Integrity is one criteria to evaluate GNSS performance, which was first introduced in the aviation field. It is a measure of trust which can be placed in the correctness of the information supplied by the total system. In recent years, many GNSS-based applications emerge in the urban environment including liability critical ones, so the concept of integrity attracts more and more attention from urban GNSS users. However, the algorithms developed for the aerospace domain cannot be introduced directly to the GNSS land applications. This is because a high data redundancy exists in the aviation domain and the hypothesis that only one failure occurs at a time is made, which is not the case for the urban users. The main objective of this paper is to provide an overview of the past and current literature discussing the GNSS integrity for urban transport applications so as to point out possible challenges faced by GNSS receivers in such scenario. Key differences between integrity monitoring scheme in aviation domain and urban transport field are addressed. And this paper also points out several open research issues in this field.

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GNSS Position Integrity in Urban Environments: A Review of Literature

Current GPS positioning accuracy in urban areas is still unsatisfactory for various applications, including pedestrian navigation and autonomous driving. Due to the ineffectiveness of special corrector designs against non-line-of-sight (NLOS) reception, the research regarding NLOS signals has been increasing in the recent years. This study first develops an algorithm to detect NLOS signals from the pseudorange measurements by using a 3D building model, ray-tracing simulation, and known receiver position. According to the analysis of 24 h of collected NLOS data, a new finding is that NLOS pseudorange delay is highly correlated with the elevation angle of satellite instead of the received signal strength. Thus, we further propose an innovative NLOS model using two variables, the elevation angle and the distance between the receiver and building that reflect the NLOS. The proposed model is evaluated in both pseudorange and position domains. Based on the experiment results regarding pseudorange error, the difference between the proposed model and the collected NLOS measurement is very small. Finally, the proposed model is applied to a hypothesis-based positioning method and achieves about 6.3 m in terms of horizontal positioning accuracy, which is only slightly worse than the method applied with ray-tracing simulation.

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Analysis and modeling GPS NLOS effect in highly urbanized area

LoRa is the new communication technology under the Low Power Wide Area Network (LPWAN). It emphasizes on the long-range communication with the high receiving sensitivity ability which allows it to work under the noise interference or noise floor effectively. The range of communication has become the critical part on most of the IoT system, especially in Wi- Fi and Bluetooth-based IoT system. With the emergence of LoRa technology, further improvements to applications of the Internet of Things (IoT) can be realized. By using a single receiver in the LoRa network, it is able to handle many nodes at multiple locations within the area, unlike Wi-Fi-based system which needs to have many access points to increase the coverage area. The combination of LoRa and Wi-Fi technology reduces the cost of deployment of the IoT system. In this paper, the actual deployment of IoT system using LoRa technology with the combination of Wi-Fi technology. Besides, this paper presents the performance and the actual coverage area of the LoRa network in both the indoor and outdoor condition. For the indoor condition, a different set of LoRa parameter including Spreading Factor and Bandwidth is tested at different locations. As a result, the quality of the LoRa network does not only depend on the distance from the gateway but also the effect of path loss due to the structural element. Moreover, a single LoRa gateway located at the middle floor of the building is able to handle the LoRa end-devices at any location within the campus with Spreading Factor of 9. A better indoor coverage can be improved by adding an additional LoRa gateway in the campus. While for the outdoor condition, the maximum coverage area in the LoRa network for Spreading Factor of 12 is 330 meters with an indoor gateway deployment.

Internet of Things (IoT) using LoRa technology

As a non-renewable resource, mineral oil has been re-evaluated for its value and suitability as the coolant and insulator in transformers because of its environmental unfriendliness, fire hazards, and operating efficiency dissatisfaction. As a result, alternative dielectric liquids meeting the demands for more reliable, safer, and cleaner energy has been a critical need. Plant-based insulating fluids, non-toxic to the environment and ultimately biodegradable, have been flowing into the mainstream for achieving cost efficiencies, performance advantages, and enhancing safety. With more than 20 years of field experience, plant-based oils are now employed in more than 600,000 transformers worldwide, and have been established an enviable performance track record. Far from being just a mineral oil replacement, but more significantly, plant-based oils have filled a gap in some specific application scenarios where mineral oils fail to satisfy the fire-safety and environmental standards. This article reviews the research status of plant-based insulating fluids for transformer, including both challenges and outlook circa 2020. Plant-based oils have many inherent advantages over mineral oil, but also have numerous and unique challenges.

A critical review of plant-based insulating fluids for transformer: 30-year development

Sensors play important roles for autonomous driving. Localization is definitely a key one. Undoubtedly, global positioning system (GPS) sensor will provide absolute localization for almost all the future land vehicles. In terms of driverless car, 1.5 m of positioning accuracy is the minimum requirement, since the vehicle has to keep in a driving lane that usually wider than 3 m. However, the skyscrapers in highly-urbanized cities, such as Tokyo and Hong Kong, dramatically deteriorate GPS localization performance, leading more than 50 m of error. GPS signals are reflected at modern glassy buildings, which caused the notorious multipath effect. Fortunately, the number of navigation satellite is rapidly increasing in a global scale, since the rise of multi-global navigation satellite system. It provides an excellent opportunity for positioning algorithm developer of GPS sensor. More satellites in the sky imply more measurements to be received. Novelty, this paper proposes to take advantage of the fact that clean measurements (refers to line-of-sight measurement) are consistent and multipath measurements are inconsistent. Based on this consistency check, the faulty measurements can be detected and excluded to obtain better localization accuracy. Experimental results indicate that the proposed method can achieve less than 1-m lateral positioning error in middle urban canyons.

/pdf/multiple-faulty-gnss-measurement-exclusion-based-on-je7ac9wo99.pdf

Multiple Faulty GNSS Measurement Exclusion Based on Consistency Check in Urban Canyons

LoRa is being considered as one of the promising system for Low-Power-Wide-Area-Network (LPWAN) to support the growth of Internet of Things (IoT) applications. Designed to operate in the industrial, scientific and medical (ISM) bands, LoRa had been tested and evaluated mainly in Europe and US in the 868 MHz and 915 MHz modulation bands. Using chirp spread spectrum technology, LoRa is expected to be robust against degredation. This paper provides some early results in the performance of LoRa signal propagation of 433 MHz modulation in tropical foliage environments.

Impact of foliage on LoRa 433MHz propagation in tropical environment

Engine's behaviour on magnetite nanoparticles as additive and hydrogen addition of chicken fat methyl ester fuelled DICI engine: A dual fuel approach

Reliable GNSS positioning is a very challenging task in harsh urban environment. The main source of error is due to non-line-of-sight (NLOS) reception and multipath phenomena. The effect of assuming a direct path in a NLOS propagation environment leads to serious degradation in accuracy. Instead of discarding all measurements which are found to be in NLOS conditions, we propose to properly use these observations to improve the positioning accuracy and integrity in harsh environments. In this paper, we combine the sigma-e variance model with a mean jump (i.e. NLOS bias) to model the pseudorange (PR) errors. First, we use a 3D model of the environment to detect the NLOS state of reception and to predict the NLOS bias related to the excess delay phenomenon. For reliable positioning, we use a C/No-based variance adjustment for the LOS PRs, and we subtract the bias from the NLOS PRs during the trilateration step of position computation. The performance of the proposed scheme is assessed using real data and compared to a standard Kalman filter without predicted information from the 3D simulator.

https://hal-enac.archives-ouvertes.fr/hal-00943395/document

Reliable GNSS Positioning in Mixed LOS/NLOS Environments Using a 3D Model

The characterization of GNSS position errors in urban environments is an important issue for integrity monitoring and classification of receivers’ performance. However, these errors are not observable directly by the receiver, therefore RAIM methods use statistics based on the pseudorange residuals (i.e. observable errors). In this work, we focus on the modelling and analysis of navigation errors in the position-domain rather than individual range-domain errors that are difficult to model in urban environments due to multipath and non-line-of-sight (NLOS) signals. Using a trajectory of reference we compute the horizontal position errors (HPE) and its non-parametric distribution function given by the empirical Cumulative Distribution Function (CDF). According to the results, we observe that these errors have a heavy-tailed distribution, and then we propose to fit the empirical CDF with the CDF of the generalized Pareto distribution (GPD). We use an inflated version of the fitted Pareto model to overbound the CDF of the HPE for the calculation of Horizontal Protection Level (HPL), i.e., bounding the radial position errors.

https://hal-enac.archives-ouvertes.fr/hal-01160130/document

Characterization of GNSS Receiver Position Errors for User Integrity Monitoring in Urban Environments

Artificial neural networks which are inspired by the concept of the biological neurons are commonly used in many applications including in the field of weather forecasting. The neural networks approaches have provided an educated solution to aid in the decision-making process for weather forecasting as well as a viable means of the prediction of raindrop. This paper attempts to determine the suitability and the applicability of artificial neural networks for rain prediction based on temperature, pressure and humidity. Those conditions have been used as input data and solution was classified as percentage of raining. Multilayer perceptron network with two different learning algorithms have been studied. The multilayered perceptron trained using Lavenberg Marquardt algorithm has been proven to produce better results with accuracy percentage (99.75%) as compared to back propagation (94.57%).

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Khairol Amali Bin Ahmad

Papers

Impact of foliage on LoRa 433MHz propagation in tropical environment

Engine's behaviour on magnetite nanoparticles as additive and hydrogen addition of chicken fat methyl ester fuelled DICI engine: A dual fuel approach

Reliable GNSS Positioning in Mixed LOS/NLOS Environments Using a 3D Model

Characterization of GNSS Receiver Position Errors for User Integrity Monitoring in Urban Environments

Prediction of rainfall based on weather parameter using artificial neural network