Urban environment recognition based on theGNSSsignal characteristics

Abstract

Statistical characteristics of signal reception conditions vary greatly in different types of environments. Hence, Global National Satellite System (GNSS) receivers must recognize surroundings for choosing the most suitable positioning methods in real time. Targeting vehicular positioning applications in a city, a novel environment recognition algorithm based only on the GNSS signal characteristics is proposed to distinguish between six distinct settings. To characterize different environmental interferences, a signal feature vector is built to represent the signal attenuation, blockage, and multipath. By training the classification model with labeled feature vectors, the support vector machine (SVM) algorithm is used to predict the scene type. A temporal filtering method is proposed to improve the accuracy. With advanced training of the model, this recognition method can work for the receiver in real time. To prove the extensive applicability of the proposed algorithm, the prediction data set and the training data set are collected in different cities. The testing results show overall recognition accuracy of 89.3% across different environments.

Adaptive GNSS Receiver Design for Highly Dynamic Multipath Environments

“…The data collection sites is a key to understand the actual problems faced by single and multi-constellation GNSS navi- gation. Most of the field experiments for environment characterization are performed in urban or semi-urban or open-sky environments [47]- [49] where it is difficult to use some of the parameters used above for environment characterization without knowing the exact geometry of the satellites and probability of blockage [50]. In order to precisely gauge the depth of problems faced by a GNSS receiver, carefully planned field experiments are conducted on candidate sites having high degree of naturalism.…”

“…Several researchers have used signal characteristics (i.e., No. of visible satellites, DOP and signal strength/CNR) to propose algorithms for environment detection using the machine learning techniques, fuzzy inference systems, stochastic modelling and Hidden Markov model [47]- [49], [60],…”

“…[61]. In [47], Support Vector Machine (SVM) is used to achieve recognition accuracy of 89.3% across different environments using strength attenuation and strength fluctuation, blockage coefficient and GDOP as detection parameters.…”

Characterization and mitigation of multipath fading on multi-frequency GNSS signals in urban environment

“…The low-frequency trend SI trend is estimated by a sixth-order BW high-pass filter with 0.1 Hz cutoff frequency, the same value as in Van Dierendonck et al [14]. Then the raw intensity SI raw is normalized by the trend SI trend to form the normalized signal intensity SI norm in (4).…”

“…Strode et al [3] developed a GNSS multipath detector using three-frequency signal-to-noise measurements. Wang et al [4] built a signal feature vector including mean and standard deviation, blockage coefficient, geometric dilution of precision (GDOP) expansion ratio, and strength fluctuation to characterize dif-ferent environmental interferences. They utilized the support vector machine (SVM) algorithm to recognize the urban environment type.…”