Multipath tracking with LTE signals for accurate TOA estimation in the application of indoor positioning

Liu, Z.; Chen, Liang; Zhou, Xin; Shen, Nan; Chen, Ruizhi

doi:10.1080/10095020.2022.2108344

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…In LTE networks, the cell reference signal (CRS) is uniformly distributed throughout the LTE downlink channel and transmits periodically at a high rate, which has been used by researchers in navigation and positioning applications (Liu et al, 2023b).…”

Section: Signals In Navigation Positioningmentioning

confidence: 99%

“…The acquisition of high-precision navigation observation information through signal of opportunity (SOP) is a method to assist GNSS for high precision positioning. At present, WiFi (Shu et al, 2016, Yan et al, 2018, Gao et al, 2021, Bluetooth (Chen et al, 2013, Faragher and Harle, 2015, Zhuang et al, 2018 and cellular network signals (Driusso et al, 2017, Liu et al, 2023b, Shamaei and Kassas, 2021, Liu et al, 2023a are the widely used signals of opportunity in wireless positioning technology. Although WiFi and Bluetooth have the advantages of low-cost and lowpower consumption, they cannot provide high-precision LBSs to a large number of users under a wide area because of the limited coverage area of the base station (BS).…”

Section: Introductionmentioning

confidence: 99%

“…Researchers are gradually shifting their focus to positioning technologies based on cellular network signals. In (Driusso et al, 2017, Liu et al, 2023b, the researchers have developed several high-precision softwaredefined receivers (SDRs) for time-of-arrival (TOA) estimation that can be used in complex environments based on LTE signals. In (Shamaei and Kassas, 2021), Kimia Shamaei et al de- At present, wireless signal tracking methods are commonly based on the principle of DLL or PLL to develop SDRs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Toa Estimation of Cellular Network Signals Based on Machine Learning in Complex Urban Environments

Liu,

Chen,

Jiao

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The precision location-based services in complex environment is a challenge in the field of navigation and positioning. With the continuous development of wireless communication technology in recent years, cellular network signals such as LTE and 5G have emerged as unique advantages in navigation and positioning applications. This paper presents a time-of-arrival (TOA) estimation method based on machine learning, which can use cellular network signals to obtain accurate ranging results in low signal-to-noise ratio conditions. For this purpose, we first present the cellular network signals that can be applied in navigation and positioning. Then, we describe in detail the process of TOA estimation based on machine learning. Finally, we carried out vehicular experiments in an urban environment to test the performance of the proposed method. The test results demonstrate the feasibility of the proposed method and achieve metre-level ranging accuracy.

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Section: Signals In Navigation Positioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Toa Estimation of Cellular Network Signals Based on Machine Learning in Complex Urban Environments

Liu,

Chen,

Jiao

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

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“…Generally, indoor positioning techniques are based on the characteristic parameters of the signal received by the access point (AP). Typical characteristic parameters include time of arrival (TOA), time differences of arrival (TDOA), channel state information (CSI), angle of arrival (AOA) and received signal strength (RSS), etc [2][3][4][5][6][7]. Both TOA and TDOA estimate the target location based on the signal propagation time.…”

Section: Introductionmentioning

confidence: 99%

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Zhang,

Xu,

Gao

et al. 2024

Meas. Sci. Technol.

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Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

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“…At present, many positioning methods complementary to GNSS have been developed to provide ubiquitous location-based services (LBS). Compared to positioning methods that require the deployment of positioning facilities in advance, such as WiFi [2], Ultra-Wide Band (UWB) [3], Long Term Evolution (LTE) [4], 5G [5], audio [6], etc. Pedestrian Dead Reckoning (PDR) is an independent positioning method.…”

Section: Introductionmentioning

confidence: 99%

Robust Pedestrian Dead Reckoning Integrating Magnetic Field Signals and Digital Terrestrial Multimedia Broadcasting Signals

et al. 2023

Self Cite

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Currently, many positioning technologies complementary to Global Navigation Satellite System (GNSS) are providing ubiquitous positioning services, especially the coupling positioning of Pedestrian Dead Reckoning (PDR) and other signals. Magnetic field signals are stable and ubiquitous, while Digital Terrestrial Multimedia Broadcasting (DTMB) signals have strong penetration and stable transmission over a large range. To improve the positioning performance of PDR, this paper proposes a robust PDR integrating magnetic field signals and DTMB signals. In our study, the Spiking Neural Network (SNN) is first used to learn the magnetic field signals of the environment, and then the learning model is used to detect the magnetic field landmarks. At the same time, the DTMB signals are collected by the self-developed signal receiver, and then the carrier phase ranging of the DTMB signals is realized. Finally, robust pedestrian positioning is achieved by integrating position information from magnetic field landmarks and ranging information from DTMB signals through Extended Kalman Filter (EKF). We have conducted indoor and outdoor field tests to verify the proposed method, and the outdoor field test results showed that the positioning error cumulative distribution of the proposed method reaches 2.84 m at a 68% probability level, while that of the PDR only reaches 8.77 m. The proposed method has been validated to be effective and has good positioning performance, providing an alternative solution for seamless indoor and outdoor positioning.

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Multipath tracking with LTE signals for accurate TOA estimation in the application of indoor positioning

Cited by 12 publications

References 34 publications

The Toa Estimation of Cellular Network Signals Based on Machine Learning in Complex Urban Environments

The Toa Estimation of Cellular Network Signals Based on Machine Learning in Complex Urban Environments

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Robust Pedestrian Dead Reckoning Integrating Magnetic Field Signals and Digital Terrestrial Multimedia Broadcasting Signals

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