It is known that a global navigation satellite system (GNSS) receiver is vulnerable to interference signals, and the interference type recognition, i.e., classification, is helpful for the receiver to sense the spectrum, select an efficient mitigation solution, or identify an interferer. However, conventional interference recognition methods are subject to feature-based expert classification methodologies that are very sensitive to the choice of features and often lack flexibility to different tasks. Recently, some researchers rely on convolutional networks and transform the interference recognition to image classification. Although straightforward, this type of method suffers problems when classifying more complex interference patterns. In this article, a new GNSS interference classifier is proposed, which is composed of a devised interference fingerprint spectrum (FPS) and an especially designed deep convolutional neural network, named as FPS deep convolutional neural network (DNN). The design of the FPS makes different interference signals more discernible. The proposed deep convolutional neural network greatly improves recognition accuracies, especially at low interference powers. The tests show that the average accuracy of the proposed FPS-DNN can reach more than 95% for nine types of interferences at −110 dBm power. This result is significantly superior to the feature-based expert classifier and the conventional convolutional network classifier. It is also demonstrated that the proposed FPS-DNN has a better generalization performance even if the interference is out of the parameter space of the training dataset.

A deep understanding of multipath characteristics is essential to design signal simulators and receivers in global navigation satellite system applications. As a new constellation is deployed and more applications occur in the urban environment, the statistical multipath models of navigation signal need further study. In this paper, we present statistical distribution models of multipath time delay, multipath power attenuation, and multipath fading frequency based on the experimental data in the urban canyon environment. The raw data of multipath characteristics are obtained by processing real navigation signal to study the statistical distribution. By fitting the statistical data, it shows that the probability distribution of time delay follows a gamma distribution which is related to the waiting time of Poisson distributed events. The fading frequency follows an exponential distribution, and the mean of multipath power attenuation decreases linearly with an increasing time delay. In addition, the detailed statistical characteristics for different elevations and orbits satellites is studied, and the parameters of each distribution are quite different. The research results give useful guidance for navigation simulator and receiver designers.

In the indoor environment, where global positioning system (GPS) fails, the presence of multipath, non-line-of-sight (NLOS), and dense scatterer yield unreliable position estimation. In the massive multiple-input multiple-output (massive MIMO) system, the sparse channel property is exploited here for user localization as an integral part of the network to reduce the power of user equipment (UE). In this study, a novel spatial spectrum fusion estimation and localization (SSFEAL) algorithm using the uniform circular array (UCA) structure is proposed, which is based on the two-dimensional (2D) direction of arrival (DOA) estimation. The spatial spectrum of the incoherent narrowband uplink pilot signal on each frequency bin received at the distributed massive MIMO base stations is used for spectrum fusion. The main contributions of this research work are as follows: (1) The channel’s sparse characteristics at individual base stations are used to synthesize the covariance matrix of the received signal at the central fusion center. (2) The proposed method has 2D spatial information measurement fusion to generate informative spatial spectrum followed by grid refinement processing to reduce the localization error to sub-meter level. It is demonstrated that the proposed method using UCA gives higher-resolution estimation performance compared with the rectangular array in the localization accuracy. Comprehensive Monte Carlo simulation and real application test results show the validity and sub-meter precision in the indoor environment.

Global navigation satellite system multipath channel modeling is important for signal simulation and error mitigation in urban scenarios. However, there have not been sufficient studies on channel statistical models for dynamic land vehicles in eastern Asian cities. This paper presents both MULTIPATH and non-line-of-sight (NLOS) channel models for GPS L1CA and BDS B1I signals. They are based on an extensive field dataset collected in the Shanghai-Lujiazui area. In addition, a method is developed to estimate multipath components and NLOS signal parameters in a dynamic channel environment. It is found that the Gamma function best fits the distribution of MULTIPATH channel delays while the exponential function is better suited for the distribution of NLOS channel delays. Both MULTIPATH and NLOS channel signals follow a linear decline average power-delay trend and a zero-mean Gaussian distribution power scattering model. The Doppler fading frequency distribution for MULTIPATH and NLOS channels can be modeled by the Gaussian function except for the MULTIPATH channel signal at the low velocity range (0~3 km/h), where an absolute exponential distribution is a better fit. The lifetime distribution for MULTIPATH and NLOS channels is represented by the generalized Pareto distribution function. These findings offer insights into the GNSS signal models in urban propagation channels and are essential to the development of accurate urban navigation systems.

Global Navigation Satellite System (GNSS) multipath channel models are fundamental and critical for signal simulation and receiver performance evaluation. They also aid the designing of suitable multipath error mitigation algorithms when the properties of multipath channel are available. However, there is insufficient existing research on BeiDou Navigation Satellite System (BDS) signal multipath channel models. In this study, multipath channel statistical models are established on the basis of extensive datasets of the BDS B1I signal. A multipath parameter estimation algorithm is designed to extract information of multipath rays from the intermediate frequency data. The delay, power loss, Doppler fading frequency, and lifetime distribution models for static and dynamic vehicle platforms are established and compared, and the effects of the satellite orbit type and platform speed on the models are analyzed. The results reveal the detailed distribution and variation characteristics of the multipath parameters and are valuable for the development of accurate urban navigation systems.

Precisely predicting the location of the user in a Global-Navigation-Satellite-System-degraded environment is a highly challenging task. Localization based on cellular signal fingerprints is one of the promising solutions to this problem and has attracted increasing attention. Long Term Evolution (LTE) signal is popularly utilized for localization due to its global usage, extensive urban coverage, and favorable signal properties. This paper proposes a new multiband multicell Reference Signal Received Power (MBMC-R) fingerprint, which properly fuses LTE signals’ carrier band information, the physical cell identifier information, and RSRP values. Next, a sequential block-matching weight K nearest neighbor algorithm with a cosine similarity criterion is specially designed for performing the pattern-matching localization with the MBMC-R fingerprint. The proposed method also includes the derivation of the Cramer-Rao lower bound, which reveals the impact of various factors on the lower bound of position error. Simulation and on-field experiments prove the performance superiority over other fingerprint localization algorithms reported in the literature.