scholarly journals GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Hassana Maigary Georges ◽  
Dong Wang ◽  
Zhu Xiao
Keyword(s):  
Urban Areas ◽  
Microelectromechanical Systems ◽  
Inertial Sensors ◽  
Low Cost ◽  
Robust Solution ◽  
Positioning Systems ◽  
Variational Bayesian ◽  
Kalman Smoother ◽  
Model Combining ◽  
Artificial Neural Network Ann

Among the inertial navigation system (INS) devices used in land vehicle navigation (LVN), low-cost microelectromechanical systems (MEMS) inertial sensors have received more interest for bridging global navigation satellites systems (GNSS) signal failures because of their price and portability. Kalman filter (KF) based GNSS/INS integration has been widely used to provide a robust solution to the navigation. However, its prediction model cannot give satisfactory results in the presence of colored and variational noise. In order to achieve reliable and accurate positional solution for LVN in urban areas surrounded by skyscrapers or under dense foliage and tunnels, a novel model combining variational Bayesian adaptive Kalman smoother (VB-ACKS) as an alternative of KF and ensemble regularized extreme learning machine (ERELM) for bridging global positioning systems outages is proposed. The ERELM is applied to reduce the fluctuating performance of GNSS during an outage. We show that a well-organized collection of predictors using ensemble learning yields a more accurate positional result when compared with conventional artificial neural network (ANN) predictors. Experimental results show that the performance of VB-ACKS is more robust compared with KF solution, and the prediction of ERELM contains the smallest error compared with other ANN solutions.

scholarly journals A Novel Vehicle Stationary Detection Utilizing Map Matching and IMU Sensors

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Md. Syedul Amin ◽  
Mamun Bin Ibne Reaz ◽  
Salwa Sheikh Nasir ◽  
Mohammad Arif Sobhan Bhuiyan ◽  
Mohd. Alauddin Mohd. Ali
Keyword(s):  
Stationary State ◽  
Navigation System ◽  
Urban Areas ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Measurement Unit ◽  
Map Matching ◽  
Mm Algorithm ◽  
Bias Drift ◽  
Navigation Information

Precise navigation is a vital need for many modern vehicular applications. The global positioning system (GPS) cannot provide continuous navigation information in urban areas. The widely used inertial navigation system (INS) can provide full vehicle state at high rates. However, the accuracy diverges quickly in low cost microelectromechanical systems (MEMS) based INS due to bias, drift, noise, and other errors. These errors can be corrected in a stationary state. But detecting stationary state is a challenging task. A novel stationary state detection technique from the variation of acceleration, heading, and pitch and roll of an attitude heading reference system (AHRS) built from the inertial measurement unit (IMU) sensors is proposed. Besides, the map matching (MM) algorithm detects the intersections where the vehicle is likely to stop. Combining these two results, the stationary state is detected with a smaller timing window of 3 s. A longer timing window of 5 s is used when the stationary state is detected only from the AHRS. The experimental results show that the stationary state is correctly identified and the position error is reduced to 90% and outperforms previously reported work. The proposed algorithm would help to reduce INS errors and enhance the performance of the navigation system.

GPS-based Land Vehicle Navigation System Assisted by a Low-Cost Gyro-Free INS Using Neural Network

2004 ◽  
Vol 57 (3) ◽  
pp. 417-428 ◽  
Author(s):  
Jau-Hsiung Wang ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Urban Areas ◽  
Low Cost ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Artificial Neural Network Ann ◽  
Signal Fading

GPS-based land vehicle navigation systems are subject to signal fading in urban areas and require aid from other enabling sensors. A low-cost gyro-free inertial navigation system (INS) without accumulated attitude errors and complicated initializations could be an effective solution to the problem. This paper investigates a Constrained Navigation Algorithm (CNA) and the Artificial Neural Network (ANN) technique to compensate velocity output from a gyro-free INS. The vehicle's heading will be calibrated by a full circle test so that the magnetometer's bias and scale factor error could be removed. Experiments with a vehicle driven over level terrain have been conducted to assess the performance of the compensated gyro-free INS solutions. The effect of the architecture of Neural Network on prediction performance has also been discussed as well as the applicability of the proposed solution to land vehicle navigation with GPS outages.

scholarly journals Inertial Sensors for Risk-Based Redundancy in Dynamic Positioning

2017 ◽  
Author(s):  
Torleiv H. Bryne ◽  
Robert H. Rogne ◽  
Thor I. Fossen ◽  
Tor A. Johansen
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensors ◽  
Low Cost ◽  
Single Point ◽  
Dynamic Positioning ◽  
Reference Systems ◽  
Positioning Systems ◽  
Sensor Structure ◽  
Inertial Measurements ◽  
The Cost

In this paper we present an alternative configuration of sensors, position and heading reference systems for dynamically positioned (DP) vessels. The approach uses a sensor structure based on low-cost inertial measurements units (IMUs), satisfying fault tolerance against single-point failures that is at the essence of the IMO guidelines for both DP class 2 and 3 vessels. Recent results have shown that dual-redundant position and heading reference systems are sufficient to prevent loss of position within some well-defined time horizons by exploiting sensor fusion of the reference systems and triple-redundant MEMS-based IMUs. These IMUs also function as Vertical Reference Units (VRUs), since vessel motions is obtained using the same IMU configuration and sensor fusion framework. In this proposition, the acceleration measurements provided by the IMUs make wind and other force sensors unnecessary, except possibly for an advisory role. The proposed framework has the potential to significantly reduce the cost of dynamic positioning systems without compromising safety.

scholarly journals A Low-Cost Visible Light Positioning System for Indoor Positioning

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5145 ◽  
Author(s):  
Juan Torres ◽  
Aitor Montes ◽  
Sandra Mendoza ◽  
Pedro Fernández ◽  
Juan Betancourt ◽  
...  
Keyword(s):  
Visible Light ◽  
Electromagnetic Interference ◽  
Urban Areas ◽  
Low Cost ◽  
Visible Light Communication ◽  
Indoor Positioning ◽  
Positioning System ◽  
Time To Market ◽  
Position Information ◽  
Positioning Systems

Currently, a high percentage of the world’s population lives in urban areas, and this proportion will increase in the coming decades. In this context, indoor positioning systems (IPSs) have been a topic of great interest for researchers. On the other hand, Visible Light Communication (VLC) systems have advantages over RF technologies; for instance, they do not need satellite signals or the absence of electromagnetic interference to achieve positioning. Nowadays, in the context of Indoor Positioning (IPS), Visible Light Positioning (VLP) systems have become a strong alternative to RF-based systems, allowing the reduction in costs and time to market. This paper shows a low cost VLP solution for indoor systems. This includes multiple programmable beacons and a receiver which can be plugged to a smartphone running a specific app. The position information will be quickly and securely available through the interchange between the receiver and any configurable LED-beacon which is strategically disposed in an area. The implementation is simple, inexpensive, and no direct communication with any data server is required.

scholarly journals Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 307
Author(s):  
Winfred Ingabire ◽  
Hadi Larijani ◽  
Ryan M. Gibson ◽  
Ayyaz-UI-Haq Qureshi
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Urban Areas ◽  
Large Scale ◽  
Low Cost ◽  
Wireless Sensor ◽  
Node Localization ◽  
Positioning Systems ◽  
Power Characteristics ◽  
Random Neural Networks

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

Tenants' Satisfaction in High Residential Building

2020 ◽  
Vol 17 (1) ◽  
pp. 41
Author(s):  
UMMU SHOLEHAH MOHD NOR
Keyword(s):  
Urban Areas ◽  
Residential Buildings ◽  
Low Cost ◽  
Residential Building ◽  
Real Estate Market ◽  
The Real Estate ◽  
Level Of Satisfaction ◽  
Considerable Impact ◽  
Residential Living ◽  
Medium Cost

High residential living in Malaysia has not been widely given a significant emphasises in literature despite its increasing scale and significance in the real estate market. The significance of high rise is mainly due the increasing rate of migration from rural to urban. It is estimated a total of 77.2 percent of the Malaysian population lived in urban areas in 2020. Approximately, 30 percent of this urban population lives in strata housing. These percentages are predicted to continue to increase in the future. The emergence of high residential building has been argued as confronting various problems which has considerable impact on this life style. Satisfaction is an important outcome of living in one’s dwelling, although it is not the only consideration. High residential building in Malaysia encountered numerous problems in term of management aspects, legislation aspects, and residents’ satisfaction. The purpose of this paper is to investigate the tenants’ satisfaction living in high residential buildings in Klang Valley. The questionnaires survey is conducted amongst 276 tenants at low cost and medium cost HRB using random sampling in HRB located at areas under jurisdiction Dewan Bandaraya Kuala Lumpur (DBKL), Majlis Bandaraya Subang Jaya (MBSJ), Majlis Bandaraya Shah Alam (MBSA), Majlis Bandaraya Subang Jaya (MBSJ), Majlis Perbandaran Selayang (MPS) and Majlis Perbandaran Ampang Jaya (MPAJ). The result from this study shows that tenant in medium cost residential building are more satisfied in term of facilities and management as compared to tenants in low cost residential building. Tenants also not disclosed to the existing act and procedure related to high residential building. In conclusion, this study suggested the Local Authority to emphasise the role of tenant. These recommendation hopefully will increase the level of satisfaction amongst the residents in HRB.

scholarly journals Predicting Excavation-Induced Tunnel Response by Process-Based Modelling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Linlong Mu ◽  
Jianhong Lin ◽  
Zhenhao Shi ◽  
Xingyu Kang
Keyword(s):  
Soil Properties ◽  
Urban Areas ◽  
Field Measurements ◽  
Inverse Modelling ◽  
Machine Learning Techniques ◽  
Interaction Patterns ◽  
Support Structures ◽  
Interaction Processes ◽  
Excavation Support ◽  
Artificial Neural Network Ann

Potential damages to existing tunnels represent a major concern for constructing deep excavations in urban areas. The uncertainty of subsurface conditions and the nonlinear interactions between multiple agents (e.g., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. This paper proposes an initiative to solve this problem by using process-based modelling, where information generated from the interaction processes between soils, structures, and excavation activities is utilized to gradually reduce uncertainty related to soil properties and to learn the interaction patterns through machine learning techniques. To illustrate such a concept, this paper presents a simple process-based model consisting of artificial neural network (ANN) module, inverse modelling module, and mechanistic module. The ANN module is trained to learn and recognize the patterns of the complex interactions between excavation deformations, its geometries and support structures, and soil properties. The inverse modelling module enables a gradual reduction of uncertainty associated with soil characterizations by accumulating field observations during the construction processes. Based on the inputs provided by the former two modules, the mechanistic module computes the response of tunnel. The effectiveness of the proposed process-based model is evaluated against high-fidelity numerical simulations and field measurements. These evaluations suggest that the strategy of combining artificial intelligence techniques with information generated during interaction processes can represent a promising approach to solve complex engineering problems in conventional industries.

scholarly journals A Proposal of a Motion Measurement System to Support Visually Impaired People in Rehabilitation Using Low-Cost Inertial Sensors

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 848
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Miguel Ángel Cuba ◽  
Sergio Sánchez Lara ◽  
José Javier Serrano Olmedo
Keyword(s):  
Visually Impaired ◽  
Inertial Sensors ◽  
Personal Autonomy ◽  
Low Cost ◽  
Step Length ◽  
Hand Position ◽  
Visually Impaired People ◽  
Gait Parameters ◽  
Impaired Person ◽  
Further Development

The rehabilitation of a visually impaired person (VIP) is a systematic process where the person is provided with tools that allow them to deal with the impairment to achieve personal autonomy and independence, such as training for the use of the long cane as a tool for orientation and mobility (O&M). This process must be trained personally by specialists, leading to a limitation of human, technological and structural resources in some regions, especially those with economical narrow circumstances. A system to obtain information about the motion of the long cane and the leg using low-cost inertial sensors was developed to provide an overview of quantitative parameters such as sweeping coverage and gait analysis, that are currently visually analyzed during rehabilitation. The system was tested with 10 blindfolded volunteers in laboratory conditions following constant contact, two points touch, and three points touch travel techniques. The results indicate that the quantification system is reliable for measuring grip rotation, safety zone, sweeping amplitude and hand position using orientation angles with an accuracy of around 97.62%. However, a new method or an improvement of hardware must be developed to improve gait parameters’ measurements, since the step length measurement presented a mean accuracy of 94.62%. The system requires further development to be used as an aid in the rehabilitation process of the VIP. Now, it is a simple and low-cost technological aid that has the potential to improve the current practice of O&M.

scholarly journals Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 179
Author(s):  
Said Munir ◽  
Martin Mayfield ◽  
Daniel Coca
Keyword(s):  
Data Fusion ◽  
Spatial Variability ◽  
Urban Areas ◽  
Road Traffic ◽  
Dispersion Model ◽  
Low Cost ◽  
Spatial Modelling ◽  
City Centre ◽  
Small Scale ◽  
The City

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

scholarly journals Development and Experimental Evaluation of a Low-Cost Cooperative UAV Localization Network Prototype

2018 ◽  
Vol 7 (4) ◽  
pp. 42 ◽  
Author(s):  
Salil Goel ◽  
Allison Kealy ◽  
Bharat Lohani
Keyword(s):  
Information Sharing ◽  
Inertial Sensors ◽  
Low Cost ◽  
Wide Band ◽  
Cooperative Networks ◽  
Global Navigation Satellite Systems ◽  
Cooperative Localization ◽  
Cooperative Network ◽  
Precise Positioning ◽  
Assisted Navigation

Precise localization is one of the key requirements in the deployment of UAVs (Unmanned Aerial Vehicles) for any application including precision mapping, surveillance, assisted navigation, search and rescue. The need for precise positioning is even more relevant with the increasing automation in UAVs and growing interest in commercial UAV applications such as transport and delivery. In the near future, the airspace is expected to be occupied with a large number of unmanned as well as manned aircraft, a majority of which are expected to be operating autonomously. This paper develops a new cooperative localization prototype that utilizes information sharing among UAVs and static anchor nodes for precise positioning of the UAVs. The UAVs are retrofitted with low-cost sensors including a camera, GPS receiver, UWB (Ultra Wide Band) radio and low-cost inertial sensors. The performance of the low-cost prototype is evaluated in real-world conditions in partially and obscured GNSS (Global Navigation Satellite Systems) environments. The performance is analyzed for both centralized and distributed cooperative network designs. It is demonstrated that the developed system is capable of achieving navigation grade (2–4 m) accuracy in partially GNSS denied environments, provided a consistent communication in the cooperative network is available. Furthermore, this paper provides experimental validation that information sharing is beneficial to improve positioning performance even in ideal GNSS environments. The experiments demonstrate that the major challenges for low-cost cooperative networks are consistent connectivity among UAV platforms and sensor synchronization.

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