Kam Tim Woo

GPS Localization Accuracy Improvement by Fusing Terrestrial TOA Measurements

This paper explores the use of terrestrial time of arrival (TOA) measurements to improve the initial Global Positioning System (GPS) location fix accuracy. First, we present a geometric approach when a GPS location fix and one TOA measurement are available. Then, a more general hybrid GPS/TOA method via the Weighted Least Square Estimator (WLSE) is proposed. To simplify the calculation, a closed-form solution based on the two-step Least Square approach is also designed. The Cramer-Rao Lower Bound (CRLB) is derived as a performance benchmark. Simulation results exhibit excellent performance of the proposed methods which attain the CRLB in different scenarios. The proposed methods work even if only one TOA measurement (in addition to a GPS location fix) is available and the corresponding accuracy improvement (compared with the initial GPS location fix) can be as much as 30%.

Performance analysis of localization algorithms

Workpiece localization, with novel applications such as workpiece setup, refixturing and dimensional inspections, is a problem of permanent importance in manufacturing. Using the popular least square formulation, several geometric algorithms have been developed for workpiece localization over the last few years. In this paper, we analyze and compare the performance of three localization algorithms based on the following criteria: (a) robustness with respect to variations in initial conditions; (b) accuracy of computed results; and (c) computational efficiency. We develop an approach for improving the robustness of the algorithms for workpieces with sculptured surfaces for which the region of convergence is typically small. Based on simulation results, we also discuss sensitivity of the algorithms with respect to the number of measurement points and give a lower bound on this number for recovering a Euclidean transformation with certain accuracy.

Hybrid TOA/AOA-Based Mobile Localization with and without Tracking in CDMA Cellular Networks

This paper proposes a hybrid TOA/AOA (Time of Arrival/Angle of Arrival)-based localization algorithm for Code Division Multiple Access (CDMA) networks. The algorithm extends the Taylor Series Least Square (TS-LS) method originally developed for TOA-based systems to incorporate AOA measurements. In addition, tracking algorithms utilizing velocity and acceleration measurements are investigated. Simulation results illustrate that the proposed TOA/AOA TS-LS can provide better performance than conventional schemes in localization accuracy and in reduced likelihood of encountering non-convergence problem compared with TOA TS-LS. Tracking algorithms using the Extended and Unscented Kalman Filter (EKF and UKF) can track the objects relatively well, further decreasing the positioning error. UKF is found to provide closer tracking of the trajectory than EKF, for it truly captures the statistical mean and variance of the noises.

Energy Efficient Assisted GPS Measurement and Path Reconstruction for People Tracking

In the use of a wearable GPS and cellular tracker for applications such as elderly tracking, device power consumption is an important consideration. To save power, assisted GPS (AGPS) location fixes should not be performed frequently. On the other hand, we also do not want to lose important information about the users mobility patterns and routines. To solve this dilemma, in this paper, we present the design of a system that intelligently schedules on-line AGPS location fixes only when necessary based on information extracted from users historical mobility data, and then reconstruct the user path based on these sparsely taken on-line location fixes. Experimental results show that our on-line algorithm can significantly reduce the number of AGPS fixes needed and the reconstruction method works well without a priori knowledge of a map and streets information.

Indoor Localization via Discriminatively Regularized Least Square Classification

In this paper, we address the received signal strength (RSS)-based indoor localization problem in a wireless local area network (WLAN) environment and formulate it as a multi-class classification problem using survey locations as classes. We present a discriminatively regularized least square classifier (DRLSC)-based localization algorithm that is aimed at making use of the class label information to better distinguish the RSS samples taken from different locations after proper transformation. Besides DRLSC, two other regularized least square classifiers (RLSCs) are also presented for comparison. We show that these RLSCs can be expressed in a unified problem formulation with a closed-form solution and convenient assessment of the convexity of the problem. We then extend the linear RLSCs to their nonlinear counterparts via the kernel trick. Moreover, we address the missing value problem, utilize clustering to reduce the training and online complexity, and introduce kernel alignment for fast kernel parameter tuning. Experimental results show that, compared with other methods, the kernel DRLSC-based algorithm achieves superior performance for indoor localization when only a small fraction of the data samples are used.

An Energy-Efficient Elderly Tracking Algorithm

Location and tracking of healthy or infirm elders is a potentially useful application as society aging is accelerating worldwide and as technologies such as cellular positioning and Assisted Global Positioning System (AGPS) are maturing. For a wearable device to be used in such an application, the battery operating hour is often a key consideration. This paper describes an energy-efficient cellular and AGPS tracking algorithm based on the concept of Personal Common Location (PCL) that we define as the locations at which an elder spends most of his/her time, as recognized from historic cell ID record. During the tracking process, AGPS fixes are avoided if the elderly carrier is determined to be located within a PCL, if the destination PCL can be predicted, or if AGPS signals are likely to be unavailable. This paper also describes our approach for off-line PCL recognition, the necessity of cell ID clustering, as well as the results from our tracking experiments.

Histogram Based Particle Filtering with Online Adaptation for Indoor Tracking in WLANs

Indoor localization using signal strength in Wireless Local Area Networks is becoming increasingly prevalent in today’s pervasive computing applications. In this paper, we propose an indoor tracking algorithm under the Bayesian filtering and machine learning framework. The main idea is to apply a graph-based particle filter to track a person’s location on an indoor floor map, and to utilize the machine learning method to approximate the likelihood of an observation at various locations based on the calibration data. Histograms are used to approximate the RSS distributions at the survey points, and Nadaraya–Watson kernel regression is adopted to recover the distributions at non-survey points only from the nearby locations. In addition, we also propose a simple algorithm to continuously update the radio map with the online measurements. A series of experiments are carried out in an office environment. Results show that the proposed Histogram Based Particle Filtering (HBPF)/HBPF with Online Adaptation achieves superior performance than other existing algorithms while retaining low computational complexity.

MCMC-based human tracking with stereo cameras under frequent interaction and occlusion

Human Tracking in a video sequence is an important task in civilian surveillance. Successful human tracking provides data for security-purposes. However, human tracking in video sequences is always a challenging problem. Due to the rapid changes in shape with irregular motion, typical methods may not have good results, especially under occlusion and interaction. Recently, methods based on multiple cameras have been proposed. However, this requires a high computation cost. In the stereo cameras approach, 3D information is obtained and projected onto an occupancy map. In this paper, we propose an algorithm combining the occlusion and interaction model and Markov Chain Monte Carlo (MCMC) such that humans can be tracked under frequent interaction and occlusion. We have successfully reduced the number of failures by 78%. We present an efficient and effective algorithm under frequent interaction and occlusion.

Human tracking in crowded environment with stereo cameras

In this paper, a human detection and tracking system in a crowded environment is presented. The biggest challenge of the system is to detect occluded people from the captured video where visible information of the occluded people in a camera is reduced. Many researchers proposed to reconstruct the occluded information from other cameras which are installed in the same place with different viewing angles, but, hardware cost and computation cost will be increased. In this paper, a stereo camera system is used with a novel occlusion model to solve the occlusion problem. Result shows that it significantly reduces the hardware cost and computation cost of the system while maintaining a good tracking result under severe occlusions.

A Fast Constant Modulus Algorithm for Blind Equalization

In the past decade, equalizers were employed to cancel the intersymbol interference introduced by the channel. Meanwhile, blind equalizers were proposed to maintain a higher channel capacity than that of an equalizer with supervised training. However, the main drawback of blind equalization is the low speed of convergence. This paper proposed a novel equalizer for blind estimation with QAM constellation. Simulation results show that our proposed equalizer can achieve a faster convergence, smaller steady state errors and lower computational complexity when compared to those traditional blind equalizers