Hasil untuk "Canals and inland navigation. Waterways"

Fernando D. Nunes, Fernando M. G. Sousa

Evil waveforms (EWFs) are anomalies in signals transmitted by a global navigation satellite system, provoked by electric malfunctions, that can significantly degrade the accuracy of the receiver’s position, velocity, and time solution. In this work, cross-correlation functions of a received signal disturbed by EWF distortion and the locally generated code signal are derived for threat models TM-A, TM-B, and TM-C, with expressions obtained for binary phase shift keying, binary offset carrier (BOC), and composite BOC pilot modulations. Closed-form expressions are offered in terms of sine integral functions for TM-A. A description is presented on how to use these results to assess the performance of EWF detectors through semi-analytic simulation, allowing the detectability and hazard regions to be determined in a computationally efficient way.

Julien Burkhard, Aman Sharma, Jan Skaloud

The deterministic errors of an accelerometer comprise the prevailing i) bias, ii) scale factor, and iii) non-orthogonality. Together, these errors result in a nonlinear measurement model, which is conventionally solved via an iterative nonlinear least-squares method. In contrast to the conventional approach, we propose a novel method to transform the above nonlinear model into a system of linear equations, resulting in an exact, closed-form solution of the deterministic errors. The developed mathematical formulations are first verified in a simulation setting, followed by a real-time implementation using Robot Operating System for small micro-electromechanical inertial measurement units.

Josef Krška, Václav Navrátil

An integral step in an ultra-wideband localization network installation is determining the positions of the fixed infrastructure nodes, the anchors. This process is time-consuming and usually requires specialized equipment. Additionally, it is difficult to achieve scalability, as any change or addition in the network requires a redetermination of the affected anchors. One can automate this process by utilizing the distance-measuring capabilities of the network infrastructure and employing a distributed position estimation algorithm, such as the consensus subgradient (CSG) algorithm. Yet, the CSG suffers from scalability issues due to high problem dimensionality and data-sharing bottlenecks in practical applications. Consequently, implementation in embedded devices is difficult. In this article, we propose a modification of this algorithm, the neighborhood CSG, which aims toward embedded implementation by local reduction of the problem dimensions without hindering the precision of the original CSG algorithm or its convergence rate.

Yiran Luo, Li-Ta Hsu, Naser El-Sheimy

Low-cost global navigation satellite system and global positioning system(GPS) receivers require reliable baseband processing to guarantee accurate positioning. However, classic baseband performance is limited in challenging cases due to the characteristics of traditional loop filters. Accordingly, a snapshot baseband maximum likelihood estimator (MLE) using super-long coherent integration (S-LCI) in a fractional Fourier domain (FrFD) is proposed to upgrade the traditional frequency/phase/delay lock loop tracking algorithms. First, applying the S-LCI correlation in an FrFD increases the accuracy of a weak and dynamic signal estimation. Tolerance of the initial guess error in the snapshot baseband processing is then relaxed by the MLE. Finally, a gradient descent algorithm accelerates the convergence of signal estimation. Moreover, we derive the Cramer-Rao lower bound for the proposed MLE. Both numerical simulations and real-world experiments based on this GPS receiver prototype verify the effectiveness of its high-accuracy estimations of weak signals, strong tolerance for large initial guess errors, and prompt responses to converging.

Yunxiang Liu, Zhe Yang, Y. Jade Morton et al.

In this paper, we present a spatiotemporal deep learning (STDL) network to conduct binary phase scintillation forecasting at a high-latitude global navigation satellite systems (GNSS) station. Historical measurements from the target and surrounding GNSS stations are utilized. In addition, external features such as solar wind parameters and geomagnetic activity indices are also included. The results show that the STDL network can adaptively incorporate spatiotemporal and external information to achieve the best performance by outperforming a naive method, three conventional machine learning algorithms (logistic regression, gradient boosting decision tree, and fully connected neural network) and a machine learning algorithm known as long short-term memory that incorporates temporal information.

Tobias Bamberg, Andriy Konovaltsev, Michael Meurer

New GNSS applications demand resilience against radio interference and high position accuracy. Separately, these demands can be fulfilled by multi-antenna systems using spatial filtering and carrier-phase positioning algorithms like real-time kinematic (RTK), respectively. However, combining these approaches encounters a severe issue: The spatial filtering induces a phase offset into the measured carrier phase leading to a loss of position accuracy. This paper presents a new approach to compensate for the phase offset in a blind manner (i.e., without knowing the antenna array radiation pattern or the direction of arrival of the signals). The proposed approach is experimentally validated in two jamming scenarios. One includes a jammer with increasing power and the other includes a moving jammer. The results demonstrate that the approach successfully compensates for the phase offset and, hence, allows for the combined use of RTK positioning and spatial filtering even under jamming.

Richard B. Langley

Welcome to the Spring 2022 issue of NAVIGATION – our first issue since transitioning to an open access journal at the beginning of the year. As I mentioned in “Navigator Notes” in the Winter 2021 issue, all journal articles from now on will be available for download without charge. And Institute of Navigation members will be able to download complete issues. Consult the ION website at https://navi.ion.org for details. In this issue, we again feature articles on a wide range of topics including detection of GPS satellite oscillator anomalies, vision localization, and GNSS spoofing. We are also featuring an article on the design of a lunar navigation satellite system as well as a comprehensive article on how the Wide Area Augmentation System monitors the state of the ionosphere to protect users from ionospheric disturbances. ION will continue promoting the research of journal authors through video abstracts hosted on the ION website. In fact, a video abstract will become compulsory for publishing in NAVIGATION. The latest video abstracts are documented below. ION also engages with the PNT community, through its webinar series, to highlight current topics of interest to the community. The most recent webinars are also documented below and we announce the ION 2021 Samuel M. Burka Award winner.

Salem Abd El-Hakem Hegazy, Ahmed M. Kamel, Ibrahim Ismail Arafa et al.

A circular error probability (CEP) metric in ballistic missile science is an experimental indicator of the accuracy of a missile system. There are a lot of error sources that cause a ballistic missile to deviate from its ideal trajectory, and that causes a deviation from required CEP. This work discusses the problems of dispersion of ballistic missiles due to inertial navigation system (INS) errors. INS deterministic errors are usually calibrated and compensated using some proper techniques. However, INS stochastic errors can be modeled and analyzed. In this study, a chosen missile is thoroughly analyzed using the six degrees-of-freedom missile flight trajectory simulator. A Monte Carlo simulation is used to generate a large number of flight trajectories to inspect the effect of INS stochastic noise on missile CEP. Moreover, a strategy for selecting an adequate sensor according to mission requirements and its corresponding sensor errors is introduced.