scholarly journals Adaptive Estimation Algorithm for Correcting Low-Cost MEMS-SINS Errors of Unmanned Vehicles under the Conditions of Abnormal Measurements

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 623
Author(s):  
Lifei Zhang ◽  
Proletarsky Andrey Viktorovich ◽  
Maria Sergeevna Selezneva ◽  
Konstantin Avenirovich Neusypin
Keyword(s):  
Adaptive Estimation ◽  
Low Cost ◽  
Experimental Tests ◽  
Estimation Algorithm ◽  
Satellite System ◽  
Unmanned Vehicles ◽  
Estimation Accuracy ◽  
Accuracy Class ◽  
Unmanned Vehicle ◽  
The Impact

In this paper, a low-cost small-sized strap-down inertial navigation system (SINS)—Gyrolab GL-VG 109—is studied. When the system is installed on an unmanned vehicle and works in autonomous mode, it is difficult to determine the navigation parameters of the unmanned vehicle. Correcting the SINS information from the Global Navigation Satellite System (GNSS) can significantly increase the determination accuracy of the navigation parameters. However, this is only available when the GNSS signals are stable. A new adaptive estimation algorithm that can automatically detect, evaluate, and process the abnormal measurements is proposed in the present work. The determination of the navigation parameters can reach the third accuracy class using the proposed method. The effectiveness of the algorithm is verified by the mathematical simulation and the experimental tests (with a real SINS GL-VG 109), which are conducted in urban environments with a GNSS signal containing 15% and 40% abnormal measurements. The results show that the proposed method can significantly reduce the impact of abnormal measurements and improve the estimation accuracy.

Researching of the low-precision SINS research for an unmanned vehicle

2020 ◽  
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Adaptive Estimation ◽  
Inertial Navigation ◽  
Estimation Algorithm ◽  
Satellite Navigation ◽  
Accuracy Class ◽  
Unmanned Vehicle ◽  
Satellite Navigation System ◽  
Algorithmic Support

A small-sized inertial navigation system (SINS) Gyrolab GL VG 109 is researched. It is shown that this system has low accuracy; therefore it cannot be used to determine the parameters of an unmanned vehicle in an autonomous mode. Correction of the system from the satellite navigation system significantly increases the accuracy of determining the parameters of an unmanned vehicle, but only under conditions of stable signals from the satellite navigation system (SNS). The algorithmic support for the correction facility of the navigation system based on the scalar adaptive estimation algorithm and identification procedure is formed. The use of algorithmic correction of SINS from SNS using an estimation algorithm allows achieving an accuracy that corresponds to systems of the third accuracy class. Keywords inertial navigation system without platform; unmanned vehicle; correction; satellite navigation system; scalar estimation algorithm; scalar identification; analysis of accuracy

scholarly journals Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

2021 ◽  
Author(s):  
Dengqing Tang ◽  
Lincheng Shen ◽  
Xiaojiao Xiang ◽  
Han Zhou ◽  
Tianjiang Hu
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Vision System ◽  
Estimation Method ◽  
Satellite System ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
The Impact ◽  
Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

scholarly journals On-Body Characterization of Planar Differential Antennas for Multiple, Wide, and Narrow Bands

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Luigi Vallozzi ◽  
Domenico Pepe ◽  
Thijs Castel ◽  
Hendrik Rogier ◽  
Domenico Zito
Keyword(s):  
Human Body ◽  
Ad Hoc ◽  
Low Cost ◽  
Experimental Tests ◽  
Body Measurements ◽  
Antenna Performance ◽  
Sensing Applications ◽  
The Impact ◽  
Narrow Bands

This paper reports the results of the on-body experimental tests of a set of four planar differential antennas, originated by design variations of radiating elements with the same shape and characterized by the potential for covering wide and narrow bands. All the antenna designs have been implemented on low-cost FR4 substrate and characterized experimentally through on-body measurements. The results show the impact of the proximity to the human body on antenna performance and the opportunities in terms of potential coverage of wide and narrow bands for future ad hoc designs and implementations through wearable substrates targeting on-body and off-body communication and sensing applications.

scholarly journals Evaluation of the Integrity Risk for Precise Point Positioning

2021 ◽  
Vol 14 (1) ◽  
pp. 128
Author(s):  
Bing Xue ◽  
Yunbin Yuan ◽  
Han Wang ◽  
Haitao Wang
Keyword(s):  
Precise Point Positioning ◽  
Time Window ◽  
Experimental Tests ◽  
Satellite System ◽  
Worst Case ◽  
Zero Bias ◽  
Point Positioning ◽  
Integrity Risk ◽  
Global Navigation Satellite ◽  
The Impact

Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) is an attractive positioning technology due to its high precision and flexibility. However, the vulnerability of PPP brings a safety risk to its application in the field of life safety, which must be evaluated quantitatively to provide integrity for PPP users. Generally, PPP solutions are processed recursively based on the extended Kalman filter (EKF) estimator, utilizing both the previous and current measurements. Therefore, the integrity risk should be qualified considering the effects of all the potential observation faults in history. However, this will cause the calculation load to explode over time, which is impractical for long-time missions. This study used the innovations in a time window to detect the faults in the measurements, quantifying the integrity risk by traversing the fault modes in the window to maintain a stable computation cost. A non-zero bias was conservatively introduced to encapsulate the effect of the faults before the window. Coping with the multiple simultaneous faults, the worst-case integrity risk was calculated to overbound the real risk in the multiple fault modes. In order to verify the proposed method, simulation and experimental tests were carried out in this study. The results showed that the fixed and hold mode adopted for ambiguity resolution is critical to an integrity risk evaluation, which can improve the observation redundancy and remove the influence of the biased predicted ambiguities on the integrity risk. Increasing the length of the window can weaken the impact of the conservative assumption on the integrity risk due to the smoothing effect of the EKF estimator. In addition, improving the accuracy of observations can also reduce the integrity risk, which indicates that establishing a refined PPP random model can improve the integrity performance.

scholarly journals Performance Evaluation of Alternative Relative Orientation Procedures for UAV-based Imagery with Prior Flight Trajectory Information

2016 ◽  
Vol XLI-B3 ◽  
pp. 21-25 ◽  
Author(s):  
F. He ◽  
A. Habib
Keyword(s):  
Low Cost ◽  
Closed Form Solution ◽  
Experimental Tests ◽  
Form Solution ◽  
Relative Orientation ◽  
Conjugate Points ◽  
Flying Height ◽  
Reliable Estimate ◽  
Comparative Performance ◽  
The Impact

Thanks to recent advances at the hardware (e.g., emergence of reliable platforms at low cost) and software (e.g., automated identification of conjugate points in overlapping images) levels, UAV-based 3D reconstruction has been widely used in various applications. However, mitigating the impact of outliers in automatically matched points in UAV imagery, especially when dealing with scenes that has poor and/or repetitive texture, remains to be a challenging task. In spite of the fact that existing literature has already demonstrated that incorporating prior motion information can play an important role in increasing the reliability of the matching process, there is a lack of methodologies that are mainly suited for UAV imagery. Assuming the availability of prior information regarding the trajectory of a UAV-platform, this paper presents a two-point approach for reliable estimation of Relative Orientation Parameters (ROPs) of UAV-based images. This approach is based on the assumption that the UAV platform is moving at a constant flying height while maintaining the camera in a nadir-looking orientation. For this flight scenario, a closed-form solution that can be derived using a minimum of two pairs of conjugate points is established. In order to evaluate the performance of the proposed approach, experimental tests using real stereo-pairs acquired from different UAV platforms have been conducted. The derived results from the comparative performance analysis against the Nistér five-point approach demonstrate that the proposed two-point approach is capable of providing reliable estimate of the ROPs from UAV-based imagery in the presence of poor and/or repetitive texture with high percentage of matching outliers.

scholarly journals Robust adaptive fault estimation for multizone building VAV terminal units

2021 ◽  
Author(s):  
Mona Subramaniam ◽  
Tushar Jain ◽  
Joseph Yame
Keyword(s):  
Adaptive Estimation ◽  
Estimation Algorithm ◽  
Fault Estimation ◽  
Variable Air Volume ◽  
Energy Management Systems ◽  
Building Energy Management ◽  
Terminal Units ◽  
Robust Adaptive ◽  
Utility Cost ◽  
The Impact

In this paper, we propose a novel bilinear observer- based robust fault detection, isolation and adaptive fault estimation methodology to precisely estimate a class of actuator faults, namely bias in damper position and lock-in-place faults, in Variable-Air-Volume (VAV) terminal units of Heating Ventilation and Air-Conditioning (HVAC) systems. The proposed adaptive fault estimator is robust in the sense that the fault estimates are not affected by the unmeasured disturbance variable and that the effects of measurement noises on fault estimates are attenuated. The fault estimation algorithm with the integrated building control system improves occupants comfort and reduces the operation, maintenance, and utility cost, thereby reducing the impact on the environment. The effectiveness of the methodology for adaptive estimation of multiple or single VAV damper faults is successfully demonstrated through different simulation scenarios with SIMBAD (SIMulator of Building And Devices), which is being used in industries for testing and validation of building energy management systems.

scholarly journals Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4896 ◽  
Author(s):  
Mohamed Elsheikh ◽  
Walid Abdelfatah ◽  
Aboelmagd Nourledin ◽  
Umar Iqbal ◽  
Michael Korenberg
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Satellite System ◽  
Single Frequency ◽  
The Road ◽  
Land Vehicles ◽  
On The Road ◽  
Vehicle Automation ◽  
Global Navigation Satellite

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance.

scholarly journals Precise Point Positioning Algorithm for Pseudolite Combined with GNSS in a Constrained Observation Environment

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1120 ◽  
Author(s):  
Chuanzhen Sheng ◽  
Xingli Gan ◽  
Baoguo Yu ◽  
Jingkui Zhang
Keyword(s):  
High Precision ◽  
Global Navigation Satellite System ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Satellite System ◽  
Navigation Satellite ◽  
Global Navigation ◽  
Point Positioning ◽  
Global Navigation Satellite ◽  
Better Than

In urban canyon environments, Global Navigation Satellite System (GNSS) satellites are heavily obstructed with frequent rise and fall and severe multi-path errors induced by signal reflection, making it difficult to acquire precise, continuous, and reliable positioning information. To meet imperative demands for high-precision positioning of public users in complex environments, like urban canyons, and to solve the problems for GNSS/pseudolite positioning under these circumstances, the Global Navigation Satellite System (GNSS) Precision Point Positioning (PPP) algorithm combined with a pseudolite (PLS) was introduced. The former problems with the pseudolite PPP technique with distributed pseudo-satellites, which relies heavily on known points for initiation and prerequisite for previous high-precision time synchronization, were solved by means of a real-time equivalent clock error estimation algorithm, ambiguity fixing, and validation method. Experiments based on a low-cost receiver were performed, and the results show that in a weak obstructed environment with low-density building where the number of GNSS satellites was greater than seven, the accuracy of pseudolite/GNSS PPP with fixed ambiguity was better than 0.15 m; when there were less than four GNSS satellites in severely obstructed circumstances, it was impossible to obtain position by GNSS alone, but with the support of a pseudolite, the accuracy of PPP was able to be better than 0.3 m. Even without GNSS, the accuracy of PPP could be better than 0.5 m with only four pseudolites. The pseudolite/GNSS PPP algorithm presented in this paper can effectively improve availability with less GNSS or even without GNSS in constrained environments, like urban canyons in cities.

scholarly journals Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

2021 ◽  
Author(s):  
Dengqing Tang ◽  
Lincheng Shen ◽  
Xiaojiao Xiang ◽  
Han Zhou ◽  
Tianjiang Hu
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Vision System ◽  
Estimation Method ◽  
Satellite System ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
The Impact ◽  
Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

scholarly journals Performance Evaluation of Alternative Relative Orientation Procedures for UAV-based Imagery with Prior Flight Trajectory Information

2016 ◽  
Vol XLI-B3 ◽  
pp. 21-25
Author(s):  
F. He ◽  
A. Habib
Keyword(s):  
Low Cost ◽  
Closed Form Solution ◽  
Experimental Tests ◽  
Form Solution ◽  
Relative Orientation ◽  
Conjugate Points ◽  
Flying Height ◽  
Reliable Estimate ◽  
Comparative Performance ◽  
The Impact

Thanks to recent advances at the hardware (e.g., emergence of reliable platforms at low cost) and software (e.g., automated identification of conjugate points in overlapping images) levels, UAV-based 3D reconstruction has been widely used in various applications. However, mitigating the impact of outliers in automatically matched points in UAV imagery, especially when dealing with scenes that has poor and/or repetitive texture, remains to be a challenging task. In spite of the fact that existing literature has already demonstrated that incorporating prior motion information can play an important role in increasing the reliability of the matching process, there is a lack of methodologies that are mainly suited for UAV imagery. Assuming the availability of prior information regarding the trajectory of a UAV-platform, this paper presents a two-point approach for reliable estimation of Relative Orientation Parameters (ROPs) of UAV-based images. This approach is based on the assumption that the UAV platform is moving at a constant flying height while maintaining the camera in a nadir-looking orientation. For this flight scenario, a closed-form solution that can be derived using a minimum of two pairs of conjugate points is established. In order to evaluate the performance of the proposed approach, experimental tests using real stereo-pairs acquired from different UAV platforms have been conducted. The derived results from the comparative performance analysis against the Nistér five-point approach demonstrate that the proposed two-point approach is capable of providing reliable estimate of the ROPs from UAV-based imagery in the presence of poor and/or repetitive texture with high percentage of matching outliers.

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