Navigation Satellite System Signals Research Articles

A Unified Dual-frequency Constant Envelope Multiplexing Design Framework for Modernised GNSS Signals

In the field of modernised Global Navigation Satellite System (GNSS) signal design, several Dual-frequency Constant Envelope Multiplexing (DCEM) methods have been recently proposed. However, the existing DCEM methods, such as Alternative Binary Offset Carrier (AltBOC), generalised AltBOC and Asymmetric Constant Envelope Binary Offset Carrier (ACE-BOC), are only applied in some special cases. In this paper, we present a unified DCEM design framework for GNSS signals. The existing DCEM methods can be unified in this framework. First, the signal components at two carrier frequencies are combined into two single-frequency constant envelope signals. Then, the linear sum of dual-frequency signals with non-constant envelopes is obtained. Finally, the linear sum is converted into the corresponding DCEM signal by solving an optimisation problem. The proposed design framework has no strict constraints on the number, power ratio and phase relationship of the signal components. Moreover, some special design cases under this framework are also analysed in detail. The analytical results show that the proposed design method can reach higher multiplexing efficiency compared with the existing methods. Based on the proposed method, we suggest a scheme to multiplex the BeiDou regional signals and global signals at the B2 frequency. The simulation results of correlation functions and Power Spectrum Density (PSD) verify the correctness and effectiveness of the proposed design method.

Research on the soil moisture sliding estimation method using the LS-SVM based on multi-satellite fusion

ABSTRACTGlobal Navigation Satellite System Interferometric Reflectometry (GNSS-IR) is a new remote-sensing technique; it can be used to estimate near-surface soil moisture from signal-to-noise ratio (SNR) data. Considering the advantages of multi-satellite convergence and the time and space scale of soil moisture, based on multi-satellite fusion, non-linear sliding estimation method of soil moisture is proposed. Firstly, the direct and reflection signals of Global Navigation Satellite System (GNSS) satellites are separated by means of a low-order polynomial fitting, and then, by establishing the sinusoidal fitting model of reflection signals, the relative delay phase of the SNR interferogram is obtained. Finally, a linear regression model is used to analyse and select the phase of the SNR interferogram, and a sliding estimation method of soil moisture using the least squares Support Vector Machine based on multi-satellites fusion is established. Based on the monitoring data provided by the Plate Boundary Observations network, the feasibility and effectiveness of using single and multiple GNSS satellites to slide estimate soil moisture are compared and analysed. Theoretical analysis and experimentation show that GNSS-IR can provide full play to the advantages of artificial intelligence and effectively integrate the performance of each satellite. The fitting process is stable, and the model needs less modelling data; the sliding method can achieve a long time estimation, and the estimation error is relatively stable. Based on the satellites which the correlation coefficient (r) between the phase of the SNR interferogram and the soil moisture is greater than 0.50, the fusion satellites model is established; the results are better than that of using single satellites; the r between estimated results and reference values of soil moisture is greater than or equal to 0.94, and the root mean square error and mean absolute error are less than 0.060 and 0.050, respectively. Therefore, the estimation of soil moisture can be treated as a non-linear event, and multi- satellite fusion estimation is feasible and effective.

Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD).

A significant challenge in global navigation satellite system (GNSS) signal processing is a requirement for a very high sampling rate. The recently-emerging compressed sensing (CS) theory makes processing GNSS signals at a low sampling rate possible if the signal has a sparse representation in a certain space. Based on CS and SVD theories, an algorithm for sampling GNSS signals at a rate much lower than the Nyquist rate and reconstructing the compressed signal is proposed in this research, which is validated after the output from that process still performs signal detection using the standard fast Fourier transform (FFT) parallel frequency space search acquisition. The sparse representation of the GNSS signal is the most important precondition for CS, by constructing a rectangular Toeplitz matrix (TZ) of the transmitted signal, calculating the left singular vectors using SVD from the TZ, to achieve sparse signal representation. Next, obtaining the M-dimensional observation vectors based on the left singular vectors of the SVD, which are equivalent to the sampler operator in standard compressive sensing theory, the signal can be sampled below the Nyquist rate, and can still be reconstructed via minimization with accuracy using convex optimization. As an added value, there is a GNSS signal acquisition enhancement effect by retaining the useful signal and filtering out noise by projecting the signal into the most significant proper orthogonal modes (PODs) which are the optimal distributions of signal power. The algorithm is validated with real recorded signals, and the results show that the proposed method is effective for sampling, reconstructing intermediate frequency (IF) GNSS signals in the time discrete domain.

Comparative study of using different ionosphere models in Thailand for single-frequency GNSS users

An ionosphere layer makes inaccurate global navigation satellite systems (GNSS) signal. The ionosphere layer contains free electrons delaying the speed of GNSS signals. A suitable selection of ionosphere model is very useful to eliminate this inaccurate GNSS signal. This article presents a comparative study by applying various ionosphere models in Thailand including: Klobuchar model, Global Ionosphere Maps, Ionosphere Model from Quasi-Zenith Satellite System and local ionosphere model. The local ionosphere model is generated from GNSS observations at with the use of locally available GNSS tracking stations by the Bernese GPS software 5.0. Coordinates derived from the above-mentioned models were compared and statistical tests in two observed scenarios; namely, single-frequency data and ionosphere-free linear combination from dual-frequency data. These experiment results proved that applying the local ionosphere model to the estimation produces the most accurate positioning results. They are similar to the results obtained from an ionosphere-free linear combination, but with significant differences.

An Unambiguous Tracking Technique for Sine-BOC(kn,n) Modulated GNSS Signals

Binary offset carrier modulation is applied in several signals of the global navigation satellite system (GNSS). It enhanced the robustness of GNSS against the impact of multipath and make the compatibility of GNSS better. However, it has the main drawback of potentially giving biased measurements in acquisition and tracking. The technique we proposed is based on linear fitting with multi-correlator to accomplish unambiguous tracking. The coefficient of each correlator was deduced in this paper. The theoretical analysis and simulation results indicate that the proposed method can performs well on multipath and thermal noise.

Implementation and Analysis of Tightly Integrated INS/Stereo VO for Land Vehicle Navigation

Tight integration of inertial sensors and stereo visual odometry to bridge Global Navigation Satellite System (GNSS) signal outages in challenging environments has drawn increasing attention. However, the details of how feature pixel coordinates from visual odometry can be directly used to limit the quick drift of inertial sensors in a tight integration implementation have rarely been provided in previous works. For instance, a key challenge in tight integration of inertial and stereo visual datasets is how to correct inertial sensor errors using the pixel measurements from visual odometry, however this has not been clearly demonstrated in existing literature. As a result, this would also affect the proper implementation of the integration algorithms and their performance assessment. This work develops and implements the tight integration of an Inertial Measurement Unit (IMU) and stereo cameras in a local-level frame. The results of the integrated solutions are also provided and analysed. Land vehicle testing results show that not only the position accuracy is improved, but also better azimuth and velocity estimation can be achieved, when compared to stand-alone INS or stereo visual odometry solutions.

Feasibility Study of Rain Rate Monitoring from Polarimetric GNSS Propagation Parameters

In this work, the feasibility of estimating rain rate based on polarimetric Global Navigation Satellite Systems (GNSS) signals is explored in theory. After analyzing the cause of polarimetric signals, three physical-mathematical relation models between co-polar phase shift (KHH, KVV), specific differential phase shift (KDP), and rain rate (R) are respectively investigated. These relation models are simulated based on four different empirical equations of nonspherical raindrops and simulated Gamma raindrop size distribution. They are also respectively analyzed based on realistic Gamma raindrop size distribution and maximum diameter of raindrops under three different rain types: stratiform rain, cumuliform rain, and mixed clouds rain. The sensitivity of phase shift with respect to some main influencing factors, such as shape of raindrops, frequency, as well as elevation angle, is also discussed, respectively. The numerical results in this study show that the results by scattering algorithms T-matrix are consistent with those from Rayleigh Scattering Approximation. It reveals that they all have the possibility to estimate rain rate using the KHH-R, KVV-R or KDP-R relation. It can also be found that the three models are all affected by shape of raindrops and frequency, while the elevation angle has no effect on KHH-R. Finally, higher frequency L1 or B1 and lower elevation angle are recommended and microscopic characteristics of raindrops, such as shape and size distribution, are deemed to be important and required for further consideration in future experiments. Since phase shift is not affected by attenuation and not biased by ground clutter cancellers, this method has considerable potential in precipitation monitoring, which provides new opportunities for atmospheric research.

Distance measuring equipment interference suppression based on parametric estimation and wavelet-packet transformation for global navigation satellite systems

It is well known that some global navigation satellite system (GNSS) signals operate on the aeronautical radionavigation services (ARNS) frequency band, which is also utilized by other aeronautical service signals, such as pulsed signals from the distance-measuring equipment (DME) system. Thus, the high-power DME signal, as interference, will significantly degrade the performance of the GNSS. Some algorithms, including time blanking, frequency notch filtering, and hybrid filtering, have been proposed to suppress DME interference. However, when the density of the DME pulse is high, they will bring a huge loss of the desired satellite signal. This paper proposes a hybrid approach for DME interference suppression by combining a novel parametric algorithm and the wavelet-packet-based algorithm. An overlap detection algorithm is also proposed to automatically switch between the two algorithms. When there is no overlapped pulse, the parametric method will be utilized to suppress the interference and retain more of the desired satellite signal. For overlapped DME pulses, the interferences are suppressed by the wavelet-packet transformation algorithm. The problem of parameter selection for wavelet-packet transformation is also discussed systematically. The trade-off between performance and complexity is obtained by adaptively selecting between the traditional algorithm and the proposed hybrid approach based on a pulse density detection algorithm. Numerical results are presented to demonstrate the effectiveness of the proposed approach.

INDOOR UWB POSITIONING IN EIGER LOCALIZATION SYSTEM

The EIGER project proposes a global approach for the exploitation of the efficient joint use of Global Navigation Satellite System (GNSS) signals and Ultra-Wide Band (UWB) positioning in order to allow permanent and reliable outdoor/indoor localization. This paper is focused on the UWB positioning subsystem designed within the project. The architecture of the subsystem, design of its components and the transmission scheme leading to the time difference of arrival determination are described. Results of system laboratory tests are presented and discussed.

Cosine faded harmonics binary offset carrier modulation for next‐generation GNSS

A new family of faded harmonics modulations, cosine faded harmonics binary offset carrier modulations (FH-BOCcos), is designed to account for the spectral overlap among global navigation satellite system (GNSS) signals. The generalised mathematical model and power spectral density function of FH-BOCcos modulation are established. The spectral characteristics and modulation parameters are also analysed. The proposed modulations can suppress specific sidelobes of the spectrum while retaining similar signal performance compared with traditional BOC modulation.

Detection of GNSS Signals Propagation in Urban Canyos Using 3D City Models

This paper presents one of the solutions to the problem of multipath propagation and effects on Global Navigation Satellite Systems (GNSS) signals in urban canyons. GNSS signals may reach a receiver not only through Line-of-Sight (LOS) paths, but they are often blocked, reflected or diffracted from tall buildings, leading to unmodelled GNSS errors in position estimation. Therefore in order to detect and mitigate the impact of multipath, a new ray-tracing model for simulation of GNSS signals reception in urban canyons is proposed - based on digital 3D maps information, known positions of GNSS satellites and an assumed position of a receiver. The model is established and validated using experimental, as well as real data. It is specially designed for complex environments and situations where positioning with highest accuracy is required - a typical example is navigation for blind people.

History of extreme ultraviolet solar measurements in XX–XXI centuries: from balloons to the International Space Station as instrumental platforms

The Sun was one of the central points for observations as long as human cultures are known. After researchers observed the extension of the solar rainbow spectrum towards shorter wavelengths with increasing altitude they started measurements of the solar ultraviolet spectrum from balloons in 1934. The acquisition of spectra in the extreme ultraviolet and soft X-ray spectral regions was beginning after 1946 using photographic recordings aboard rockets. However, it took six more years to achieve good spectral resolution below about 220 nm because servomechanisms had to be developed to fairly accurately point the spectrographs to the Sun during the rocket flight. Another big step forward was achieved by the application of photoelectric recording technology and by using satellites as a platform to observe qualitatively the strong variability of the solar ultraviolet irradiance on short-, medium- and long-term scales. Until the end of the past century these measurements have been strongly impeded by the inherent degradation of the EUV instruments with time. Since there is progress solving this serious problem, measurements with sufficient radiometric accuracy allow composing the data available to the first set of ultraviolet data covering a period of eleven years. Based on the sophisticated instrumentation verified in space, future EUV measurements of the solar spectral irradiance on the International Space Station are promising accuracy levels of about 5%. Added by low-cost equipment on-line measurements will allow providing data needed in ionospheric modeling for correcting propagation delays of navigation signals from space to Earth. Adding ultraviolet airglow and auroral emission measurements the impact of space weather on the ionosphere can be studied and also be used deriving more detailed correction procedures for the evaluation of Global Navigation Satellite System (GNSS) signals.

Constant‐envelope dual QPSK‐like modulation and its generalised form for modern GNSS signals

A new constant-envelope modulation multiplexing two quadrature phase shift keying (QPSK)-like global navigation satellite system (GNSS) signals and its generalised form with unbalanced power are presented. The modulations can be regarded as the extended form of generalised DualQPSK modulations and provide a solution to the Compass B1 band multiplexing problem.

Calibration of Galileo signals for time metrology.

Using global navigation satellite system (GNSS) signals for accurate timing and time transfer requires the knowledge of all electric delays of the signals inside the receiving system. GNSS stations dedicated to timing or time transfer are classically calibrated only for Global Positioning System (GPS) signals. This paper proposes a procedure to determine the hardware delays of a GNSS receiving station for Galileo signals, once the delays of the GPS signals are known. This approach makes use of the broadcast satellite inter-signal biases, and is based on the ionospheric delay measured from dual-frequency combinations of GPS and Galileo signals. The uncertainty on the so-determined hardware delays is estimated to 3.7 ns for each isolated code in the L5 frequency band, and 4.2 ns for the ionosphere-free combination of E1 with a code of the L5 frequency band. For the calibration of a time transfer link between two stations, another approach can be used, based on the difference between the common-view time transfer results obtained with calibrated GPS data and with uncalibrated Galileo data. It is shown that the results obtained with this approach or with the ionospheric method are equivalent.

A novel acquisition strategy for weak GNSS signals based on map criterion

Detecting and processing Global Navigation Satellite System (GNSS) signals indoors and in urban canyons, have gained a great deal of attention due to the problems of very weak signals and hostile environments. The detection of GNSS signals is generally based on the application of a statistical test, derived from the maximum likelihood theory. The work presented here considers a new approach to the detection of weak GNSS signals using a Bayesian technique within a scenario where the search space size is already reduced to a few tens of cells using some kind of assisted information. The search space cells are considered as candidate cells, where each candidate cell is associated with a code delay and a Doppler frequency. For each candidate cell, a posteriori probability is propagated for a fixed number of operation cycles. At the end of the process, the maximum a posterior (MAP) criterion is used to select the correct cell. Simulation results are presented indicating that the proposed method provides a significant performance advantage over other reference schemes. They include a noncoherent integration scheme and another scheme which also utilizes the posterior probabilities as decision statistics but differs in the mechanism of probability propagation.

Global Positioning System (GPS) Positioning Errors During Ionospheric Scintillation Event

As technology advancement progresses throughout the years in this modern age, every technology has its part to play in that the world is moving towards a brighter future. GPS (Global Positioning System) has diverse application in current globalized world, its application has pervasive benefits not only to navigation and positioning, it is pivotal in industries like logistics, shipping, financial services and agriculture. Since the decision to shut down the Selectivity Availability (SA) by former U.S. President, Bill Clinton, ionospheric effect is now the primary concern of error contributing factors in GPS. Ionospheric scintillation induces rapid fluctuations in the phase and the amplitude of received Global Navigation Satellite System (GNSS) signals. These rapid fluctuations or scintillation potentially introduce cycle slips, degrade range measurements, and if severe enough lead to loss of lock in phase and code. Global Ionospheric Scintillation Model (GISM) was used to compute amplitude scintillation parameter for each GPS satellite visible from Melaka, Malaysia (Latitude 20 14’ N, Longitude 1020 16’ E) as its location has strong equatorial scintillation behavior. The output data from GISM was then used to calculate the positioning error where it is depends on the Dilution of Precision (DOP) and User Equivalent Range Error (UERE). There are two schemes that were used. First, the positioning error was calculated for all the visible satellites with better DOP but worse UERE due to scintillation event. Secondly, the positioning error was calculated for those satellites that have amplitude scintillation index, S4 < 0.7 which leads to worse DOP with better UERE. Comparison of results from the both schemes was then made.

Augmented GNSS differential corrections minimum mean square error estimation sensitivity to spatial correlation modeling errors.

Railway signaling is a safety system that has evolved over the last couple of centuries towards autonomous functionality. Recently, great effort is being devoted in this field, towards the use and exploitation of Global Navigation Satellite System (GNSS) signals and GNSS augmentation systems in view of lower railway track equipments and maintenance costs, that is a priority to sustain the investments for modernizing the local and regional lines most of which lack automatic train protection systems and are still manually operated. The objective of this paper is to assess the sensitivity of the Linear Minimum Mean Square Error (LMMSE) algorithm to modeling errors in the spatial correlation function that characterizes true pseudorange Differential Corrections (DCs). This study is inspired by the railway application; however, it applies to all transportation systems, including the road sector, that need to be complemented by an augmentation system in order to deliver accurate and reliable positioning with integrity specifications. A vector of noisy pseudorange DC measurements are simulated, assuming a Gauss-Markov model with a decay rate parameter inversely proportional to the correlation distance that exists between two points of a certain environment. The LMMSE algorithm is applied on this vector to estimate the true DC, and the estimation error is compared to the noise added during simulation. The results show that for large enough correlation distance to Reference Stations (RSs) distance separation ratio values, the LMMSE brings considerable advantage in terms of estimation error accuracy and precision. Conversely, the LMMSE algorithm may deteriorate the quality of the DC measurements whenever the ratio falls below a certain threshold.

Direct Signal Enhanced Semicodeless Processing of GNSS Surface-Reflected Signals

This paper presents an aircraft demonstration of direct-signal enhanced semicodeless processing of global navigation satellite systems (GNSS) signals reflected from the Earth’s surface. Comparisons are made between this new method and an interferometric approach to GNSS reflectometry. Results show that this technique produces waveforms with greater signal-to-noise compared with the interferometric approach for all GNSS signals currently in use or planned for the near future. Alternatively, the semicodeless technique can have similar performance with smaller antennas for lower hardware costs. The semicodeless approach also has the advantage that different signals along with their different surface spatial resolutions are processed separately, each signal’s coherent integration time can be optimized, and ground/aircraft experiments and tests are free of spurious signals. The signal processing demands of the semicodeless approach are shown to be proportional to the number of signal components processed when integrated with a GNSS precise orbit determination (POD) receiver.

Water Vapor Probabilistic Retrieval Using GNSS Signals

In this paper, we propose a novel Bayesian procedure to update the probability distribution for a set of possible atmospheric states, once ground measures of temperature, pressure, humidity, and tropospheric delay of Global Navigation Satellite System (GNSS) signals are made. It is based on a representative dataset of matching pairs of reanalysis atmospheric states and ground measures. By applying the basic rules of probability theory and logic inference, a computable expression for the conditional probability of the states given the measures is found. This allows us to select the most plausible atmospheric conditions, consistent with ground observations. Compared with more conventional techniques, the proposed approach has the advantage of always giving a result, even if not all the measures are available. Moreover, it provides the probability distributions of the retrieved quantities, which collapse to the corresponding prior distributions in the worst case of no significant measures. In any case, the final uncertainties are fully quantified, as needed for many meteorological applications, including data assimilation and ensemble forecasts for a numerical weather model. In addition to the theoretical details, a practical example of operational application, using a ten-year dataset on a Mediterranean test site, is also presented. The most probable retrieved atmospheric profiles of water vapor and temperature, as well as the corresponding values of precipitable water, are compared with balloon measurements on such a test site, showing good agreement and a significant improvement when the GNSS delay measure is added. In particular, the precipitable water retrieval turns out at least as accurate as that obtained with conventional approaches.

Moving Target Positioning Based on a Distributed Camera Network

We propose a systematic framework for moving target positioning based on a distributed camera network. In the proposed framework, low-cost static cameras are deployed to cover a large region, moving targets are detected and then tracked using corresponding algorithms, target positions are estimated by making use of the geometrical relationships among those cameras after calibrating those cameras, and finally, for each target, its position estimates obtained from different cameras are unified into the world coordinate system. This system can function as complementary positioning information sources to realize moving target positioning in indoor or outdoor environments when global navigation satellite system (GNSS) signals are unavailable. The experiments are carried out using practical indoor and outdoor environment data, and the experimental results show that the systematic framework and inclusive algorithms are both effective and efficient.