Abstract
There has been a continuous increase in the demands for Global Navigation Satellite System (GNSS) receivers in a wide range of applications. More and more wireless and mobile devices are equipped with built-in GNSS receivers; their users' mobility behavior can result in challenging signal conditions that have detrimental effects on the receivers' tracking and positioning accuracy. A major error source is the multipath signals, which are signals that are reflected off different surfaces and propagated to the receiver's antenna via different paths. Analysis of the received multipath signals indicated that their characteristics depend on the surrounding environment. This paper introduces a machine-learning pattern recognition algorithm that utilizes the aforementioned dependency to classify the multipath signals' characteristics and identify the surrounding environment. The identified environment is utilized in a novel adaptive tracking technique that enables a GNSS receiver to change its tracking strategy to best suit the current signal condition. This will lead to a robust positioning under challenging signal conditions. The algorithm is verified using real and simulated Global Positioning System (GPS) signals with accurate multipath models. Keywords-component; GPS; GNSS; machine learning; pattern recognition; PCA; PNN; multipath.
Highlights
A Global Navigation Satellite System (GNSS) [1, 2] is a radio navigation system that employs spread spectrum techniques to transmit ranging signals and navigation data
The simulated Global Positioning System (GPS) signals are used with the multipath patterns to get a variety of signals conditions to be used in the verification process
This paper introduced a novel machine-learning patternrecognition algorithm to identify the surrounding environment from the characteristics of the multipath reflected signals
Summary
A Global Navigation Satellite System (GNSS) [1, 2] is a radio navigation system that employs spread spectrum techniques to transmit ranging signals and navigation data. The software provided in [33] is used to generate received signal patterns that typically appear in urban and suburban environments Each matrix has a size of NEtr.Nc. Define each column vector of Wtrc as Wtric, where i = 1, ..., Nc. Define each column vector of Wtrc as Wtric, where i = 1, ..., Nc Another PDF, for each class, can be estimated as (4) the PCA weights matrix relating the unclassified pattern to each class, Wp, is divided into Ncl matrices, defined as Wpc, where c is the class index, and p is the new pattern to be classified. Each column vector is defined as Wpic, where, i = 1, ..., Nc. The summation layer of the PNN calculates the probability that the unclassified pattern belongs to each class. An urban environment has higher number of reflected signals than a suburban environment, and indoor signals are weaker and can incur longer blockage times
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