scholarly journals Tightly-Coupled Vehicle Positioning Method at Intersections Aided by UWB

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2867 ◽  
Author(s):  
Huaikun Gao ◽  
Xu Li
Keyword(s):  
Low Cost ◽  
Moving Average ◽  
Arima Model ◽  
Wide Band ◽  
Autonomous Driving ◽  
Fusion Algorithm ◽  
Positioning Strategy ◽  
Positioning Method ◽  
Tightly Coupled ◽  
Vehicle Positioning

Reliable and precise vehicle positioning is essential for most intelligent transportation applications as well as autonomous driving. Due to satellite signal blocking, it can be challenging to achieve continuous lane-level positioning in GPS-denied environments such as urban canyons and crossroads. In this paper, a positioning strategy utilizing ultra-wide band (UWB) and low-cost onboard sensors is proposed, aimed at tracking vehicles in typical urban scenarios (such as intersections). UWB tech offers the potential of achieving high ranging accuracy through its ability to resolve multipath and penetrate obstacles. However, not line of sight (NLOS) propagation still has a high occurrence in intricate urban intersections and may significantly deteriorate positioning accuracy. Hence, we present an autoregressive integrated moving average (ARIMA) model to first address the NLOS problem. Then, we propose a tightly-coupled multi sensor fusion algorithm, in which the fuzzy calibration logic (FCL) is designed and introduced to adaptively adjust the dependence on each received UWB measurement to effectively mitigate NLOS and multipath interferences. At last, the proposed strategy is evaluated through experiments. Ground test results validate that this low-cost approach has the potential to achieve accurate, reliable and continuous localization, regardless of the GPS working statue.

scholarly journals Roll Angular Rate Measurement for High Spinning Projectiles Based on Redundant Gyroscope System

Micromachines ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 940
Author(s):  
Jing Mi ◽  
Jie Li ◽  
Xi Zhang ◽  
Kaiqiang Feng ◽  
Chenjun Hu ◽  
...  
Keyword(s):  
Low Cost ◽  
Moving Average ◽  
Angular Rate ◽  
Arima Model ◽  
Micro Electro Mechanical System ◽  
Rate Measurement ◽  
Adaptive Kalman Filter ◽  
Autoregressive Integrated Moving Average ◽  
Gyroscope System ◽  
Technology Level

Precision-guided projectiles, which can significantly improve the accuracy and efficiency of fire strikes, are on the rise in current military engagements. The accurate measurement of roll angular rate is critical to guide a gun-launched projectile. However, Micro-Electro-Mechanical System (MEMS) gyroscope with low cost and large range cannot meet the requirement of high precision roll angular rate measurement due to the limitation by the current technology level. Aiming at the problem, the optimization-based angular rate estimation (OBARS) method specific for projectiles is proposed in this study. First, the output angular rate model of redundant gyroscope system based on the autoregressive integrated moving average (ARIMA) model is established, and then the conventional random error model is improved with the ARIMA model. After that, a Sage-Husa Adaptive Kalman Filter (SHAKF) algorithm that can suppress the time-varying process and measurement noise under the flight condition of the high dynamic of the projectile is designed for the fusion of dynamic data. Finally, simulations and experiments have been carried out to validate the performance of the method. The results demonstrate the proposed method can effectively improve the angular rate accuracy more than the related traditional methods for high spinning projectiles.

scholarly journals UAV UWB POSITIONING CLOSE TO BUILDING FACADES: A CASE STUDY

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 97-102
Author(s):  
M. P. Bouloukou ◽  
A. Masiero ◽  
A. Vettore ◽  
V. Gikas
Keyword(s):  
Wide Band ◽  
Satellite System ◽  
Ease Of Use ◽  
Positioning Strategy ◽  
Wide Range ◽  
Positioning Method ◽  
High Flexibility ◽  
Global Navigation Satellite ◽  
University Of Padua

Abstract. Nowadays, Unmanned Aerial Vehicles represent a very popular tool used in dramatically wide range of applications: indeed, their high flexibility, ease of use, and in certain cases quite affordable price make them a very attractive solutions in a number of applications, including surveying and mapping. Despite such a wide range of uses, their usage in automatic/autonomous mode is still restricted by the requirement of the availability of a reliable positioning and navigation system, which in practically all the commercial solutions is represented by the Global Navigation Satellite System (GNSS). Unfortunately, the availability and reliability of GNSS cannot be ensured in all the working conditions of interest. In particular, such condition may not hold downtown, close to high buildings. Since this can also be an operative condition of wide interest, this paper aims at investigating the use of an alternative positioning method that can be integrated with GNSS in order to compensate its unavailability. To be more specific, this paper investigates the positioning performance of an Ultra Wide-Band (UWB) system when an UWB rover is attached to a drone flying close to a building facade, whereas a set of UWB anchors are on the ground, close to the facade. The results obtained in the case study of a building of the University of Padua show that the UWB system positioning performance is quite good (quite less than 1 meter error for most of the time) up to approximately 15–20 meters of distance from the anchors. Close to the top of the building the error significantly increases when using an Extended Kalman filter (EKF) positioning approach, probably mostly due to the low UWB measurement success rate at such heights and to the poor geometric configuration of the UWB network. Nevertheless, a Gauss-Newton-based positioning strategy outperforms the EKF in such critical case, still ensuring errors at 1 meter level.

Model Statistika Prediksi Energi Surya Dengan Menggunakan Autoregresif Integrated Moving Average (ARIMA)

Jurnal MIPA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 181
Author(s):  
Imriani Moroki ◽  
Alfrets Septy Wauran
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Moving Average ◽  
Arima Model ◽  
Solar Irradiation ◽  
Energy Sources ◽  
Energy Potential ◽  
Good Design ◽  
Potential Sources ◽  
The Common

Energi terbarukan adalah salah satu masalah energi paling terkenal saat ini. Ada beberapa sumber potensial energi terbarukan. Salah satu energi terbarukan yang umum dan sederhana adalah energi matahari. Masalah besar ketersediaan energi saat ini adalah terbatasnya sumber energi konvensional seperti bahan bakar. Ini semua sumber energi memiliki banyak masalah karena memiliki jumlah energi yang terbatas. Penting untuk membuat model dan analisis berdasarkan ketersediaan sumber energi. Energi matahari adalah energi terbarukan yang paling disukai di negara-negara khatulistiwa saat ini. Tergantung pada produksi energi surya di daerah tertentu untuk memiliki desain dan analisis energi matahari yang baik. Untuk memiliki analisis yang baik tentang itu, dalam makalah ini kami membuat model prediksi energi surya berdasarkan data iradiasi matahari. Kami membuat model energi surya dan angin dengan menggunakan Metode Autoregresif Integrated Moving Average (ARIMA). Model ini diimplementasikan oleh R Studio yang kuat dari statistik. Sebagai hasil akhir, kami mendapatkan model statistik solar yang dibandingkan dengan data aktualRenewable energy is one of the most fomous issues of energy today. There are some renewable energy potential sources. One of the common n simple renewable energy is solar energy. The big problem of the availability of energy today is the limeted sources of conventional enery like fuel. This all energy sources have a lot of problem because it has a limited number of energy. It is important to make a model and analysis based on the availability of the energy sources. Solar energy is the most prefered renewable energy in equator countries today. It depends on the production of solar energy in certain area to have a good design and analysis of  the solar energy. To have a good analysis of it, in this paper we make a prediction model of solar energy based on the data of solar irradiation. We make the solar and wind enery model by using Autoregresif Integrated Moving Average (ARIMA) Method. This model is implemented by R Studio that is a powerfull of statistical. As the final result, we got the statistical model of solar comparing with the actual data

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Forecast of COVID-19 Cases in Nigeria: An implementation of an ARIMA model (Preprint)

2020 ◽  
Author(s):  
Sanyaolu Ameye ◽  
Michael Awoleye ◽  
Emmanuel Agogo ◽  
Ette Etuk
Keyword(s):  
Predictive Model ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
Series Data ◽  
Global Pandemic ◽  
First Case ◽  
Epidemiological Trend ◽  
Data Points

BACKGROUND The Coronavirus disease 2019 (COVID-2019) is a global pandemic and Nigeria is not left out in being affected. Though, the disease is just over three months since first case was identified in the country, we present a predictive model to forecast the number of cases expected to be seen in the country in the next 100 days. OBJECTIVE To implement a predictive model in forecasting the near future number of positive cases expected in the country following the present trend METHODS We performed an Auto Regressive Integrated Moving Average (ARIMA) model prediction on the epidemiological data obtained from Nigerian Centre for Disease Control to predict the epidemiological trend of the prevalence and incidence of COVID-2019. RESULTS There were 93 time series data points which lacked stationarity. From our ARIMA model, it is expected that the number of new cases declared per day will keep rising and towards the early September, 2020, Nigeria is expected to have well above sixty thousand confirmed cases. CONCLUSIONS We however believe that as we have more data points our model will be better fine-tuned.

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