Search Results (71)

In deformation monitoring, complex environments, such as seismic excitation, often lead to noise during signal acquisition and transmission processing. This study integrates sequential variational mode decomposition (SVMD), a dual-parameter (DP) model, and an improved wavelet threshold function (IWT), presenting a denoising method termed SVMD-DP-IWT. Initially, SVMD decomposes the signal to obtain intrinsic mode functions (IMFs). Subsequently, the DP parameters are determined using fuzzy entropy. Finally, the noisy IMFs denoised by IWT and the signal IMFs are used for signal reconstruction. Both simulated and engineering measurements validate the performance of the proposed method in mitigating noise. In simulation experiments, compared to wavelet soft-threshold function (WST) with the sqtwolog threshold, the root-mean-square error (RMSE) of SVMD-Dual-CC-WST (sqtwolog threshold), SVMD-DP-IWT (sqtwolog threshold), and SVMD-DP-IWT (minimaxi threshold) improved by 51.44%, 52.13%, and 52.49%, respectively. Global navigation satellite system (GNSS) vibration monitoring was conducted outdoors, and the accelerometer vibration monitoring experiment was performed on a pseudo-classical building in a multi-functional shaking table laboratory. GNSS displacement data and acceleration data were collected, and analyses of the acceleration signal characteristics were performed. SVMD-DP-IWT (sqtwolog) and SVMD-DP-IWT (minimaxi) effectively retain key vibration signal features during the denoising process. The proposed method significantly preserves vibration features during noise reduction of an ancient building in deformation monitoring, which is crucial for damage assessment. Full article

Acquiring and tracking multiple frequencies across many GNSS satellites introduces computation complexity, resulting in higher power consumption. A relatively small but optimal list of signals and satellites may result in better performance without adding additional complexity load and unnecessary power loss. This research intended to generate an optimal list of satellites and signals and to prioritise them to be tracked by the receiver, minimising unnecessary processing. To achieve this, an optimal geometry is generated using a bootstrapping algorithm, which also introduces an innovative index to determine what to add to the satellites/signals list to prioritise acquisition and tracking. Full article

The resilience of Global Navigation Satellite System (GNSS) usage against spoofing attacks can be increased by signal monitoring algorithms aiming to detect a spoofing signal at the acquisition stage of GNSS receiver signal processing. A common approach is to search for the presence of multiple correlation peaks in the absolute value of the Cross-Ambiguity Function (CAF). In this context, it is particularly challenging to detect spoofing signals with a correlation peak closely aligned to that of the authentic signal, as is the case at the early stage of a coherent spoofing attack. In the present work, a spoofing detection method is proposed that monitors the magnitude of the CAF by means of clustering techniques. It is designed to detect the pull-off during a coherent power-matched spoofing attack already at an early stage. The method is evaluated for the GPS L1 C/A signal based on a static scenario from the Texas Spoofing Test Battery (TEXBAT) data set as well as for the Galileo E1-B signal based on a real-world digital snapshot recording in the E1 frequency band that is augmented by emulated spoofing signals at the level of digital signal processing. Full article

Currently, autonomous flight control for unmanned aerial vehicles (UAVs) has become increasingly critical in remote-sensing applications, such as high-resolution data acquisition and road disease detection. However, this task also faces significant challenges, particularly the weak GNSS signals in flight areas and the complex flight environment. Furthermore, many existing autonomous-flight-control algorithms for UAVs are computationally demanding, which limits their deployment on embedded devices with constrained memory and processing power, thereby affecting both operational efficiency and the safety of UAV missions. To address these issues, we propose PISCFF-LNet, a lightweight road-extraction network that integrates prior knowledge and spatial contextual features. The network employs a dual-branch encoder architecture to separately extract spatial and contextual features, thus obtaining multi-dimensional feature representations. In addition, to enhance the integration of different features and improve the overall feature representation, we also introduce a feature-fusion module. To further enhance UAV performance, we introduce an improved ray-based eight neighborhood algorithm (RENA), which efficiently extracts road-edge information with a remarkably low latency of just 7 ms, providing accurate flight guidance and reducing misidentification. To provide a comprehensive evaluation of the model’s performance, we have developed a new drone remote-sensing road-semantic-segmentation dataset, DRS Road, which includes approximately 2600 ultra-high-resolution remote-sensing images across six scene categories. The experimental results demonstrate that PISCFF-LNet achieves improvements of 1.06% in Intersection over Union (IoU) and 0.83% in F1-Score on the DeepGlobe Road dataset, and 1.03% in IoU and 0.57% in F1-Score on the DRS Road dataset, compared to existing methods. Finally, we applied the algorithm to a UAV, using a PID-based flight-control algorithm. The results show that drones employing our algorithm exhibit superior flight performance in both simulated and real-world environments. Full article

Global Navigation Satellite System (GNSS) meta-signals are obtained when components from different frequencies are jointly processed as a single entity. While most research work has focused on GNSS meta-signals made of two side-band components, meta-signal theory has been recently extended to the case where the number of components is a power of two. This condition was dictated by the use of multicomplex numbers for the representation of GNSS meta-signals. Multicomplex numbers are multi-dimensional extensions of complex numbers whose dimension is a power of two. In this paper, the theory is further extended and a procedure for the construction of GNSS meta-signals with an arbitrary number of components is provided. Also in this case, multicomplex numbers are used to effectively represent a GNSS meta-signal. From this representation, multi-dimensional Cross Ambiguity Functions (CAFs) are obtained and used to derive acquisition and tracking algorithms suitable for the joint processing of components from different frequencies. The specific case with three components is analysed. Theoretical results are supported by experimental findings obtained by jointly processing Galileo E5a, E5b and E6 signals collected using three synchronized Software-Defined Radio (SDR) HackRF One front-ends. Experimental results confirm the validity of the developed theory. Full article

Signal distortion bias (SDB) in Global Navigation Satellite System (GNSS) data processing, defined as the time difference between the distorted chip and the ideal rectangular chip, leads to systematic biases in pseudoranges, affecting satellite and receiver differential code biases (DCBs). The stability of SDBs, allowing them to be treated as constant values, highlights the importance of investigating both their stability and estimation accuracy. Two different methods are used to estimate SDBs: (1) the hybrid method and (2) the geometry-free method. Data from approximately 430 stations, spanning the entire year of 2021, were analyzed to evaluate the estimation accuracy and the short-term and long-term stability of GPS SDBs. The analysis focused on two code signals: C1C (L1 Coarse/Acquisition) and C2W (L2 P(Y)). The results show that the short-term and long-term stability of GPS C1C and C2W SDBs is comparable for both methods, with only minor variations between them. Additionally, one month of data were used to validate the accuracy of estimated SDBs across different receiver groups. The results demonstrate that geometry-free SDBs provide stable satellite DCB estimates with an average bias below 0.15 ns and minimal residual biases, while hybrid SDBs provide satellite DCB estimates with an average bias below 0.20 ns. Overall, the comparison underscores the superior performance of geometry-free SDBs in achieving consistent satellite DCB estimates. Full article

The recent rapid development of low Earth orbit (LEO) constellation-based navigation techniques has enhanced the ability of position, navigation, and timing (PNT) services in deep attenuation and interference environments. However, existing navigation modulations face the challenges of high acquisition complexity and do not improve measurement precision at the same signal strength. We propose a pulsed orthogonal time frequency space (Pulse-OTFS) signal, which naturally converts continuous signals into pulses through a special delay-Doppler domain pseudorandom noise (PRN) code sequence arrangement. The performance evaluation indicates that the proposed signal reduces at least 89.4% of the acquisition complexity. The delay measurement accuracy is about 8 dB better than that of the traditional binary phase shift keying (BPSK) signals with the same bandwidth. It also provides superior compatibility and anti-multipath performance. The advantages of fast acquisition and high-precision measurement are verified by processing the real signal in the developed software receiver. As Pulse-OTFS occupies only one time slot of a signal period, it can be easily integrated with OTFS-modulated communication signals and used as a navigation signal from broadband LEO satellites as an effective complement to the global navigation satellite system (GNSS). Full article

Global Navigation Satellite Systems (GNSS) provide positioning, velocity, and time services for civilian applications. A critical step in the positioning process is the acquisition of visible satellites in the sky. Modern GNSS systems, such as Galileo—developed and maintained by the European Union—utilize a new modulation technique known as Binary Offset Carrier (BOC). However, BOC signals introduce multiple side-peaks in their autocorrelation function, which can lead to significant errors during the acquisition process. In this paper, we propose a novel acquisition method based on higher-order cumulants that effectively eliminates these side-peaks. This method is capable of simultaneously acquiring both conventional ranging signals, such as GPS C/A code, and BOC-modulated signals. The effectiveness of the proposed method is demonstrated through the acquisition of simulated signals, with a comparison to traditional methods. Additionally, we apply the proposed method to real satellite signals to further validate its performance. Our results show that the proposed method successfully suppresses side-peaks, improves acquisition accuracy in weak signal environments, and demonstrates potential for indoor GNSS applications. The study concludes that while the method may increase computational load, its performance in challenging conditions makes it a promising approach for future GNSS receiver designs. Full article

The precise point positioning (PPP) service via the B2b signal (PPP-B2b) on the BeiDou Navigation Satellite System (BDS) provides high-accuracy orbit and clock data for global navigation satellite systems (GNSSs), enabling real-time atmospheric data acquisition without internet access. In this study, we assessed the quality of orbit, clock, and differential code bias (DCB) products from the PPP-B2b service, comparing them to post-processed products from various analysis centres. The zenith tropospheric delay (ZTD) and precipitable water vapour (PWV) were computed at 32 stations using the PPP technique with PPP-B2b corrections. These results were compared with post-processed ZTD with final orbit/clock products and ZTD/PWV values derived from the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) and radiosonde data. For stations between 30° N and 48° N, the mean root mean square error (RMSE) of ZTD for the PPP-B2b solution was approximately 15 mm compared to ZTD from the International GNSS Service (IGS). However, accuracy declined at stations between 30° N and 38° S, with a mean RMSE of about 25 mm, performing worse than ZTD estimates using Centre National d’Études Spatiales (CNES) products. The mean RMSEs of PWV derived from PPP-B2b were 3.7 mm and 4.4 mm when compared to PWV from 11 co-located radiosonde stations and ERA5 reanalysis, respectively, and underperformed relative to CNES solutions. Seasonal variability in GNSS-derived PWV was also noted. This reduction in accuracy limits the global applicability of PPP-B2b. Despite these shortcomings, satellite-based PPP services like PPP-B2b remain viable alternatives for real-time positioning and atmospheric applications without requiring internet connectivity. Full article

A multi-constellation, multi-frequency Global Navigation Satellite System (GNSS) receiver is capable of simultaneously receiving signals from multiple satellite constellations across various frequency bands. This allows for increased observations, thereby enhancing navigation accuracy, continuity, effectiveness, and reliability. The spread spectrum code structures used in satellite navigation signals differ among systems. Compatible code generators are employed in multi-constellation, multi-frequency GNSS receivers to support tasks such as signal acquisition and tracking. There are three main types of spread spectrum code structures: Linear Feedback Shift Register (LFSR), Legendre sequences, and Memory codes. The Indian Regional Navigation Satellite System (IRNSS) released the L1-SPS (Standard Positioning Service) signal format in August 2023, which utilizes the Interleaved Z4-linear ranging code (IZ4 code) as its spread spectrum code. Currently, there is no universal code generator design compatible with the IZ4 code. In this paper, a proposed universal code generator is based on the hardware structure of the IRNSS IZ4 code generator. It achieves compatibility with all LFSR-based spread spectrum codes and enables parallel generation of multiple sets of GNSS signal spread spectrum codes, thereby improving hardware utilization efficiency. The proposed structure is implemented and validated using FPGA design, and resource consumption is provided as part of the validation results. Full article

Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To determine the pose of the monocular query images collected from a moving vehicle, neighboring GSVs should be found to establish the correspondence through image-wise and block-wise matching. First, each query image is semantically segmented and a template containing all permanent objects is generated. The template is then utilized in conjunction with a template matching approach to identify the corresponding patch from each GSV image within the database. Through the conversion of the query template and corresponding GSV patch into feature vectors, their image-wise similarity is computed pairwise. To ensure reliable matching, the query images are temporally grouped into query blocks, while the GSV images are spatially organized into GSV blocks. By using the previously computed image-wise similarities, we calculate a block-wise similarity for each query block with respect to every GSV block. A query block and its corresponding GSV blocks of top-ranked similarities are then input into a photogrammetric triangulation or structure from motion process to determine the pose of every image in the query block. A total of three datasets, consisting of two public ones and one newly collected on the Purdue campus, are utilized to demonstrate the performance of the proposed method. It is shown it can achieve a meter-level positioning accuracy and is robust to changes in acquisition conditions, such as image resolution, scene complexity, and the time of day. Full article

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector. Full article

Rapidly stitching unmanned aerial vehicle (UAV) imagery to produce high-resolution fast-stitch maps is key to UAV emergency mapping. However, common problems such as gaps and ghosting in image stitching remain challenging and directly affect the visual interpretation value of the imagery product. Inspired by the data characteristics of high-precision satellite images with rich access and geographic coordinates, a seamless stitching method is proposed for emergency response without the support of ground control points (CGPs) and global navigation satellite systems (GNSS). This method aims to eliminate stitching traces and solve the problem of stitching error accumulation. Firstly, satellite images are introduced to support image alignment and geographic coordinate acquisition simultaneously using matching relationships. Then a dynamic contour point set is constructed to locate the stitching region and adaptively extract the fused region of interest (FROI). Finally, the gradient weight cost map of the FROI image is computed and the Laplacian pyramid fusion rule is improved to achieve seamless production of the fast-stitch image map with geolocation information. Experimental results indicate that the method is well adapted to two representative sets of UAV images. Compared with the Laplacian pyramid fusion algorithm, the peak signal-to-noise ratio (PSNR) of the image stitching results can be improved by 31.73% on average, and the mutual information (MI) can be improved by 19.98% on average. With no reliance on CGPs or GNSS support, fast-stitch image maps are more robust in harsh environments, making them ideal for emergency mapping and security applications. Full article

Global navigation satellite systems (GNSSs) are an integral part of global positioning. However, because GNSS performance is impacted by signal obscuration and the presence of multipath in urban and deep urban environments, it is not accurate, reliable, and widely available enough to be a standalone system in all environments. This creates two problems: (1) the GNSS user does not know when or where GNSS performance may be degraded and (2) the GNSS user has limited ability to mitigate these issues. No mitigation strategy exists to improve the availability of GNSSs themselves. Inertial measurement units (IMUs) and sensor fusion provide other costly methods to improve positioning performance, but most systems still rely on GNSSs for absolute position. Spirent’s GNSS Foresight service aims to solve both issues. As a cloud-based solution, GNSS Foresight provides satellite and signal information, and this can be employed to support the decision-making strategy and calculations in the GNSS receiver to improve its positioning solution performance, integrity, and reliability. In this paper, GNSS Foresight is introduced, and a performance evaluation of GNSS Foresight in dense urban areas is presented. Using the data collected from two urban areas in North America, we evaluated GNSS Foresight and compared the performance of GNSS positioning solutions with and without Foresight-aided data. The comparison results show the observed improvements in GNSS receiver operation. Foresight can also be used to develop measurement engine performance enhancements in the acquisition of new satellites and the tracking/re-acquisition of current satellites using line-of-sight (LOS) satellite information. In the positional computation process, Foresight enables receivers to prioritize LOS signals over degraded non-line-of-sight (NLOS) signals, hence significantly reducing positioning errors and outperforming conventional GNSS positioning, particularly in difficult urban environments. Full article

In order to realize real time monitoring of foundation displacement of railway cable bridge based on satellite observation system, reasonable data sources of satellite monitoring and data analysis duration are necessary, and the influence of various errors should be eliminated in the process of satellite signal acquisition. In this paper, the validity of foundation settlement monitoring of bridge tower by satellite is verified through accuracy and stability tests. For eliminating the multipath error and random noise of GNSS signal, a new method is presented by combining stationary wavelet transform and empirical mode decomposition in this study. For improving the observation accuracy of the satellite monitoring system, combing the measured data, the GNSS data are corrected by establishing non-linear mapping between the GNSS data and precise leveling data by BP neural network. Based on that, the accuracy of the presented method is verified by the foundation settlement data of a new railway cable bridge tower. Full article