Exploiting AoA Estimation Accuracy for Indoor Localization: A Weighted AoA-Based Approach

Zheng, Yang; Sheng, Min; Liu, Junyu; Li, Jiandong

doi:10.1109/lwc.2018.2853745

Cited by 108 publications

(32 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional indoor localization technology based on BS signal can be categorized into three groups: time based [26], [27], angle based [28], [29] and fingerprint based algorithm [30], [31]. Time-based and angle-based algorithms have low commercial cost, while they are usually greatly affected by the physical environment and channel quality.…”

Section: B Related Indoor Localization Technologymentioning

confidence: 99%

An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks

Wei

Xie

2021

IEEE Access

View full text Add to dashboard Cite

In this paper, a commercial high-accuracy, low-cost and real-time indoor building-level localization system is proposed, which is applicable for locating the Minimization of Drive-Tests (MDT) data in the long-term-evolution (LTE) cellular communication network system. The system utilizes MDT data containing Global Navigation Satellite Systems (GNSS) information which is easy to collect and low cost to assist indoor localization, instead of using indoor drive test (DT) data which needs high manual collection and maintenance costs. In order to compensate for the loss of location accuracy, this paper innovatively divide the online process into two phases: indoor and outdoor (IO) identification phase and indoor localization phase. A real-time and precise GMM-based unsupervised algorithm is applied to identifying if the non-GNSS MDT data is in indoor environment in IO identification phase. Then, a multi-class classification algorithm based on Bayesian classifier is used to locate indoor MDT data to the specific building. The results of experiments conducted in an in-service LTE network using more than 100 LTE base stations demonstrate that the proposed technique yields a IO identification accuracy of 90% and an indoor location accuracy of 49.3m(@67%) respectively, which provides at least 30.2% enhancement in location accuracy compared to traditional technology without DT fingerprint database. This proposed indoor localization technique is applicable in network optimization and Operation and Maintenance (O&M) to assist communication service providers to reduce their operating expense (OPEX) by locating those MDT data without GNSS information. INDEX TERMS Long-term-evolution, indoor localization, indoor and outdoor identification, Bayesian classifier, Gaussian mixture model, penetration loss.

show abstract

Section: B Related Indoor Localization Technologymentioning

confidence: 99%

An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks

Wei

Xie

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, due to the high demand for LBS, significant attention has been made on the development of indoor positioning systems (IPS) recently. Typical ranging techniques based on received-signal-strength-indicator (RSSI) [1], time-of-arrival (ToA) [2], time-difference-of-arrival (TDoA) [3], angle-of-arrival (AoA) [4], and channel-state-information (CSI) [5] have been proposed using various access technologies such as Wi-Fi [6], Bluetooth [7], ultra wide band (UWB) [8], and radio-frequency identification tags (RFID) [9] for indoor positioning. Most ranging techniques require at least three known anchor nodes to calculate the location of the unknown target.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Machine Learning for Indoor Positioning

et al. 2020

View full text Add to dashboard Cite

Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.

show abstract

“…Source localization is a classical subject due to its importance in the applications of sensor networks, radar, and underwater navigation [1][2] . In such applications, the main idea of source localization is to use the noisy measurement, such as the timedifference-of-arrival (TDOA) [3][4][5] [6] , time-of-arrival (TOA) [7] [8] , and angle-of-arrival (AOA) [9] [10] .…”

Section: Introductionmentioning

confidence: 99%

Efficient Underwater Acoustical Localization Method Based On Time Difference and Bearing Measurements

Zhang

Shin

et al. 2021

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

This paper addresses the underwater acoustical localization problem by using the time-difference-of-arrival (TDOA) and bearing-angle-of-arrival (BAOA) measurements. For the underwater acoustic equipment, such as the ultra-short baseline system (USBL), whose bearing measurements are different from the traditional angle-of-arrival (AOA) model, a closed-form solution for the hybrid TDOA/BAOA-based source localization problem is developed. However, the solution suffers from the measurement noise and cannot achieve the Cramer-Rao lower bound (CRLB) performance in the case of large measurement noise. Thus, an iterative constrained weighted least squares method is presented to further minimize the error in the case of large noise. The CRLB for hybrid TDOA/BAOA source localization is analyzed and the solution is proved to achieve the CRLB performance. Numerical simulations and field tests demonstrate that the proposed method outperforms the traditional methods in terms of estimation bias and accuracy. It can achieve the CRLB performance better.

show abstract

Exploiting AoA Estimation Accuracy for Indoor Localization: A Weighted AoA-Based Approach

Cited by 108 publications

References 11 publications

An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks

An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks

A Survey of Machine Learning for Indoor Positioning

Efficient Underwater Acoustical Localization Method Based On Time Difference and Bearing Measurements

Contact Info

Product

Resources

About