Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading

Large flexible engineering structures, such as long span bridges or tall buildings, are susceptible to quasistatic and dynamic deformations caused by different loadings, thus accurate displacement measurements are desirable to assess the integrity and reliability of the structure. In this study, an integrated system that includes Global Positioning System (GPS), accelerometer and anemometer was developed to obtain the responses of a long span bridge to the extreme wind loadings. Spectral analysis based on the Fast Fourier Transform (FFT) algorithm was first carried out to detect the dominant frequencies of the middle pylon. Then the noisy GPS displacement measurements and accelerometer data are de-noised using the Vondrak filter, and the low frequency disturbance was separated from GPS displacement time series. A least-squares based displacement reconstruction scheme using noise-mitigated accelerations was employed, and the Tikhonov regularization scheme with optimal selected regularization factor was used to alleviate the ill-posedness. At last, an adaptive recursive least squares (RLS) filter was adopted to separate the slow-varying movements, and the total displacement with enhanced measurement accuracy was obtained from the combined quasi-static and high-frequency dynamic displacements. A field monitoring data set collected on the Erqi Yangtze River Bridge, a three-tower cable-stayed bridge located in Wuhan, China, was used to validate the effectiveness of the proposed integration processing scheme. The GPS/accelerometer/anemometer installed on the center supporting tower was used to characterize the interaction between the responses and the ambient wind loadings. The results demonstrate the proposed technique can significantly improve the measurement accuracy of pylon displacement under strong winds. The deformation accuracy with the amplitude of several millimeters can be successfully detected,and the spectrum of the pylon response obtained from both GPS data and accelerometer data reveals the identified first dominant frequency of the middle pylon is 0.172 Hz

Review on Sparse-Based Multipath Estimation and Mitigation: Intense Solution to Counteract the Effects in Software GPS Receivers

Multipath is the major concern in GPS receivers that fade the actual GPS signal causes positioning error up to 10 m so special care need to be taken to mitigate the multipath effects. Numerous methods like hardware based antenna arrays technique, receiver based narrow correlator receiver, double -delta discriminator, Adaptive Multipath Estimator, Wavelet Transformation and Particle filter, Kalman filter based post receiver methods etc. used to resolve the problem. But some of the methods can only reduce code multipath error but not effective in eliminating carrier multipath error. Most of these techniques are based on the assumption that the Line-of-Sight (LOS) signal is stronger than the Non-Line of-Sight (NLOS) signals. However, in the scenarios where the LOS signal is weaker than the composite multipath signal, this approach may result in a bias in code tracking. In this chapter, different types of multipath mitigation and its limitation are described. The recent development in sparse signal processing based blind channel estimation is investigated to compensate the multipath error. The Rayleigh and Rician fading model with different multipath parameters are simulated to test the urban scenario. The inverse problem of finding the GPS signal is addressed based on the deconvolution approach. To solve linear inverse problems, the suitable kind of appropriate objective function has been formulated to find the signal of interest. By exploiting this methods, the signal is observed and the carrier and code tracking loop parameters are computed with minimal error

On Fault Detection and Exclusion in Snapshot and Recursive Positioning Algorithms for Maritime Applications

Resilient provision of Position, Navigation and Timing (PNT) data can be considered as a key element of the e-Navigation strategy developed by the International Maritime Organization (IMO). An indication of reliability has been identified as a high level user need with respect to PNT data to be supplied by electronic navigation means. The paper concentrates on the Fault Detection and Exclusion (FDE) component of the Integrity Monitoring (IM) for navigation systems based both on pure GNSS (Global Navigation Satellite Systems) as well as on hybrid GNSS/inertial measurements. Here a PNT-data processing Unit will be responsible for both the integration of data provided by all available on-board sensors as well as for the IM functionality. The IM mechanism can be seen as an instantaneous decision criterion for using or not using the system and, therefore, constitutes a key component within a process of provision of reliable navigational data in future navigation systems. The performance of the FDE functionality is demonstrated for a pure GNSS-based snapshot weighted iterative least-square (WLS) solution, a GNSS-based Extended Kalman Filter (EKF) as well as for a classical error-state tightly-coupled EKF for the hybrid GNSS/inertial system. Pure GNSS approaches are evaluated by combining true measurement data collected in port operation scenario with artificially induced measurement faults, while for the hybrid navigation system the measurement data in an open sea scenario with native GNSS measurement faults have been employed. The work confirms the general superiority of the recursive Bayesian scheme with FDE over the snapshot algorithms in terms of fault detection performance even for the case of GNSS-only navigation. Finally, the work demonstrates a clear improvement of the FDE schemes over non-FDE approaches when the FDE functionality is implemented within a hybrid integrated navigation system

Multi-Constellation GNSS Multipath Mitigation Using Consistency Checking

In a typical urban environment, a mixture of multipath-free, multipath-contaminated and non-line-of-sight (NLOS) propagated GNSS signals are received. The errors caused by multipath-contaminated and NLOS reception are the dominant source of reduced consumer-grade positioning accuracy in the urban environment. Many conventional receiver-based and antenna-based techniques have been developed to mitigate either multipath or NLOS reception with mixed success. Nevertheless, the positioning accuracy can be maximised based on the simple principle of selecting only those signals least contaminated by multipath and NLOS propagation to form the navigation solution. The advent of multi-constellation GNSS provides the opportunity to realise this technique that is potentially low-cost and effective for consumer-grade devices. It may also be implemented as an augmentation to other multipath mitigation techniques. The focus of this paper is signal selection by consistency checking, whereby measurements from different satellites are compared with each other to identify the NLOS and most multipath-contaminated signals. The principle of consistency checking is that multipath-contaminated and NLOS measurements produce a less consistent navigation solution than multipath-free measurements. RAIM-based fault detection operates on the same principle. Three consistency-checking schemes based on single-epoch least-squares residuals are assessed: single sweep, recursive checking and a hybrid version of the first two. Two types of weighting schemes are also considered: satellite elevation-based and signal C/N0-based weighting. The paper also discussed the different observables that may be used by a consistency-checking algorithm for different applications and their effect on detection sensitivity. Test results for the proposed algorithms are presented using data from both static positioning and stand-alone dynamic positioning experiments. The static data was collected using a pair of survey-grade multi-constellation GNSS receivers using both GPS and GLONASS signals at open sky and urban canyon locations, while the dynamic data was collected using a consumer-grade GPS/GLONASS receiver on a car in a mixed urban environment. Significant improvements in position domain are demonstrated using the weighted recursive methods in the open environments. However in the urban environments, there are insufficient directly received signals for the conventional RAIM-based signal selection to be effective all the time. Both positioning improvements and risky outliers are demonstrated. More advanced techniques have been identified for investigation in future research

Performance Improvement of GNSS Receiver by Mitigation of Multipath Effects

The Rake Receiver is excessively used in modern CDMA communication but for navigation application it is not self-sufficient to provide satisfactory performance. To overcome the shortcomings of Rake Receiver for navigation purpose one needs to introduce some differential rake architecture which will mitigate the multipath corresponding to the incoming data after it has been processed and demodulated. In this manner a particular will be able to cancel out the multipath and at the end will have only the strongest multipath from which decision can be made to recover the data back. The other very critical module is Delay Locked Loop (DLL) which will align the code at the receiver end so as to minimize the pseudo range error. The DLL will try to lock the incoming signal with the local code and in order to do so it will consider 3 different locally generated codes Early, Prompt and Late. According to the parameter defined, it may accuracy up to one tenth of a chip. The DLL will use the code-phase provided by the previous blocks and try to find the local code which will give us the minimum pseudo range error. If the multipath signals are delayed by more than 1.5 chips then matched filter algorithm will detect all three signals by processing on auto-correlation function. But when delay is less than 1.5 chips then NLMS algorithm is used for multipath detection. This two algorithm is incorporated in this design. Whenever incoming signal is received it will first try to find out multipath components within 1.5 chips. After that it will go to the next step in order to find multipath components outside 1.5 chips. In this project the above mentioned approaches are combined so as to get a system which will give the optimum performance in terms of the SNR and the pseudo range

Reduction of initial convergence period in GPS PPP data processing

Precise Point Positioning (PPP) has become a popular technique to process data from GPS receivers by applying precise satellite orbit and clock information, along with other minor corrections to produce cm to dm-level positioning. Although PPP presents definite advantages such as operational flexibility and cost effectiveness for users, it requires 15-25 minutes initialization period as carrier-phase ambiguities converge to constant values and the solution reaches its optimal precision. Pseudorange multipath and noise are the largest remaining unmanaged errors source in PPP. It is proposed that by reducing these effects carrier-phase ambiguities will reach the correct steady state at an earlier time, thus reducing the convergence period of PPP. Given this problem, this study seeks to improve management of these pseudorange errors. The well-known multipath linear combination was used in two distinct ways: 1) to directly correct the raw pseudorange observables, and 2) to stochastically de-weight the pseudorange observables. Corrections to the observables were made in real-time using data from the day before, and post-processed using data from the same day. Post-processing has shown 4 7% improvement in the rate of convergence, as the pseudorange multipath and noise were effectively mitigated. A 36% improvement in the rate of convergence was noted when the pseudorange measurements were stochastically de-weighting using the multipath observable. The strength of this model is that it allows for real-time compensation of the effects of the pseudorange multipath and noise in the stochastic model

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

A novel dynamical filter based on multi-epochs least-squares to integrate the carrier phase and pseudorange observation for GNSS measurement

© 2020 by the authors. The high noise of pseudorange and the ambiguity of carrier phase observation restrain the GNSS (Global Navigation Satellite System) application in military, industrial, and agricultural, to name a few. Thus, it is crucial for GNSS technology to integrate the pseudorange and carrier phase observations. However, the traditional method proposed by Hatch has obtained only a low convergence speed and precision. For higher convergence speed and precision of the smoothed pseudorange, aiming to improve positioning accuracy and expand the application of GNSS, we introduced a new method named MELS (Multi-Epochs Least-Squares) that considered the cross-correlation of the estimating parameters inspired by DELS (Double-Epochs Least-Square). In this study, the ionospheric delay was compensated, and so its impact was limited to the performance of the filters, and then exploited the various filters to integrate carrier phase observation and pseudorange. We compared the various types of Hatch's filter and LS (Least-Square) methods using simulation datasets, which confirmed that the types of LS method provided a smaller residual error and a faster convergence speed than Hatch's method under various precisions of raw pseudorange. The experimental results from the measured GNSS data showed that LS methods provided better performance than Hatch's methods at E and U directions and a lower accuracy at N direction. Nevertheless, the types of LS method and Hatch's methods improved about 12% and 9-10% at the 3D direction, respectively, which illustrated the accumulating improvement at the enhanced directions was more than the decreased direction, proving that the types of LS method resulted to better performance than the Hatch's filters. Additionally, the curve of residual and precision based on various LS methods illustrated that the MELS only provided a millimeter accuracy difference compared with DELS, which was proved by the simulated and measured GNSS datasets