Carrier Phase Ranging for Indoor Positioning With 5G NR Signals

Chen, Liang; Zhou, Xin; Chen, Feifei; Yang, Lie‐Liang; Chen, Ruizhi

doi:10.1109/jiot.2021.3125373

Cited by 82 publications

(24 citation statements)

References 41 publications

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“…In addition, multiple reflected signals that overlap the direct path signal distort the first peak of the combined signal [ 10 ]. Existing ToA estimation algorithms are based on thresholding the first peak, using a prior model [ 3 ], or the MUSIC/ESPRIT algorithm. The performance of the proposed approach was compared against MUSIC and ESPRIT.…”

Section: Resultsmentioning

confidence: 99%

“…Due to the emergence of 5G and 6G technology, people have higher and higher requirements for indoor positioning [ 1 , 2 ]. According to Market&Markets research data, the global market size related to indoor location is expected to reach USD 18.74 billion by 2025 [ 3 ]. The huge market demand has become a key driver of location-based applications in the future.…”

Section: Introductionmentioning

confidence: 99%

“…At present, in addition to satellite-based positioning technology [ 1 , 2 ], various positioning technologies are available, such as Bluetooth [ 4 , 5 ], radar [ 6 , 7 ], radio frequency identification [ 8 ], ultra-wideband [ 9 ], geomagnetic field [ 10 ], visible light [ 11 ], thermal infrared [ 12 ], sound [ 13 ], 5G cellular networks [ 3 ], and so on. Most of these positioning technologies, however, require additional hardware anchor points and therefore incur additional costs.…”

Section: Introductionmentioning

confidence: 99%

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The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location

Peng

Tang

et al. 2022

Sensors

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With the increasing demand for wireless location services, it is of great interest to reduce the deployment cost of positioning systems. For this reason, indoor positioning based on WiFi has attracted great attention. Compared with the received signal strength indicator (RSSI), channel state information (CSI) captures the radio propagation environment more accurately. However, it is necessary to take signal bandwidth, interferences, noises, and other factors into account for accurate CSI-based positioning. In this paper, we propose a novel dictionary filtering method that uses the direct weight determination method of a neural network to denoise the dictionary and uses compressive sensing (CS) to extract the channel impulse response (CIR). A high-precision time-of-arrival (TOA) is then estimated by peak search. A median value filtering algorithm is used to locate target devices based on the time-difference-of-arrival (TDOA) technique. We demonstrate the superior performance of the proposed scheme experimentally, using data collected with a WiFi positioning testbed. Compared with the fingerprint location method, the proposed location method does not require a site survey in advance and therefore enables a fast system deployment.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location

Peng

Tang

et al. 2022

Sensors

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“…Possible solutions to overcome these accuracy limitations on the way to 6G are carrier aggregation for positioning and carrier-phase based positioning [77]. Carrier aggregation aims to achieve a higher overall effective bandwidth by combining PRSs received on different carriers into one signal with a very high overall bandwidth [25].…”

Section: Beyond 5gmentioning

confidence: 99%

Positioning in 5G and 6G Networks—A Survey

Mogyorósi

Revisnyei

Pašić

et al. 2022

Sensors

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Determining the position of ourselves or our assets has always been important to humans. Technology has helped us, from sextants to outdoor global positioning systems, but real-time indoor positioning has been a challenge. Among the various solutions, network-based positioning became an option with the arrival of 5G mobile networks. The new radio technologies, minimized end-to-end latency, specialized control protocols, and booming computation capacities at the network edge offered the opportunity to leverage the overall capabilities of the 5G network for positioning—indoors and outdoors. This paper provides an overview of network-based positioning, from the basics to advanced, state-of-the-art machine-learning-supported solutions. One of the main contributions is the detailed comparison of machine learning techniques used for network-based positioning. Since new requirements are already in place for 6G networks, our paper makes a leap towards positioning with 6G networks. In order to also highlight the practical side of the topic, application examples from different domains are presented with a special focus on industrial and vehicular scenarios.

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“…The indoor positioning has thrived for more than twenty years, and various technologies and methods have emerged to meet the requirements of location-based services in Global Navigation Satellite System (GNSS) denied environments. According to the principles of indoor positioning technologies, they can be divided into wireless signal positioning (Alvarez-Merino et al, 2021;Chen et al, 2021;Li et al, 2020;Poulose et al, 2020;Ye et al, 2022), inertial navigation (Feng et al, 2020;Liu et al, 2021), computer visual positioning (Maheepala et al, 2020;Morar et al, 2020), and others (Huang et al, 2022;Kunhoth et al, 2020;Li et al, 2016;Ruiz et al, 2011;Xu et al, 2021). Wireless Fidelity (WiFi) plays a vital role in wireless signal positioning technologies because of its wide applications and high commercial value (Liu et al, 2020;Zhuang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Supplementary open dataset for WiFi indoor localization based on received signal strength

Wang

et al. 2022

Satell Navig

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Several Wireless Fidelity (WiFi) fingerprint datasets based on Received Signal Strength (RSS) have been shared for indoor localization. However, they can’t meet all the demands of WiFi RSS-based localization. A supplementary open dataset for WiFi indoor localization based on RSS, called as SODIndoorLoc, covering three buildings with multiple floors, is presented in this work. The dataset includes dense and uniformly distributed Reference Points (RPs) with the average distance between two adjacent RPs smaller than 1.2 m. Besides, the locations and channel information of pre-installed Access Points (APs) are summarized in the SODIndoorLoc. In addition, computer-aided design drawings of each floor are provided. The SODIndoorLoc supplies nine training and five testing sheets. Four standard machine learning algorithms and their variants (eight in total) are explored to evaluate positioning accuracy, and the best average positioning accuracy is about 2.3 m. Therefore, the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format. The dataset can be used for clustering, classification, and regression to compare the performance of different indoor positioning applications based on WiFi RSS values, e.g., high-precision positioning, building, floor recognition, fine-grained scene identification, range model simulation, and rapid dataset construction.

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Carrier Phase Ranging for Indoor Positioning With 5G NR Signals

Cited by 82 publications

References 41 publications

The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location

The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location

Positioning in 5G and 6G Networks—A Survey

Supplementary open dataset for WiFi indoor localization based on received signal strength

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