MOSAIC: Simultaneous Localization and Environment Mapping Using mmWave Without A-Priori Knowledge

Yassin, Ali; Nasser, Youssef; Al‐Dubai, Ahmed; Awad, Mariette

doi:10.1109/access.2018.2879436

Cited by 51 publications

(55 citation statements)

References 27 publications

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“…Vehicle to Infrastructure (V2I) communication relies on fast Dedicated Short Range Communication (DSRC) between a stationary Roadside Unit (RSU) and On-Board Unit (OBU) inside a moving vehicle [188]. Base station exchange covers a measurement area via a set of moving receivers [145], [153], [184], [186].…”

Section: ) Spatial Sweepingmentioning

confidence: 99%

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz that correspond to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.

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Section: ) Spatial Sweepingmentioning

confidence: 99%

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

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“…As the process exploits multipath mmWave propagation, the method also estimates the location of walls and obstacles in a simultaneous localization and mapping (SLAM) fashion. The work in [51] merges angle-of-arrival, received signal strength and time of arrival information to infer both the location of the client and the shape of the environment in a SLAM fashion. The devised system yields good performance in simulations.…”

Section: The Importance Of Location Informationmentioning

confidence: 99%

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

et al. 2019

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Millimeter wave (mmWave) communications have emerged as one of the most promising options to vastly increase wireless data rates due to the high bandwidth they offer. Given the high path loss at mmWave frequencies, such systems require directional antennas to achieve a good communication range. The communicating devices thus need to align the beam directions of their mmWave antennas. Due to the high penetration loss, the paths between the antennas also need to be free of blocking obstacles. This makes efficient and reliable operation of mmWave networks in dynamic environments very challenging. At the same time, the directionality reduces interference and allows to scale these networks to much higher access point and device densities. In this article, we discuss the above challenges and present techniques that allow mmWave networks to scale to high-density deployments, to adapt to dynamic and mobile environments, and to consistently achieve high data rates. This includes learning the environment to find different propagation paths, reacting timely to channel impairments such as blockage, and integrating mmWave networks with networks operating at lower-frequency for robustness. A key ingredient to enable these forms of adaptivity is the use of location information. Such mechanisms then turn a collection of very-high-speed but brittle mmWave links into an efficient, low-latency, and reliable network.

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“…A variety of works have been developed in the context of this paper. We can coarsely categorize these into three classes: methods based on geometry [7]- [10], methods based on message passing [4], [11], [12], and methods based on random finite set (RFS) theory.…”

Section: Introductionmentioning

confidence: 99%

“…In the first category, [7] formulates the SLAM problem using the geometric relation between observations, and a non-Bayesian estimator for the user location and extended Kalman filter for mapping are introduced. MmWave imaging for one single reflected path is utilized in [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

Kim

Granström

Gao

et al. 2020

IEEE Trans. Wireless Commun.

122

128

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5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays. However, radiobased positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. Building such a map is challenging, due to the inherent data association uncertainty, missed detection, and clutter. We propose a new method for cooperative vehicle positioning and mapping of the radio environment in order to address these challenges. The proposed method comprises a multimodel probability hypothesis density (PHD) filter and a map fusion routine, which is able to consider different types of objects and different fields of views (FoVs). Simulation results demonstrate that the proposed method handles the aforementioned challenges, and improves the vehicle positioning and mapping performance.

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MOSAIC: Simultaneous Localization and Environment Mapping Using mmWave Without A-Priori Knowledge

Cited by 51 publications

References 27 publications

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

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