Adopting Machine Learning to GNSS Positioning on MediaTek P60 Platform

Jau, Pei-Hung; Chen, Hung−Wen; Chen, Po‐Yu; Chen, Hao; Tsai, Mu-Han; Weng, Chia-Tse; Chou, Demarco

doi:10.33012/2018.16000

Cited by 3 publications

(2 citation statements)

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“…The ideal operating environment for GNSS is line-ofsight (LOS), but for a variety of obvious reasons, the signal undergoes reflection or refraction [37]. LOS signals are the direct signals from the satellite to the receiver while the signal reflection received by the receiver is referred to as Non-Line-of-Sight (NLOS) reception [80]. Integration of Multiple Sensors: IoT devices often integrate multiple sensors, such as accelerometers, magnetometers, and gyroscopes along with GNSS receivers.…”

Section: Localization In Non-line-of-sight (Nlos) Scenariosmentioning

confidence: 99%

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

Agarwal,

Ainapure,

Shukla

2023

IJRITCC

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This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT.

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Section: Localization In Non-line-of-sight (Nlos) Scenariosmentioning

confidence: 99%

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

Agarwal,

Ainapure,

Shukla

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…In fact, the identification of MP will be helpful for improving GNSS positioning in urban canyons.NMEA/RINEX-level classification rate is limited because the complex signal propagation in urban environment is not fully manifested in these end attributes. With the availability of GNSS raw measurements in mass-market devices, e.g., tablets and smartphones with Android 7 operating system [23], deeper-level GNSS measurements are accessible, such as carrier phase, code pseudorange, navigation message bits, correlation result of each channel [24]. This opens the door to develop a more advanced GNSS positioning algorithm, including signal type classification at a deeper level.…”

mentioning

confidence: 99%

Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements

Jia

Luo

et al. 2019

Remote Sensing

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This paper proposes to use a correlator-level global positioning system (GPS) line-of-sight/multipath/non-line-of-sight (LOS/MP/NLOS) signal reception classifier to improve positioning performance in an urban environment. Conventional LOS/MP/NLOS classifiers, referred to as national marine electronics association (NMEA)-level and receiver independent exchange format (RINEX)-level classifiers, are usually performed using attributes extracted from basic observables or measurements such as received signal strength, satellite elevation angle, code pseudorange, etc. The NMEA/RINEX-level classification rate is limited because the complex signal propagation in urban environment is not fully manifested in these end attributes. In this paper, LOS/MP/NLOS features were extracted at the baseband signal processing stage. Multicorrelator is implemented in a GPS software-defined receiver (SDR) and exploited to generate features from the autocorrelation function (ACF). A robust LOS/MP/NLOS classifier using a supervised machine learning algorithm, support vector machine (SVM), is then trained. It is also proposed that the Skymask and code pseudorange double difference observable are used to label the real signal type. Raw GPS intermediate frequency data were collected in urban areas in Hong Kong and were postprocessed using a self-developed SDR, which can easily output correlator-level LOS/MP/NLOS features. The SDR measurements were saved in the file with the format of NMEA and RINEX. A fair comparison among NMEA-, RINEX-, and correlator-level classifiers was then carried out on a common ground. Results show that the correlator-level classifier improves the metric of F1 score by about 25% over the conventional NMEA-and RINEX-level classifiers for testing data collected at different places to that of training data. In addition to this finding, correlator-level classifier is found to be more feasible in practical applications due to its less dependency on surrounding scenarios compared with the NMEA/RINEX-level classifiers.The former, like the conventional GNSS positioning algorithm, still makes use of pseudorange measurements, meanwhile it is aided with the information of satellite visibility or additional path delay of reflected signals obtained using 3D city models. For shadow matching, the basic idea is to compare the measured signal availability and strength with predictions obtained using 3D city models over a range of candidate positions. For detailed implementation of 3DMA GNSS positioning, readers are referred to [5][6][7][8][9][10]. For the 3DMA GNSS positioning, the accuracy of line-of-sight/non-line-of-sight (LOS/NLOS) signal reception classification directly affects its performance [5]. In addition to the 3DMA GNSS positioning, conventional ranging-based least-squares GNSS positioning can also benefit from an accurate signal classification by excluding or down-weighting the identified multipath (MP)/NLOS measurements [11].Various approaches to classifying LOS/NLOS signal have been proposed. As mentioned above, the 3D ...

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Data Denoising Based on Hadamard Matrix Transformation and Rayleigh Quotient Maximization: Application to GNSS Signal Classification

Yue

Hsu

2022

IEEE Trans. Instrum. Meas.

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Adopting Machine Learning to GNSS Positioning on MediaTek P60 Platform

Cited by 3 publications

References 0 publications

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

Intelligent GPS L1 LOS/Multipath/NLOS Classifiers Based on Correlator-, RINEX- and NMEA-Level Measurements

Data Denoising Based on Hadamard Matrix Transformation and Rayleigh Quotient Maximization: Application to GNSS Signal Classification

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