Global Navigation Satellite System Acquisition Research Articles

Thresholding optimization of global navigation satellite system acquisition with constant false alarm rate detection using metaheuristic techniques

Thresholding optimization of global navigation satellite system acquisition with constant false alarm rate detection using metaheuristic techniques

Field-road classification for GNSS recordings of agricultural machinery using pixel-level visual features

The understanding of agricultural machinery, whether it is used “in field” or “on road”, plays an essential role in optimizing the efficiency of cross-regional agricultural mechanization services that have been available in China for decades. With the widespread availability of Global Navigation Satellite System(GNSS)-enabled devices, developing a method for automatically identifying the activity associated with each point in GNSS recorded trajectories can significantly enhance the optimization of these services. In this paper, we propose a novel field-road classification method that utilizes two feature vectors to represent each point in a GNSS trajectory. The first feature vector is a statistical feature vector extracted with existing methods, while the second feature vector is a visual feature vector obtained through an image segmentation model. To extract visual features, we first convert each GNSS trajectory into a trajectory image that encodes the motion information (e.g., speed, direction) of each point and the spatiotemporal relationship between points. Then, an image segmentation model specifically for field-road classification was developed, which can effectively extract a visual feature vector for each pixel in a trajectory image, corresponding to each point in the GNSS trajectory. The image segmentation model used in our approach has a robust feature extraction capability that enhances the statistical feature representation of each point by adding pixel-level visual features. We evaluated the effectiveness of our multi-view field-road classification method using trajectories of corn, wheat, and paddy harvesters. The results demonstrated that our method achieved accuracy scores of 94.17%, 90.93%, and 83.43% for respective datasets. Our method outperformed state-of-the-art field-road classification methods, especially for trajectories with high-frequency GNSS acquisition.

Fast GNSS acquisition algorithm based on SFFT with high noise immunity

The Global Navigation Satellite System(GNSS) has been widely used in various fields. To achieve positioning, the receiver must first lock the satellite signal. This is a complicated and expensive process that consumes a lot of resources of the receiver. For this reason, this paper proposes a new fast acquisition algorithm with High Signal-to-noise ratio(SNR) performance based on sparse fast Fourier transform(HSFFT). The algorithm first replaces the IFFT process of the traditional parallel code phase capture algorithm with inverse sparse fast Fourier transform (ISFFT) with better computing performance, and then uses linear search combined with code phase discrimination to replace the positioning loop and the estimation loop with poor noise immunity in ISFFT. Theoretical analysis and simulation results show that, compared with the existing SFFT parallel code phase capture algorithm, the calculation amount of this algorithm is reduced by 19%, and the SNR performance is improved by about 5dB. Compared with the classic FFT parallel code phase capture algorithm, the calculation amount of the algorithm in this paper is reduced by 43%, and when the capture probability is greater than 95%, the SNR performance of the two is approximately the same.

Deep Learning of GNSS Acquisition.

Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios.

Shipborne GNSS acquisition of sea surface heights in the Baltic Sea

Abstract For determining precise sea surface heights, six marine GNSS (global navigation satellite system) survey campaigns were performed in the eastern Baltic Sea in 2021. Four GNSS antennas were installed on the vessel, the coordinates of which were computed relative to GNSS–CORS (continuously operating reference stations). The GNSS–CORS results are compared to the PPP (precise point positioning)-based results. Better accuracy is associated with the GNSS–CORS postprocessed points; however, the PPP approach provided more accurate results for longer than 40 km baselines. For instance, the a priori vertical accuracy of the PPP solution is, on average, 0.050 ± 0.006 m and more stable along the entire vessel’s survey route. Conversely, the accuracy of CORS-based solutions decreases significantly when the distances from the GNSS–CORS exceed 40 km, whereas the standard deviation between the CORS and PPP-based solutions is up to 0.075 m in these sections. Note that in the harbor (about 4 km from the nearest GNSS–CORS), the standard deviation of vertical differences between the two solutions remains between 0.013 and 0.024 m. In addition, the GNSS antennas situated in different positions on the vessel indicated different measurement accuracies. It is suggested for further studies that at least one GNSS antenna should be mounted above the mass center of the vessel to reduce the effects of the dominating pitch motion during the surveys.

Adaptive Algebraic Reconstruction Algorithms for GNSS Water Vapor Tomography

Objectives Global navigation satellite system(GNSS) tomography, characterized by reconstructing the three-dimensional distribution of the atmospheric water vapor, has proved its capacity for studying the extreme weather events. The ill-conditioned problem resulting from the GNSS acquisition geometry is the critical issue of the GNSS tomography system. Algebraic reconstruction techniques, with the advantages of simple iteration and fast convergence, has been widely applied to the tropospheric tomography. However, the error allocation principle based on the intersection length of GNSS rays with each ray-voxel affects the accuracy of tomographic results. An improved adaptive algebraic reconstruction techniques are proposed to address this problem. Methods In view of the unreasonable error allocation in the traditional algorithm, we suggest a new error allocation principle based on the variation of water vapor density(WVD) in voxels for the adaptive algorithms, in which the product of the intersection length and WVD is considered as a new principle to redistribute the difference between the GNSS slant water vapor(SWV)and the reconstructed SWV. Besides, the weight matrix model of elevation angles is introduced to the improved algorithm to optimize the tomographic results. Results The new algorithms are tested by measured data from Xuzhou continuously operating reference stations(CORS) network and radiosonde during July2016. Experimental results reveal that the WVD derived from the adaptive algorithms performs better than that of the general algorithms in root mean squared error(RMSE), standard deviation(STD) and mean absolute error(MAE), and the RMSE is decreased by 25.91%, 15.81%, and 24.64% for adaptive algebraic reconstruction technique(AART), adaptive multiplicative algebraic reconstruction technique(AMART)and adaptive simultaneous iterative reconstruction technique(ASIRT) respectively. Under the conditions of light rain, moderate rain and heavy rain, the adaptive algorithms retrieve better water vapor profile than the traditional ones. Conclusions Overall, the water vapor profile derived from the adaptive algorithms agrees with the radiosonde distribution better than the traditional algorithms, which reveals the advantage of the proposed method on optimizing the tomography solutions.

A new integrated method of GNSS and MODIS measurements for tropospheric water vapor tomography

Global Navigation Satellite System (GNSS) tomography has proved its potential for sensing the three-dimensional (3D) distribution of atmospheric water vapor. Although the standard tomography model has improved, the geometry defect of GNSS acquisition remains a key problem and thus becomes the focus of this research. Thanks to the availability of high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) precipitable water vapor (PWV) maps, the integration of GNSS and MODIS measurements for tropospheric tomography system is introduced and discussed to address this problem. A methodology based on the node tomography model is developed to exploit the MODIS signals, which overcomes the disadvantage of the standard voxel model in combining the MODIS observations. Three experimental schemes based on MODIS data and simultaneous GNSS observations over the Xuzhou area are implemented to validate the proposed approach. The results show that the mean number of newly crossed voxels (i.e., punctured only by the MODIS signals) increases from 2 in the top layer to 24 in the first layer. Consequently, the average number of total crossed voxels is increased from 272 to 447, which highlights the contribution of the MODIS observations to pass through the empty voxels. Besides, the mean number of tomographic observation equations is enhanced by 35.71% when the MODIS signals are combined. Two-dimensional (2D) profiles of water vapor from radiosonde and 3D distributions of it from ERA5 data are considered to validate the proposed method. It is noted that when using the proposed approach, the mean root-mean-square error (RMSE) of the 2D tomographic profiles is decreased by a value of about 1.06 g/m3 and the overall accuracy of the 3D water vapor distribution is improved by 30.40%. Both the 2D and 3D validations demonstrate the satisfactory performance of the new integrated method to optimize the tomographic water vapor field.

Low-Computation GNSS Signal Acquisition Method Based on a Complex Signal Phase in the Presence of Sign Transitions

During signal acquisition in a global navigation satellite system acquisition stage, a method of signal parameters estimation with low computational complexity is needed. However, due to the influence of sign transitions, the correct peak corresponding to the signal parameters is difficult to detect. For the estimation of the code phase of the received signal in the presence of sign transitions with low computational complexity, an acquisition method based on a complex signal phase (AMCSP) is proposed. The problem of estimating the sign transition position and the code phase is transformed into a problem of solving for a complex signal phase. Special block matrixes are constructed to obtain the complex signal phase, and integration processing is utilized to improve the detection probability performance. Based on an analysis of undesirable cases, a final code phase estimation process is proposed. Furthermore, expressions for the detection performance and computational complexity of AMCSP are derived. Simulation results demonstrate that the computational cost of AMCSP is much lower than that of a fast Fourier transform-based method.

High Sensitivity and Fast Acquisition Signal Processing Techniques for GNSS Receivers: From fundamentals to state-of-the-art GNSS acquisition technologies

Higher sensitivity and faster acquisition can be two conflicting goals for a global navigation satellite system (GNSS) acquisition function, and both of the goals must be considered in the development of GNSS signal processing techniques to meet the demands for location-based services (LBSs) in GNSS-challenged environments. This article introduces the fundamentals of GNSS acquisition functions and various GNSS acquisition techniques for new GNSS signals and investigates recent acquisition techniques achieving high sensitivity and fast acquisition. It provides useful information for engineers who study state-of-the-art GNSS signal acquisition techniques and want to understand the challenges involved in improving GNSS acquisition sensitivity and acquisition time.

Orbital Filter Aiding of a High Sensitivity GPS Receiver for Lunar Missions

Recent studies have shown that weak Global Navigation Satellite System (GNSS) signals could potentially be used to navigate from the Earth to the Moon. This would increase autonomy, robustness and flexibility of the navigation architectures for future lunar missions. However, the utilization of GNSS signals at very high altitudes close to the Moon can be significantly limited by the very low power levels seen at the receiver’s antenna. This can result in a strongly reduced visibility of the GNSS satellites, which can worsen the already poor relative geometry of the GNSS receiver to the GNSS satellites. Furthermore, during most of a Moon Transfer Orbit (MTO), the very weak GNSS signals are also affected by Doppler shifts and Doppler rates larger than the ones generally experienced on the Earth, due to the much higher relative dynamics between the receiver and the transmitters. As a consequence, commercial GNSS receivers for terrestrial use cannot successfully acquire and track such signals. More advanced architectures and specific implementations are thus required to use GNSS for lunar missions. In this paper we propose the use of an adaptive orbital filter to aid the GNSS acquisition and tracking modules and to strongly increase the achievable navigation accuracy. The paper describes the orbital filter architecture and tests results carried out by processing realistic radio frequency (RF) signals generated by our Spirent GSS 8000 full constellation simulator for a highly elliptical MTO.

New formulation of GNSS acquisition with CFAR detection

Summary This paper deals with the analysis of the acquisition process performed by a global navigation satellite system (GNSS) receiver with a pilot and data channel or in case of GNSS hybrid receiver. Signal acquisition decides the presence or absence of GNSS signal by comparing signal under test with a fixed threshold and provides a code delay and a Doppler frequency estimation, but in low signal conditions or in a noisy environment; acquisition systems are vulnerable and can give a high false alarm and low detection probability. Firstly, we introduce a cell-averaging-constant false alarm rate (CFAR) then a data-pilot cell-averaging-CFAR detector fusion based to deal with these situations. In this context, we use a new mathematical derivation to develop a closed-form analytic expressions for the probabilities of detection and false alarm. The performances of the proposed detector are evaluated and compared with a non-CFAR case through an analytical and numerical results validated by Mont Carlo simulations. Copyright © 2016 John Wiley & Sons, Ltd.

An Unambiguous Deterministic Compressed Acquisition Technique for BOC Signal

The long PN code in a Binary Offset Carrier (BOC) modulated signal has a higher rate than the GPS C/A code, which makes the acquisition process in the new global navigation satellite system (GNSS) much more complex. To achieve a fast acquisition of the GNSS signal with a reduced number of correlators and low computational complexity, a two-stage deterministic compressed (DS) GNSS acquisition technique has been proposed. Nevertheless, the ambiguous threat of BOC signal isn’t taken into account in the DS technique. To eliminate the ambiguity threat in the acquisition process of BOC (m, n) signal, an unambiguous deterministic compressed acquisition is proposed in this paper. The main idea of the proposed technique is to introduce a simplified GRASS algorithm in the second stage measurement. Theoretical analysis and simulation results show that, at the expense of some performance degradation the proposed technique can completely remove the undesired positive side peaks of BOC (m, n) signal, and reduce the chance of false peak acquisition compared with the DS technique.

Design of FFT-Based TDCC for GNSS Acquisition

Due to the longer spreading code used for the next generation GNSS (Global Navigation Satellite System) signals, receivers have to spend longer time or require larger amount of hardware resources for signal acquisition. Since many recent GNSS receivers use DSP (Digital Signal Processor) to realize parallel signal acquisition scheme in the frequency domain, this paper proposes FFT-based TDCC (Two-Dimensional Compressed Correlator) with which coherently compressed hypotheses are tested using a reduced number of IFFT points in the 1st stage, and the individual hypotheses that construct the compressed hypothesis found in the 1st stage are tested using the conventional parallel search scheme in the 2nd stage. The performance of the proposed technique is demonstrated with numerous Monte Carlo simulations and a comparison to the conventional FFT-based search technique is provided. The results show that the proposed technique requires lower computation and has lower mean acquisition time than the conventional FFT-based search technique for moderate and high C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> (Carrier-to-Noise Density Ratio) GNSS signals.

Acquisition of GNSS signals in urban interference environment

In urban environment Global Navigation Satellite System (GNSS) signals are impaired by strong fading and by the presence of several potential sources of interference that can severely affect the acquisition. The work presented here evaluates the acquisition performance for the most common acquisition strategies in terms of receiver operating characteristics (ROC) and studies the impact of fading and interference on the acquisition performance. Two different interference scenarios are considered: a single interferer and a network of interferers. We present a framework to evaluate the GNSS acquisition performance with respect to all relevant system parameters that jointly considers the acquisition method, the effect of radio signal propagation conditions, and the spatial distribution of the interfering nodes.

Comparison Between Ratio Detection and Threshold Comparison for GNSS Acquisition

A comparison between two widespread global navigation satellite system (GNSS) acquisition strategies is presented. The first strategy bases its decision on comparing the energy within a cell to a threshold, while the second one uses the ratio between the two largest cell energies. It is shown that the first method outperforms the second one in terms of receiver operating characteristics (ROCs) in many practically relevant cases. Moreover, despite the purported simplicity of the ratio detection method, it is further shown that its complexity is comparable to or even higher than the one of threshold comparison with adaptive threshold setting.

Micro-electro-mechanical-sensor inertial navigation system-assisted global navigation satellite system receiver acquisition scheme and performance evaluation

When an aircraft moves under a low carrier-to-noise ratio (CNR) or at a high speed, increasing the sensitivity of global navigation satellite system (GNSS) receiver is a goal quite hard to achieve. A novel acquisition scheme assisted with micro-electro-mechanical-sensor (MEMS) inertial navigation system (INS) is presented to estimate the Doppler caused by user dynamics relative to each satellite ahead of time. Based on tightly coupled GNSS/INS estimation algorithm, MEMS INS Doppler error that can be achieved is first described. Then, by analyzing the mean acquisition time and signal detection probability, the MEMS INS-assisted acquisition capabilities in cold, warm and hot starts are quantitatively determined and compared with the standard GNSS acquisition capability. The simulations and comparisons have shown that: the acquisition time in cold start can be shortened by at least 23 s, the time in warm start can be shortened to 1 s and the acquisition capability is improved 95%, and the reacquisition time in hot start can be shortened by around 0.090 s and the capability can be enhanced 40%. The results demonstrate the validity of the novel method.

GNSS Acquisition in the Presence of Continuous Wave Interference

The extreme weakness of a global navigation satellite system (GNSS) signal makes it vulnerable to a wide variety of interfering signals, falling within the GNSS frequency bands. One of the main classes of these disturbing signals is represented by continuous wave interference (CWI), which include all signals that can be effectively represented as pure sinusoids. This paper deals with the performance of GNSS signal acquisition in the presence of CWI. An original model describing the impact of CWI on signal acquisition is proposed and the expressions of both false alarm and detection probabilities are mathematically formulated and validated by Monte Carlo simulations. The paper also investigates the role of the interference amplitude and frequency along with the impact of different system parameters on the detection and false alarm probabilities.