Robust Vehicle Positioning Based on Multi-Epoch and Multi-Antenna TOAs in Harsh Environments

An, Xinyuan; Zhao, Sihao; Cui, Xiaowei; Liu, Gang; Lu, Mingquan

doi:10.1109/tits.2022.3190023

Cited by 11 publications

(8 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking the ±3σ of the measurement noise into consideration, we can replace the true tag-anchor distances in (8) by the TOF measurement δ ik ± 3σ. Note that L and U are symmetrical.…”

Section: A Step 1: Edm Completionmentioning

confidence: 99%

“…They aim to provide guidance capability with flexible deployment, easy AGV route customization and low maintenance cost. However, these positioning technologies also face their own challenges of deterioration in accuracy and availability, e.g., error accumulation for the inertial navigation system, appalling weather for the laser-based system, weak illumination for the visionbased system, and blockages for the radio positioning system [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Rigid Body Localization Based on Euclidean Distance Matrix Completion for AGV Positioning Under Harsh Environment

Cui

Zhao

et al. 2023

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

“…Taking the ±3σ of the measurement noise into consideration, we can replace the true tag-anchor distances in (8) by the TOF measurement δ ik ± 3σ. Note that L and U are symmetrical.…”

Section: A Step 1: Edm Completionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Rigid Body Localization Based on Euclidean Distance Matrix Completion for AGV Positioning Under Harsh Environment

Cui

Zhao

et al. 2023

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

“…RF emission detection techniques such as received signal strength indicator (RSSI) [46] and time-difference-of-arrival (TDOA) [47] were employed in this work. These two techniques provide the most amount of information without adding any additional hardware and complexity to the framework for vehicle localization; other techniques such as angle-of-arrival (AOA) [48] and time-of-arrival (TOA) [47], [49] require additional antennas, additional hardware, and tight timing synchronization between transmitter and receiver. The localization and tracking framework was assumed to possess mobile sensors connected to a centralized cloud center or fusion center (FC).…”

Section: Proposed Attack Detection Frameworkmentioning

confidence: 99%

Phantom Car Attack Detection via Passive Opportunistic RF Localization

et al. 2023

View full text Add to dashboard Cite

A novel opportunistic approach of passive RF localization is presented for detecting "phantom car" attacks, i.e., vehicles intentionally reporting false position/velocity information to surrounding vehicles and communication networks. Current state-of-the-art approaches for vehicle localization mostly rely on either: (i) self-reported position/velocity updates obtained via navigation technologies such as GPS, or (ii) cooperative communication approaches involving multiple vehicles exchanging situational awareness information with each other. In both cases, these approaches assume that all vehicles involved truthfully share their actual location information, which might not be the case if malicious users are present within the traffic flow. The proposed approach does not make this assumption. Rather, it extracts location information of vehicles operating on the road without the need to cooperate with them. Multiple sensors surrounding these vehicles are opportunistically and passively measured for their RF emission characteristics (e.g., received signal strength, time difference of arrival) based on using on-board widely available wireless signals (e.g., 5G, Bluetooth, WiFi). These RF emissions are not intentionally designed to be used in localization applications. Their characteristics are processed via data fusion and bounded via vehicle dynamics behavioral models before being compared against reported positions within the vehicular communications network. This paper presents the phantom car attack model used in this work to compromise conventional vehicle localization techniques, as well as the framework and its respective sub-components implementing the proposed detection approach. To evaluate the feasibility of the proposed approach, a custom-built Python-based computer simulation platform is described that accurately models the vehicular environment and its associated RF emissions characteristics. Finally, a simple hardware field experiment evaluating the performance of the proposed approach incorporating RF localization, data fusion, and vehicle behavioral dynamics modules illustrates the viability of identifying phantom car attacks within a non-cooperative framework.

show abstract

“…In this sense, the antenna positioning problem (APP) is identified as a critical challenge in the planning and management of cellular networks, having a decisive impact on network quality. This problem, which is fundamental at all stages of the lifecycle of mobile networks from 2G to 4G and LTE, encompasses frequency allocation, mobility management, and the optimization of antenna location and number to ensure maximum coverage [1,2,[4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Through a variety of mathematical formulations and models, such as those introduced by Reininger and Calégari et al [8,9], the goal is to achieve optimal coverage of specific geographical areas with the smallest possible number of antennas. These models account for signal path loss, influenced by physical and environmental factors, and the impact of obstacles on signal transmission and reception [5,10].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach

Calles-Esteban,

Olmedo,

Hellín

et al. 2024

Sensors

View full text Add to dashboard Cite

The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical solutions employing advanced algorithms, such as evolutionary algorithms. This study focuses on developing innovative web tools harnessing genetic algorithms to optimize antenna positioning, starting from propagation loss calculations. To achieve this, seven empirical models were reviewed and integrated into an antenna positioning web tool. Results demonstrate that, with minimal configuration and careful model selection, a detailed analysis of antenna positioning in any area is feasible. The tool was developed using Java 17 and TypeScript 5.1.6, utilizing the JMetal framework to apply genetic algorithms, and features a React-based web interface facilitating application integration. For future research, consideration is given to implementing a server capable of analyzing the environment based on specific area selection, thereby enhancing the precision and objectivity of antenna positioning analysis.

show abstract

Robust Vehicle Positioning Based on Multi-Epoch and Multi-Antenna TOAs in Harsh Environments

Cited by 11 publications

References 58 publications

Efficient Rigid Body Localization Based on Euclidean Distance Matrix Completion for AGV Positioning Under Harsh Environment

Efficient Rigid Body Localization Based on Euclidean Distance Matrix Completion for AGV Positioning Under Harsh Environment

Phantom Car Attack Detection via Passive Opportunistic RF Localization

Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach

Contact Info

Product

Resources

About