scholarly journals A Tightly Coupled Positioning Solution for Land Vehicles in Urban Canyons

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xu Li ◽  
Rong Jiang ◽  
Xianghui Song ◽  
Bin Li
Keyword(s):  
Urban Areas ◽  
Unscented Kalman Filter ◽  
Inertial Sensor ◽  
Low Cost ◽  
Information Source ◽  
Satellite Measurement ◽  
3D Navigation ◽  
Filter Model ◽  
Land Vehicles ◽  
Tightly Coupled

The integration between Global Navigation Satellite System (GNSS) and on-board sensors is widely used for vehicle positioning. However, as the main information source in the integration, the positioning performance of single- or multiconstellation GNSSs is severely degraded in urban canyons due to the effects of Non-Line-Of-Sight (NLOS) and multipath propagations. How to mitigate such effects is vital to achieve accurate positioning performance in urban canyons. This paper proposes a tightly coupled positioning solution for land vehicles, fusing dual-constellation GNSSs with other low-cost complementary sensors. First, the nonlinear filter model is established based on a cost-effective reduced inertial sensor system with 3D navigation solution. Then, an adaptive fuzzy unscented Kalman filter (AF-UKF) algorithm is developed to achieve the global fusion. In the implementation of AF-UKF, the fuzzy calibration logic (FCL) is designed and introduced to adaptively adjust the dependence on each received satellite measurement to effectively mitigate the NLOS and multipath interferences in urban areas. Finally, the proposed solution is evaluated through experiments. The results validate the feasibility and effectiveness of the proposed solution.

scholarly journals Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2954 ◽  
Author(s):  
Ralf Ziebold ◽  
Daniel Medina ◽  
Michailas Romanovas ◽  
Christoph Lass ◽  
Stefan Gewies
Keyword(s):  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Primary Source ◽  
Global Navigation Satellite Systems ◽  
Measurement Unit ◽  
Doppler Velocity ◽  
Satellite Systems ◽  
Tightly Coupled ◽  
The Baltic

Currently Global Navigation Satellite Systems (GNSSs) are the primary source for the determination of absolute position, navigation, and time (PNT) for merchant vessel navigation. Nevertheless, the performance of GNSSs can strongly degrade due to space weather events, jamming, and spoofing. Especially the increasing availability and adoption of low cost jammers lead to the question of how a continuous provision of PNT data can be realized in the vicinity of these devices. In general, three possible solutions for that challenge can be seen: (i) a jamming-resistant GNSS receiver; (ii) the usage of a terrestrial backup system; or (iii) the integration of GNSS with other onboard navigation sensors such as a speed log, a gyrocompass, and inertial sensors (inertial measurement unit—IMU). The present paper focuses on the third option by augmenting a classical IMU/GNSS sensor fusion scheme with a Doppler velocity log. Although the benefits of integrated IMU/GNSS navigation system have been already demonstrated for marine applications, a performance evaluation of such a multi-sensor system under real jamming conditions on a vessel seems to be still missing. The paper evaluates both loosely and tightly coupled fusion strategies implemented using an unscented Kalman filter (UKF). The performance of the proposed scheme is evaluated using the civilian maritime jamming testbed in the Baltic Sea.

scholarly journals Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter

2021 ◽  
Author(s):  
Mahmoud Abd Rabbou ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Precise Point Positioning ◽  
Unscented Kalman Filter ◽  
Inertial Sensor ◽  
Measurement Models ◽  
Unscented Particle Filter ◽  
Tightly Coupled ◽  
Point Positioning

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available. Keywords: GPS; PPP; INS; EKF; UKF; UPF; tightly coupled

scholarly journals Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter

2021 ◽  
Author(s):  
Mahmoud Abd Rabbou ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Precise Point Positioning ◽  
Unscented Kalman Filter ◽  
Inertial Sensor ◽  
Measurement Models ◽  
Unscented Particle Filter ◽  
Tightly Coupled ◽  
Point Positioning

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available. Keywords: GPS; PPP; INS; EKF; UKF; UPF; tightly coupled

Deep Fusion of a Skewed Redundant Magnetic and Inertial Sensor for Heading State Estimation in a Saturated Indoor Environment

2021 ◽  
Vol 15 (03) ◽  
pp. 313-335
Author(s):  
Mojtaba Karimi ◽  
Edwin Babaians ◽  
Martin Oelsch ◽  
Eckehard Steinbach
Keyword(s):  
Magnetic Field ◽  
Indoor Environment ◽  
Inertial Sensor ◽  
Low Cost ◽  
Magnetic Sensors ◽  
Filter Model ◽  
Novel Approach ◽  
Essential Components ◽  
Heading Estimation

Robust attitude and heading estimation in an indoor environment with respect to a known reference are essential components for various robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using low-cost solid-state MEMS-based sensors. The precision of heading estimation on such a system is typically degraded due to the encountered drift from the gyro measurements and distortions of the Earth’s magnetic field sensing. This paper presents a novel approach for robust indoor heading estimation based on skewed redundant inertial and magnetic sensors. Recurrent Neural Network-based (RNN) fusion is used to perform robust heading estimation with the ability to compensate for the external magnetic field anomalies. We use our previously described correlation-based filter model for preprocessing the data and for empowering perturbation mitigation. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the saturated indoor environment and achieve a Root-Mean-Square Error of less than [Formula: see text] for long-term use.

scholarly journals Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Matthew Cossaboom ◽  
Jacques Georgy ◽  
Tashfeen Karamat ◽  
Aboelmagd Noureldin
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Map Matching ◽  
Loosely Coupled ◽  
The Road ◽  
Land Vehicles ◽  
Global Positioning ◽  
Reduced Inertial Sensor System

Owing to their complimentary characteristics, global positioning system (GPS) and inertial navigation system (INS) are integrated, traditionally through Kalman filter (KF), to obtain improved navigational solution. To reduce the overall cost of the system, microelectromechanical system- (MEMS-) based INS is utilized. One of the approaches is to reduce the number of low-cost inertial sensors, decreasing their error contribution which leads to a reduced inertial sensor system (RISS). This paper uses KF to integrate GPS and 3D RISS in a loosely coupled fashion to enhance navigational solution while further improvement is achieved by augmenting it with map matching (MM). The 3D RISS consists of only one gyroscope and two accelerometers along with the vehicle’s built-in odometer. MM limits the error growth during GPS outages by restricting the predicted positions to the road networks. The performance of proposed method is compared with KF-only 3D RISS/GPS integration to demonstrate the efficacy of the proposed technique.

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 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 Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

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