[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

In this article, we have provided general, comprehensive coverage of the SDR technique, from its practical deployments and scope of applicability to key theoretical results. We have also showcased several representative applications, namely MIMO detection, B? shimming in MRI, and sensor network localization. Another important application, namely downlink transmit beamforming, is described in [1]. Due to space limitations, we are unable to cover many other beautiful applications of the SDR technique, although we have done our best to illustrate the key intuitive ideas that resulted in those applications. We hope that this introductory article will serve as a good starting point for readers who would like to apply the SDR technique to their applications, and to locate specific references either in applications or theory.

/pdf/semidefinite-relaxation-of-quadratic-optimization-problems-5ayn06v8bl.pdf

Semidefinite Relaxation of Quadratic Optimization Problems

This is the first part of a comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. It surveys various mathematical models of target motion/dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among models and insight to the pros and cons of models are provided. Some material presented here has not appeared elsewhere.

Survey of maneuvering target tracking. Part I. Dynamic models

http://www.eecs.northwestern.edu/~peters/references/LocalizationTechniquesMaoAustralia.pdf

Wireless sensor network localization techniques

Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety feature of cellular systems in response to an order issued by the Federal Communications Commission (FCC) in 1996. The FCC mandate aims to solve a serious public safety problem caused by the fact that, at present, a large proportion of all 911 calls originate from mobile phones, the location of which cannot be determined with the existing technology. However, many difficulties intrinsic to the wireless environment make meeting the FCC objective challenging. These challenges include channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions. In addition to emergency services, there are many other applications for wireless location technology, including monitoring and tracking for security reasons, location sensitive billing, fraud protection, asset tracking, fleet management, intelligent transportation systems, mobile yellow pages, and even cellular system design and management. This article provides an overview of wireless location challenges and techniques with a special focus on network-based technologies and applications.

Network-based wireless location: challenges faced in developing techniques for accurate wireless location information

An effective technique in locating a source based on intersections of hyperbolic curves defined by the time differences of arrival of a signal received at a number of sensors is proposed. The approach is noniterative and gives an explicit solution. It is an approximate realization of the maximum-likelihood estimator and is shown to attain the Cramer-Rao lower bound near the small error region. Comparisons of performance with existing techniques of beamformer, spherical-interpolation, divide and conquer, and iterative Taylor-series methods are made. The proposed technique performs significantly better than spherical-interpolation, and has a higher noise threshold than divide and conquer before performance breaks away from the Cramer-Rao lower bound. It provides an explicit solution form that is not available in the beamforming and Taylor-series methods. Computational complexity is comparable to spherical-interpolation but substantially less than the Taylor-series method. >

https://www.source-localiz.ucoz.ru/A_simple_and_efficient_estimator_for_hyperbolic_lo.pdf

A simple and efficient estimator for hyperbolic location

Localization of mobile phones is of considerable interest in wireless communications. In this correspondence, two algorithms are developed for accurate mobile location using the time-of-arrival measurements of the signal from the mobile station received at three or more base stations. The first algorithm is an unconstrained least squares (LS) estimator that has implementation simplicity. The second algorithm solves a nonconvex constrained weighted least squares (CWLS) problem for improving estimation accuracy. It is shown that the CWLS estimator yields better performance than the LS method and achieves both the Crame/spl acute/r-Rao lower bound and the optimal circular error probability at sufficiently high signal-to-noise ratio conditions.

/pdf/least-squares-algorithms-for-time-of-arrival-based-mobile-3hq7hdx01o.pdf

Least squares algorithms for time-of-arrival-based mobile location

Three or more base stations (BS) making time-of-arrival measurements of a signal from a mobile station (MS) can locate the MS. However, when some of the measurements are from non-line-of-sight (NLOS) paths, the location errors can be very large. This paper proposes a residual test (RT) that can simultaneously determine the number of line-of-sight (LOS) BS and identify them. Then, localization can proceed with only those LOS BS. The RT works on the principle that when all measurements are LOS, the normalized residuals have a central Chi-Square distribution, versus a noncentral distribution when there is NLOS. The residuals are the squared differences between the estimates and the true position. Normalization by their variances gives a unity variance to the resultant random variables. In simulation studies, for the chosen geometry and NLOS and measurement noise errors, the RT can determine the correct number of LOS-BS over 90% of the time. For four or more BS, where there are at least three LOS-BS, the estimator has variances that are near the Cramer--Rao lower bound.

/pdf/time-of-arrival-based-localization-under-nlos-conditions-1in84jlxfl.pdf

Time-of-arrival based localization under NLOS conditions

Beginning with the derivation of a least squares estimator that yields an estimate of the acceleration input vector, this paper first develops a detector for sensing target maneuvers and then develops the combination of the estimator, detector, and a "simple" Kalman filter to form a tracker for maneuvering targets. Finally, some simulation results are presented. A relationship between the actual residuals, assuming target maneuvers, and the theoretical residuals of the "simple" Kalman filter that assumes no maneuvers, is first formulated. The estimator then computes a constant acceleration input vector that best fits that relationship. The result is a least squares estimator of the input vector which can be used to update the "simple" Kalman filter. Since typical targets spend considerable periods of time in the constant course and speed mode, a detector is used to guard against automatic updating of the "simple" Kalman filter. A maneuver is declared, and updating performed, only if the norm of the estimated input vector exceeds a threshold. The tracking sclheme is easy to implement and its capability is illustrated in three tracking examples.

A Kalman Filter Based Tracking Scheme with Input Estimation

Sensors at separate locations measuring either the time difference of arrival (TDOA) or time of arrival (TOA) of the signal from an emitter can determine its position as the intersection of hyperbolae for TDOA and of circles for TOA. Because of measurement noise, the nonlinear localization equations become inconsistent; and the hyperbolae or circles no longer intersect at a single point. It is now necessary to find an emitter position estimate that minimizes its deviations from the true position. Methods that first linearize the equations and then perform gradient searches for the minimum suffer from initial condition sensitivity and convergence difficulty. Starting from the maximum likelihood (ML) function, this paper derives a closed-form approximate solution to the ML equations. When there are three sensors on a straight line, it also gives an exact ML estimate. Simulation experiments have demonstrated that these algorithms are near optimal, attaining the theoretical lower bound for different geometries, and are superior to two other closed form linear estimators.

Yiu-Tong Chan

Papers

A simple and efficient estimator for hyperbolic location

Least squares algorithms for time-of-arrival-based mobile location

Time-of-arrival based localization under NLOS conditions

A Kalman Filter Based Tracking Scheme with Input Estimation

Exact and approximate maximum likelihood localization algorithms