NLOS Multipath Detection by Using Machine Learning in Urban Environments

Suzuki, Taro; Nakano, Yukiko; Amano, Yoshiharu

doi:10.33012/2017.15291

Cited by 22 publications

(27 citation statements)

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“…where t i−max is the code delay of the maximum correlation output,t is the mean of the code delay, and M is the number of correlator output samples. In [3], the authors have also used a signal strength vs. elevation angle feature (referred as F 1 ) that is not taken into account here as we have not introduced the physical context of experiments (receiver's speed, satellite constellation) since the generated dataset is synthetic. Thus, we have made the choice to not use the feature F 1 as opposed to [3].…”

Section: B Benchmark Modelmentioning

confidence: 99%

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Convolutional Neural Network for Multipath Detection in GNSS Receivers

Munin¹,

Blais²,

Couellan³

2020

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-At)

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Global Navigation Satellite System (GNSS) signals are subject to different kinds of events causing significant errors in positioning. This work explores the application of Machine Learning (ML) methods of anomaly detection applied to GNSS receiver signals. More specifically, our study focuses on multipath contamination, using samples of the correlator output signal. The GPS L1 C/A signal data is used and sourced directly from the correlator output. To extract the important features and patterns from such data, we use deep convolutional neural networks (CNN), which have proven to be efficient in image analysis in particular. To take advantage of CNN, the correlator output signal is mapped as a 2D input image and fed to the convolutional layers of a neural network. The network automatically extracts the relevant features from the input samples and proceeds with the multipath detection. We train the CNN using synthetic signals. To optimize the model architecture with respect to the GNSS correlator complexity, the evaluation of the CNN performance is done as a function of the number of correlator output points.

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Section: B Benchmark Modelmentioning

confidence: 99%

“…To assess the performance of our detector we run comparison experiments with another machine learning based method using a predefined feature construction procedure and a Support Vector Machine (SVM) classifier [3].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Multipath Detection in GNSS Receivers

Munin¹,

Blais²,

Couellan³

2020

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-At)

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“…where ele θ is the satellite elevation; the coefficients i c are estimated using the least-squares method, with the result shown in Table 1. Feature 1 is then calculated using [26] ( ) A third-order polynomial is used to fit the measurements with the expression as…”

Section: Correlator-level Featuresmentioning

confidence: 99%

“…referred to as early late phase (ELP) for MP detection. In [26], the author proposed to identify the NLOS reception by constructing a classifier using features extracted from multiple autocorrelation functions (MACF) in a software-defined receiver (SDR) [27]. The basic principle behind this is that the NLOS reception distorts the autocorrelation function (ACF) of the direct LOS signal.…”

Section: Introductionmentioning

confidence: 99%

“…This method is straightforward. For example, based on the satellite ephemeris, the sky-pointing camera method projects the satellites onto the captured image directly, with the satellite in the building area labelled as NLOS, and the one in the open area labelled as LOS [26]. Similarly, the 3D building model method compares the elevation angle of the satellite and the building boundary at the same azimuth angle.…”

Section: Introductionmentioning

confidence: 99%

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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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Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Chen

Gao

2019

NAVIGATION

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Because of the recent interest in unmanned air vehicle (UAV) commercialization, there is a great need for navigation algorithms that provide accurate and robust positioning in urban environments that are often Global Positioning Systems (GPS) challenged or denied. In this paper, we present a probabilistic graph-based navigation algorithm resilient to GPS errors. Fusing GPS pseudorange and Light Detection and Ranging (LiDAR) odometry measurements with 3D building maps, we apply a batch estimation approach to generate a robust trajectory estimate and maps of the surrounding environment. We then leverage the maps to locate potential sources of GPS multipath and mitigate the effects of degraded pseudorange measurements on the trajectory estimate. We experimentally validate our results with flight tests conducted in GPS-challenged and GPS-denied environments.

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NLOS Multipath Detection by Using Machine Learning in Urban Environments

Cited by 22 publications

References 0 publications

Convolutional Neural Network for Multipath Detection in GNSS Receivers

Convolutional Neural Network for Multipath Detection in GNSS Receivers

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

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

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