Chee-Hyun Park

Hanyang University · Department of Electronics and Computer Engineering

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...

This study presents a robust two-step weighted least squares (WLS) localization algorithm using the MM estimator and the Kalman filter with the maximum Versoria criterion (MVC). An outlier-resistant statistic for the actual transformed distance is determined and the covariance matrix of the outlier-resistant statistic is calculated. This covariance...

This paper presents robust positioning methods that use distance observations to estimate location parameters. The propagation of a non-line-of-sight (NLOS) signal can significantly affect the estimation performance in indoor and densely populated urban areas. Hence, robust localization algorithms are considered for alleviating the adverse effects...

This letter presents a novel approach for accurately localizing moving object based on a robust time-of-arrival-based splitting mean positioning algorithm. The estimation performance of the existing localization method using the variational Bayesian Gaussian mixture model is degraded when a single observation is used. To overcome this limitation, t...

This paper presents robust localization algorithms that use range measurements to estimate the location parameters. The non-line-of-sight (NLOS) propagation of a signal can severely deteriorate the estimation performance in indoor and population-dense urban areas. Therefore, the robust localization algorithms are considered in this paper. In partic...

Robust localisation techniques that utilise distance observations to determine the location are focused upon. In urban environments with limited visibility and high population density, the presence of non‐line‐of‐sight signals can introduce a positive measurement bias, negatively affecting the accuracy of estimation. To resolve this problem caused...

This paper presents robust localization techniques that calculate location using distance observations. In enclosed and heavily populated urban environments, the positive measurement bias introduced by a non-line-of-sight signal can have a considerable adverse impact on estimation performance. Therefore, to mitigate the detrimental effects of the m...

Parametric approaches are primarily used in the context of robust localization. However, the localization performance is degraded when there is a mismatch between the assumed model and the actual situation. To circumvent this problem, in this letter, a robust weighted least squares (WLS) method based on the non-parametric kernel density estimator (...

This paper presents robust positioning methods that use range measurements to estimate location parameters. The existing maximum correntropy criterion-based localization algorithm uses only the $l_{2}$ norm minimization. Therefore, the localization performance may not be satisfying because the $l_{2}$ norm minimization is vulnerable to the larg...

Robust localization methods that employ distance measurements to predict the position of an emitter are proposed in this paper. The occurrence of outliers due to the non-line-of sight (NLOS) propagation of signals can drastically degrade the localization performance in crowded urban areas and indoor situations. Hence, robust positioning methods are...

Herein, the authors present a robust estimator of range against the impulsive noise using only the received signal's magnitude. The M estimator has been widely used in robust signal processing. However, the existing M estimator requires statistical testing involving a threshold which has an optimality that varies with time, hence algorithmically ch...

In this paper, we present robust localization algorithms that use range measurements. The least median of squares (LMedS)-weighted least squares (WLS), LMedS-spherical simplex unscented transform (SSUT) based WLS and Tukey-based extended Kalman filter (EKF) algorithms are proposed for line-of-sight (LOS)/non-line-of-sight (NLOS) mixture environment...

This paper presents novel shrinkage‐based sinusoidal phase estimation algorithms. The main contributions of this paper are two‐fold. First, the shrinkage factor is found using the spherical simplex unscented transform (SSUT) and the combination of bootstrap and SSUT to reduce the computational complexity of the Monte Carlo method. The computational...

We present robust range-based localization algorithms for which range measurements are used to estimate the location parameter. Non-line-of-sight (NLOS) propagation of signal can deteriorate the estimation performance severely in the indoor and crowded urban areas. A study for localization has been intensively performed in the line-of-sight (LOS) c...

This study considers robust time-of-arrival (TOA) source localisation algorithms. Range measurements are used to estimate the location parameter for TOA source localisation and previous information on the position of the source is employed to improve the existing measurement-based method. The proposed methods are categorised into two types. First,...

Herein, we present robust shrinkage range estimation algorithms for which received signal strength measurements are used to estimate the distance between emitter and sensor. The concepts of robustness for the Hampel filter and skipped filter are combined with shrinkage for the positive blind minimax and Bayes shrinkage estimation. It is demonstrate...

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 author...

This paper presents an a priori probability density function (pdf)-based time-of-arrival (TOA) source localization algorithms. Range measurements are used to estimate the location parameter for TOA source localization. Previous information on the position of the calibrated source is employed to improve the existing likelihood-based localization met...

We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithms that utilize the weighted block Newton (WBN) and variable step size WBN (VSSWBN) method, in which the weighting matrix is determined in the form of the inverse of the squared error or as an exponential function with a negative exponent. The proposed WBN...

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 ar...

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 approxim...

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 mult...

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-dataaided schemes for higher M-ary phase-shift-keying (M≥ 8) and quadrature amplitude modulations. The observed Cramér-Rao lower...

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...

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...

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...

A mode-singular-value-decomposition (SVD) maximum likelihood (ML) estimation procedure is proposed for the source localization problem under an additive measurement error model. In a practical situation, the noise variance is usually unknown. In this paper, we propose an algorithm that does not require the noise covariance matrix as a priori knowle...

In this paper, we have proposed an adaptive source localization method. The block LMS method has been used in channel estimation and noise reduction. In this paper, the block LMS method has been used for the source localization and the analysis of the mean square error performance has been carried out in the source localization formulation. The per...

A block-based Wiener filter is proposed for the source localization problem under an additive measurement error model. In this paper, we expand the Wiener filter, which is a single sensor case, to multi-sensor scenario. The proposed block-based Wiener filter method proves to be more efficient than the existing methods under various noise situations...

A weighted least squares (WLS) estimation procedure based on singular value decomposition (SVD) is proposed for the source localization problem under an additive measurement error model. In practical situation, the respective sensor reliability may differ. The WLS solves the problem by assigning different weights to the sensors. However, the existi...

This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-...

Estimating a location of mobile phones or sound source is of considerable interest in wireless communications and signal processing. In this letter, we propose squared range weighted least squares (SRWLS) using the range estimate attained from the Taylor series-based maximum likelihood. The weight can be determined more accurately when using the pr...

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square (VEDNSS LMS) algorithm. Adopting VEDNSS LMS provides fast convergence at early stages of adaptation while ensuring small final misadjustment. An analysis of convergence and steady-state performance fo...