Technical Limitations of GNSS Receivers in Indoor Positioning

Puričer, Pavel; Kovář, Pavel

doi:10.1109/radioelek.2007.371487

Cited by 20 publications

(9 citation statements)

References 1 publication

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“…In addition, GPS has been shown to be vulnerable to jamming or manipulation 12 . But even under conditions where an unadulterated GPS signal could be accessed in principle, there can be circumstances that result in a substantially reduced accuracy, e.g., under dense foliage cover 13 , in indoor environments 14 , or in urban canyons with dense foliage cover 15 .…”

Section: Introductionmentioning

confidence: 99%

Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes

Zhang¹,

Farabow²,

Singhal³

et al. 2023

Bioinspir. Biomim.

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Many bat species navigate in complex, heavily vegetated habitats. To achieve this, the animal relies on a sensory basis that is very different from what is typically done in engineered systems that are designed for outdoor navigation. Whereas the engineered systems rely on data-heavy senses such as lidar, bats make do with echoes triggered by short, ultrasonic pulses. Prior work has shown that "clutter echoes" originating from vegetation can convey information on the environment they were recorded in -- despite their unpredictable nature. The current work has investigated the spatial granularity that these clutter echoes can convey by applying deep-learning location identification to an echo data set that resulted from the dense spatial sampling of a forest environment. The GPS location corresponding to the echo collection events was clustered to break the survey area into the number of spatial patches ranging from two to 100. A convolutional neural network (Resnet 152) was used to identify the patch associated with echo sets ranging from one to ten echoes. The results demonstrate a spatial resolution that is comparable to the accuracy of recreation-grade GPS operating under foliage cover. This demonstrates that fine-grained location identification can be accomplished at very low data rates.

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Section: Introductionmentioning

confidence: 99%

Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes

Zhang¹,

Farabow²,

Singhal³

et al. 2023

Bioinspir. Biomim.

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“…Estimation of the position of a target in an outdoor environment is performed using Global Navigation Satellite Systems (GNSS) such as Global Positioning Systems (GPS). However, in indoor environments, GNSS cannot work properly because of the weak satellite signals [13]. For this reason, research efforts in the field of positioning have been directed towards indoor scenarios with an aim of establishing a standard indoor positioning system (IPS) that can be applied worldwide.…”

Section: Introductionmentioning

confidence: 99%

Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

et al. 2019

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Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable sensor position. This paper is focused on studying the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body sensor location for tracking a pedestrian, respectively. With the wearable sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target wearable sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.

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“…Despite the high accuracy and global coverage provided by GNSSs, they cannot properly operate indoors. The strong signal attenuation, the presence of heavy signal multipath, and other sources of interference invalidate GNSS as a positioning solution indoors [ 14 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

109

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Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

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Technical Limitations of GNSS Receivers in Indoor Positioning

Cited by 20 publications

References 1 publication

Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes

Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes

Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

Collaborative Indoor Positioning Systems: A Systematic Review

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