3D moving target tracking with measurement fusion of TDoA/FDoA/AoA

Seo, Hyeondeog; Kim, Hyo-Won; Kang, Jeongwan; Jeong, Incheol; Ahn, Woogeun; Kim, Sunwoo

doi:10.1016/j.icte.2018.07.003

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…M-step: µ ← µ + 1 12: end while filter based on the AoA estimation [35]- [37]. These attempts deal mainly with simulation results and do not involve a specific application.…”

Section: Particle Filter Implementationmentioning

confidence: 99%

AoA-Based Localization System Using a Single IoT Gateway: An Application for Smart Pedestrian Crossing

et al. 2021

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During the last decade, the concept of smart cities is gaining popularity, and many cities all around the globe are adopting it. Lately, the Smart Pedestrian Crossing (SPC) idea has become an important part of the smart city concept. The main objective of the SPC is to achieve a safe and smooth traffic flow of pedestrians and vehicles in the smart city. In this paper, we present a simple yet efficient localization system which is suitable for locating people over a pedestrian crossing. By adopting the proposed localization system, the people flow over the pedestrian crossing can be observed. Consequently, a safe and efficient traffic flow in the smart city can be achieved. The proposed system utilizes Internet of Things (IoT) transceivers that are carried by the pedestrians or embedded in the pedestrians' smart devices. These transceivers can be activated to transmit a signal every few seconds when the pedestrian passes over a pedestrian crossing. These periodic signals will be received by a single Angle of Arrival (AoA) estimation system to estimate the direction of the pedestrian. Furthermore, the pedestrian direction and the environmental features of the pedestrian crossings are utilized in a 1D particle filter to estimate accurately the pedestrian's location. The proposed system has been validated experimentally in the smart zone of Antwerp city. The experimental results reveal that the proposed localization system can provide an accurate, cost effective and reliable localization solution to this specific problem without violating the pedestrian privacy. INDEX TERMS AoA, angle of arrival, DoA, direction of arrival, Internet of Things, IoT, environmental features localization, smart pedestrian crossing, smart cities, particle filter, SAGE algorithm.

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“…M-step: µ ← µ + 1 12: end while filter based on the AoA estimation [35]- [37]. These attempts deal mainly with simulation results and do not involve a specific application.…”

Section: Particle Filter Implementationmentioning

confidence: 99%

AoA-Based Localization System Using a Single IoT Gateway: An Application for Smart Pedestrian Crossing

et al. 2021

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“…Furthermore, RSS-based and ToA-based methods require knowledge about the transmit power and the transmit time used by the target AV, which are not available in the case of a spoofing target [13]. Such knowledge is not necessary for TDoA-based methods, making it a favorable choice for MLAT with CWN [10,19]. In [19], Seo et al proposed a particle filter-based 3D target tracking algorithm with measurement fusion of TDoA, frequency difference of arrival (FDoA), and angle of arrival (AoA).…”

Section: A Related Workmentioning

confidence: 99%

Aerial Vehicles Tracking Using Noncoherent Crowdsourced Wireless Networks

Sallouha

Chiumento

Pollin

2021

IEEE Trans. Veh. Technol.

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Air traffic management (ATM) of manned and unmanned aerial vehicles (AVs) relies critically on ubiquitous location tracking. While technologies exist for AVs to broadcast their location periodically and for airports to track and detect AVs, methods to verify the broadcast locations and complement the ATM coverage are urgently needed, addressing anti-spoofing and safe coexistence concerns. In this work, we propose an ATM solution by exploiting noncoherent crowdsourced wireless networks (CWNs) and correcting the inherent clock-synchronization problems present in such non-coordinated sensor networks. While CWNs can provide a great number of measurements for ubiquitous ATM, these are normally obtained from unsynchronized sensors. This article first presents an analysis of the effects of lack of clock synchronization in ATM with CWN and provides solutions based on the presence of few trustworthy sensors in a large non-coordinated network. Secondly, autoregressivebased and long short-term memory (LSTM)-based approaches are investigated to achieve the time synchronization needed for localization of the AVs. Finally, a combination of a multilateration (MLAT) method and a Kalman filter is employed to provide an anti-spoofing tracking solution for AVs. We demonstrate the performance advantages of our framework through a dataset collected by a real-world CWN. Our results show that the proposed framework achieves localization accuracy comparable to that acquired using only GPS-synchronized sensors and outperforms the localization accuracy obtained based on state-of-the-art CWN synchronization methods.

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“…As the figure illustrates CRLB varies from sub-meter values near the edge closest to the sensor network, to more than two meters near the two farthermost vertices to the sensor network. Although in this simulation we suppose that signal-to-noiseratio (SN R) is 20dB, much higher SNRs is accessible due to the independency of noise in antennas pairs that results in the cancellation of noise terms in (9). Assuming identical SNR at input antennas, CRLB illustrated in Figure 4 is very close in shape and values to CRLB of the same sensor network that utilizes directional antennas with 0.02 o in standard deviation of directional of arrival (DOA) estimation error [31], [32].…”

Section: Cramer-rao Lower Bound Of Error (Crlb)mentioning

confidence: 99%

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

Hejazi¹,

Joneidi²,

Rahnavard³

2020

Preprint

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In this paper, a framework for localization of multiple co-channel transmitters using phase difference measurements between two antennas mounted on sensors of a sensor network is proposed. To pursue localization, we equip each sensor with two antennas and we use temporal cross-correlations between the received signals of the two antennas to extract the phase differences between each antenna pairs, named as phase interferometry measurements (PIMs), provoked by each transmitters using tensor decomposition. We calculate Cramer-Rao lower bound of error of localization using PIMs. Our simulation results show that highly accurate estimations can be achieved using PIMs. We also compare the accuracy of our proposed technique with a sensor network that exploits highly directional linear array antennas and show that our proposed technique can perform similar to a network that employs very large antenna arrays.

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3D moving target tracking with measurement fusion of TDoA/FDoA/AoA

Cited by 17 publications

References 13 publications

AoA-Based Localization System Using a Single IoT Gateway: An Application for Smart Pedestrian Crossing

AoA-Based Localization System Using a Single IoT Gateway: An Application for Smart Pedestrian Crossing

Aerial Vehicles Tracking Using Noncoherent Crowdsourced Wireless Networks

A Tensor-based Localization Framework Exploiting Phase Interferometry Measurements

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