A Survey on the Impact of Multipath on Wideband Time-of-Arrival Based Localization

Aditya, Sundar; Molisch, Andreas F.; Behairy, Hatim

doi:10.1109/jproc.2018.2819638

Cited by 80 publications

(43 citation statements)

References 121 publications

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“…Since the signals propagate in different ways and different distances, signals will arrive in the receiver with the same information but with different phases. In general, the channel multipath propagation impulse response at a particular time, t, can be written as [25]:…”

Section: The Multipath Indetermination and Triangulationmentioning

confidence: 99%

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A System to improve the management of 5G and IoT Networks by determining the Mobile Position

Faria

Santos

et al. 2019

J. Microw. Optoelectron. Electromagn. Appl.

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The interference in adjacent cells and the control of the boundaries have being vastly investigated since the conception of the first cell phone networks. A very large number of small cells are required for new 5G mobile networks, and therefore it is even more important to determine the correct mobile station positioning as well as the control boundaries. In order to minimize these problems, this paper proposes a simple and efficient system that improves the control of the Mobile Management Entity (MME) defined in the Release 8 of 3GPP. The system uses a tracking arrangement capable of determining the direction of the mobile station in the cell area. This information can be used to predict handover between adjacent nodes (changing of cell) minimizing a great problem, the high traffic in the backhaul network. In order to reach these goals, two or more receiver antennas are used as a Radio Direction Finder (RDF) and phase controlled directional antennas or massive multiple-input and multiple-output antennas pointing to different irradiation channels towards different directions. The theoretical section developed in this study was successfully confirmed by the experimental setup with results very closed to the developed formulation.

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Section: The Multipath Indetermination and Triangulationmentioning

confidence: 99%

“…To avoid this mistake, some techniques used in localization can be applied, e.g. the triangulation and the time of arrival [25]. The indetermination produced by the multi-path is a great problem to determine the exact position of a mobile station in a cell.…”

Section: The Multipath Indetermination and Triangulationmentioning

confidence: 99%

A System to improve the management of 5G and IoT Networks by determining the Mobile Position

Faria

Santos

et al. 2019

J. Microw. Optoelectron. Electromagn. Appl.

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“…Location determination is one of the classical research fields in signal processing. With the rise of the fifth-generation communication system (5G), Internet of things, automatic pilot and unmanned aerial vehicle, localization continues to receive great attention [1][2][3][4]. Based on different type of measurements, the common localization methods estimate the source position using time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), Doppler frequency, Fingerprint and received signal strength (RSS) [5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form

Gan¹,

Cong²,

Sun

2020

Sensors

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The subject of localization has received great deal attention in the past decades. Although it is perhaps a well-studied problem, there is still room for improvement. Traditional localization methods usually assume the number of sensors is sufficient for providing desired performance. However, this assumption is not always satisfied in practice. This paper studies the time of arrival (TOA)-based source positioning in the presence of sensor position errors. An error refined solution is developed for reducing the mean-squared-error (MSE) and bias in small sensor network (the number of sensors is fewer) when the noise or error level is relatively large. The MSE performance is analyzed theoretically and validated by simulations. Analytical and numerical results show the proposed method attains the Cramér-Rao lower bound (CRLB). It outperforms the existing closed-form methods with slightly raising computation complexity, especially in the larger noise/error case.

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“…In this regard, to perform micro-localization based on BLE tags in IoT applications, the main-stream methodology is to use the Received Signal Strength Indicator (RSSI) [18]. The RSSIbased solutions, however, are prone to multipath fading and drastic fluctuations in the indoor environment [19]. A technical challenge in RSSI-based solutions is the presence of non-linearities at the lowlevel system.…”

Section: Introductionmentioning

confidence: 99%

Belief Condensation Filtering for RSSI-Based State Estimation in Indoor Localization

Mehryar

Malekzadeh

Mazuelas

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of recent interest in localization/tracking within indoor environments based on Bluetooth Low Energy (BLE) technology. The basic motive behind BLE-enabled IoT applications is to provide advanced residential and enterprise solutions in an energy efficient and reliable fashion. Although recently different state estimation (SE) methodologies, ranging from Kalman filters, Particle filters, to multiple-modal solutions, have been utilized for BLEbased indoor localization, there is a need for ever more accurate and real-time algorithms. The main challenge here is that multipath fading and drastic fluctuations in the indoor environment result in complex non-linear, non-Gaussian estimation problems. The paper focuses on an alternative solution to the existing filtering techniques and introduce/discuss incorporation of the Belief Condensation Filter (BCF) for localization via BLE-enabled beacons. The BCF is a member of the universal approximation family of densities with performance bound achieving accuracy and efficiency in sequential SE and Bayesian tracking. It is a resilient filter in harsh environments where nonlinearities and non-Gaussian noise profiles persist, as seen in such applications as Indoor Localization.

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A Survey on the Impact of Multipath on Wideband Time-of-Arrival Based Localization

Cited by 80 publications

References 121 publications

A System to improve the management of 5G and IoT Networks by determining the Mobile Position

A System to improve the management of 5G and IoT Networks by determining the Mobile Position

Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form

Belief Condensation Filtering for RSSI-Based State Estimation in Indoor Localization

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