighted bootstrap method for each subject. The proposed methodology is likely the only one currently available that can provide CIs for single-sample blood pressure measurements.

Robust time-of-arrival source localization employing error covariance of sample mean and sample median in line-of-sight/non-line-of-sight mixture environments

We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithm that utilizes the weighted least squares (WLS) method in LOS/NLOS mixture environments, where the weight matrix is determined in the algebraic form. Unless the contamination ratio exceeds 50 %, the asymptotic variance of the sample median can be approximately related to that of the sample mean. Based on this observation, we use the error covariance matrix for the sample mean and median to minimize the weighted squared error (WSE) loss function. The WSE loss function based on the sample median is utilized when statistical testing supports the LOS/NLOS state, while the WSE function using the sample mean is employed when statistical testing indicates that the sensor is in the LOS state. To testify the superiority of the proposed methods, the mean square error (MSE) performances are compared via simulation.

Robust closed-form time-of-arrival source localization based on α-trimmed mean and Hodges-Lehmann estimator under NLOS environments

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 M-estimator and Taylor-series maximum likelihood estimator using the median, but the proposed robust estimators have advantages that they have the closed-form solution. The simulation results show that the root mean squared error performance of the proposed methods is similar or outperforms that of the iteration-based M-estimator. The Taylor-series maximum likelihood estimator based on the sample median is most superior among the investigated localization methods, but it has the disadvantages that the computational complexity is high and that the solution may converge to the local maxima. Also, it is shown that the performances of the closed-form proposed estimators outperform the JMAP-ML and LS estimator in the above of certain NLOS noise level. HighlightsThe robust closed-form TOA source localization methods are proposed.The proposed estimators have not been used in the TOA source localization context.The proposed methods have an advantage that they are the closed-form.The mixed version of the JMAP-ML estimator and proposed estimators is also proposed.

Closed-form two-step weighted-least-squares-based time-of-arrival source localisation using invariance property of maximum likelihood estimator in multiple-sample environment

In this study, the authors propose a closed-form time-of-arrival source localisation method and justify the employment of the invariance property of the maximum likelihood (ML) estimator in the source localisation context with multiple samples. The magnitude of the bias of the proposed sample vector function (the statistic that consists of the multiple observations set) using the invariance property of the ML estimator is smaller than that based on the sample mean. Therefore, the mean squared error (MSE) of the weighted least squares estimate using the proposed sample vector function is smaller than that based on the sample mean when the variances of both sample vector functions are the same. Furthermore, the authors investigate a situation in which sensors have erroneous position information. The simulation results show that the averaged MSE performance of the proposed method is superior to that of the existing methods irrespective of the number of samples.

Time-of-arrival source localization based on weighted least squares estimator in line-of-sight/non-line-of-sight mixture environments:

In this article, we propose a line-of-sight/non-line-of-sight time-of-arrival source localization algorithm that utilizes the weighted least squares. The proposed estimator combines multiple sorted measurements using the spatial sign concept, Mahalanobis distance, and Stahel–Donoho estimator, that is, assigning less weight to the samples as they are far from the center of inlier distribution. Also, the eigendecomposition Kendall’s τ covariance matrix is utilized as the scatter measure instead of the conventional median absolute deviation. Thus, the adverse effects by outliers can be attenuated effectively. To validate the superiority of the proposed methods, the root mean square error performances are compared with that of the existing algorithms via extensive simulation.

Biased SNR estimation using pilot and data symbols in BPSK and QPSK systems

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 estimator 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 unbiased estimate and it increases the bias slightly while considerably decreasing the variance so that the overall mean squared error decreases. The closed-form biased estimators for binary-phase-shift-keying and quadrature-phase-shift-keying systems are then obtained. Simulation results show that the mean squared error of the proposed method is lower than that of the maximum likelihood method for low and moderate signal-to-noise ratio conditions.

Sequential source localisation and range estimation based on shrinkage algorithm

This study presents the shrinkage-based sequential source localisation and range estimation algorithms. The shrinkage factor is found using the variance of the estimate in the existing shrinkage algorithm. However, the variance of the estimate is difficult to calculate when the form of the estimate is complex. To circumvent this problem, the authors propose a shrinkage algorithm that employs the Cramer-Rao lower bound (CRLB) instead of the variance for the maximum likelihood (ML) estimate. The variance of the ML estimate and CRLB were found to be similar in simulation results. Furthermore, Steins unbiased risk estimator and Ledoit-Wolf methods are used to determine the shrinkage factor. The resulting estimation accuracy of the proposed shrinkage-based sequential source localisation and range estimation methods was similar with that of the existing shrinkage algorithm.