scholarly journals An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments

2020 ◽  
Vol 10 (10) ◽  
pp. 3613
Author(s):  
Malek Karaim ◽  
Mohamed Tamazin ◽  
Aboelmagd Noureldin
Keyword(s):  
Urban Areas ◽  
Inertial Sensor ◽  
Tracking System ◽  
Integrated System ◽  
Measurement Unit ◽  
Gps Receiver ◽  
Signal Tracking ◽  
Signal Jamming ◽  
Reduced Inertial Sensor System ◽  
Lock In

The Global Positioning System (GPS) provides an accurate navigation solution in the open sky. However, in some environments such as urban areas or in the presence of signal jamming, GPS signals cannot be easily tracked since they could be harshly attenuated or entirely blocked. This often requires the GPS receiver to go into a signal re-acquisition phase for the corresponding satellite. To avoid the intensive computations necessary for the signal re-lock in a GPS receiver, a robust signal-tracking mechanism that can hold and/or rapidly re-lock on the signals and keep track of their dynamics becomes a necessity. This paper augments a vector-based GPS signal tracking system with a Reduced Inertial Sensor System (RISS) to produce a new ultra-tight GPS/INS integrated system that enhances receivers’ tracking robustness and sensitivity in challenging navigation environments. The introduced system is simple, efficient, reliable, yet inexpensive. To challenge the proposed method with real jamming conditions, real experiment work was conducted inside the Anechoic Chamber room at the Royal Military College of Canada (RMC). The Spirent GSS6700 signal simulator was used to generate GPS signals, and an INS Simulator is used for simulating the inertial measurement unit (IMU) to generate the corresponding trajectory raw data. The NEAT jammer, by NovAtel, was used to generate real jamming signals. Results show a good performance of the proposed method under real signal jamming conditions.

Integrated System to Evaluate Moored Ship Behavior

2017 ◽  
Author(s):  
E. Peña ◽  
A. Figuero ◽  
J. Sande ◽  
A. Guerra ◽  
J. D. Perez ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Tracking System ◽  
Integrated System ◽  
Maritime Transportation ◽  
Measurement Unit ◽  
Mooring Lines ◽  
Additional Measure ◽  
Determining Factors ◽  
Real Behavior ◽  
The University

The development of loading and unloading operations is an essential part of the logistical chain of maritime transportation. One of the determining factors in this process is the movement of moored ships, which has a notable influence over the efficiency of the operation. The implementation of systems to characterize their real behavior would provide an important tool for the management of these activities. In this context the Water and Environmental Engineering Group (GEAMA) at the University of A Coruña, Spain (UDC), through different projects executed in collaboration with the Port Authority of A Coruña (Spain), has developed an integrated system for monitoring the dynamic behavior of moored vessels. This system includes different devices which allow to characterize their six degrees of freedom and tensions in certain mooring lines. The analysis of motions is carried out using three combined technologies. Ship rotations (roll, pitch and yaw) are registered by means of an Inertial Measurement Unit (IMU) developed by the Research Group. The translations of heave and surge are analyzed with a visual tracking system using fixed cameras deployed near the location. Finally, the evaluation of sway motion and an additional measure of yaw are obtained using two laser-distance meters located over the dock. All devices integrated in the system were calibrated in laboratory at the R+D Centre CITTEC-UDC with several series of tests. Once the methodology was validated, the system has been employed in different projects carried out in Inner and Outer Ports of the city of A Coruña, with more than 30 monitored ships.

An Enhanced 3D Multi-Sensor Integrated Navigation System for Land-Vehicles

2014 ◽  
Vol 67 (4) ◽  
pp. 651-671 ◽  
Author(s):  
Mohamed Maher Atia ◽  
Tashfeen Karamat ◽  
Aboelmagd Noureldin
Keyword(s):  
Navigation System ◽  
Urban Areas ◽  
Inertial Sensor ◽  
Dynamic Error ◽  
Error Model ◽  
Integrated System ◽  
Accurate Estimation ◽  
Integration Scheme ◽  
Integrated Navigation ◽  
Signal Degradation

In urban areas, Global Positioning System (GPS) accuracy deteriorates due to signal degradation and multipath effects. To provide accurate and robust navigation in such GPS-denied environments, multi-sensor integrated navigation systems are developed. This paper introduces a 3D multi-sensor navigation system that integrates inertial sensors, odometry and GPS for land-vehicle navigation. A new error model is developed and an efficient loosely coupled closed-loop Kalman Filter (Extended KF or EKF) integration scheme is proposed. In this EKF-based integration scheme, the inertial/odometry navigation output is continuously corrected by EKF-estimated errors, which keeps the errors within acceptable linearization ranges which improves the prediction accuracy of the linearized dynamic error model. Consequently, the overall performance of the integrated system is improved. Real road experiments and comparison with earlier works have demonstrated a more reliable performance during GPS signal degradation and accurate estimation of inertial sensor errors (biases) have led to a more sustainable performance reliability during long GPS complete outages.

scholarly journals Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5031
Author(s):  
Humayun Khan ◽  
Adrian Clark ◽  
Graeme Woodward ◽  
Robert W. Lindeman
Keyword(s):  
Inertial Sensor ◽  
Vision System ◽  
Tracking System ◽  
Indoor Positioning ◽  
Fiducial Marker ◽  
Measurement Unit ◽  
Positioning System ◽  
Positioning Systems ◽  
Marker Tracking ◽  
Training Exercises

In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level.

scholarly journals Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773275 ◽  
Author(s):  
Francisco J Perez-Grau ◽  
Fernando Caballero ◽  
Antidio Viguria ◽  
Anibal Ollero
Keyword(s):  
Monte Carlo ◽  
Motion Tracking ◽  
Robot Navigation ◽  
Tracking System ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

Carrier Tracking Performance Comparison of Standards and the Square-Root Kalman Filter

2012 ◽  
Vol 239-240 ◽  
pp. 1188-1193
Author(s):  
Shao Jie Ni ◽  
Jing Pang ◽  
Xiao Mei Tang ◽  
Fei Xue Wang
Keyword(s):  
Kalman Filter ◽  
Engineering Design ◽  
Performance Comparison ◽  
Tracking Performance ◽  
Square Root ◽  
Signal Tracking ◽  
Tracking Method ◽  
High Carrier ◽  
Comparison Of Standards ◽  
Lock In

In order to solve the problem of loosing lock in weak GPS signal tracking, Kalman filter based carrier tracking method is presented.In this paper,two methods to track the GNSS carrier are compared,one is base on normal Kalman filter, another is based on square-root Kalman filter. The paper analyzes the under performance in the low carrier-to-noise ratio, and the expenditure of the actual project exists, but the high carrier to noise less discussed than the case will appear.The analyse and simulation result can be used to guide the engineering design of the GNSS receiver.

Evaluation of Location System Combining a GPS Receiver with Inertial Sensor

1991 ◽  
Author(s):  
Hiroaki Tsuji ◽  
Hiroyuki Maeda ◽  
Akihito Shibata ◽  
Fuminori Morisue
Keyword(s):  
Inertial Sensor ◽  
Gps Receiver ◽  
Location System

A new method to reduce the noise of the miniaturised inertial sensors disposed in redundant linear configurations

2013 ◽  
Vol 117 (1188) ◽  
pp. 111-132 ◽  
Author(s):  
T. L. Grigorie ◽  
R. M. Botez
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Frequency ◽  
Minimum Variance ◽  
Measurement Unit ◽  
Time Step ◽  
Accelerometer Sensor ◽  
Sensor Model ◽  
Input Acceleration

Abstract This paper presents a new adaptive algorithm for the statistical filtering of miniaturised inertial sensor noise. The algorithm uses the minimum variance method to perform a best estimate calculation of the accelerations or angular speeds on each of the three axes of an Inertial Measurement Unit (IMU) by using the information from some accelerometers and gyros arrays placed along the IMU axes. Also, the proposed algorithm allows the reduction of both components of the sensors’ noise (long term and short term) by using redundant linear configurations for the sensors dispositions. A numerical simulation is performed to illustrate how the algorithm works, using an accelerometer sensor model and a four-sensor array (unbiased and with different noise densities). Three cases of ideal input acceleration are considered: 1) a null signal; 2) a step signal with a no-null time step; and 3) a low frequency sinusoidal signal. To experimentally validate the proposed algorithm, some bench tests are performed. In this way, two sensors configurations are used: 1) one accelerometers array with four miniaturised sensors (n = 4); and 2) one accelerometers array with nine miniaturised sensors (n = 9). Each of the two configurations are tested for three cases of input accelerations: 0ms−1, 9·80655m/s2 and 9·80655m/s2.

scholarly journals A Low-Cost Motion Capture System for Small-Scale Wave Energy Device Tank Testing

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Francesco Paparella ◽  
Satja Sivcev ◽  
Daniel Toal ◽  
John V. Ringwood
Keyword(s):  
Wave Energy ◽  
Motion Tracking ◽  
Tracking System ◽  
Low Cost ◽  
Measurement Unit ◽  
Small Scale ◽  
Energy Device ◽  
Optical Motion ◽  
Optical Motion Tracking ◽  
Motion Tracking System

The measurement of the motion of a small-scale wave energy device during wave tank tests is important for the evaluation of its response to waves and the assessment of power production. Usually, the motion of a small-scale wave energy converter (WEC) is measured using an optical motion tracking system with high precision and sampling rate. However, the cost for an optical motion tracking system can be considerably high and, therefore, the overall cost for tank testing is increased. This paper proposes a low-cost capture system composed of an inertial measurement unit and ultrasound sensors. The measurements from the ultrasound sensors are combined optimally with the measurements from the inertial measurement unit through an extended Kalman filter (EKF) in order to obtain an accurate estimation of the motion of a WEC.

Is a Head-Worn Inertial Sensor a Valid Tool to Monitor Swimming?

2021 ◽  
pp. 1-4
Author(s):  
Stephanie J. Shell ◽  
Brad Clark ◽  
James R. Broatch ◽  
Katie Slattery ◽  
Shona L. Halson ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Video Analysis ◽  
Inertial Sensor ◽  
Absolute Error ◽  
Wearable Device ◽  
Measurement Unit ◽  
Fisher Exact Test ◽  
Mean Velocity ◽  
Video Footage ◽  
Stroke Type

Purpose: This study aimed to independently validate a wearable inertial sensor designed to monitor training and performance metrics in swimmers. Methods: A total of 4 male (21 [4] y, 1 national and 3 international) and 6 female (22 [3] y, 1 national and 5 international) swimmers completed 15 training sessions in an outdoor 50-m pool. Swimmers were fitted with a wearable device (TritonWear, 9-axis inertial measurement unit with triaxial accelerometer, gyroscope, and magnetometer), placed under the swim cap on top of the occipital protuberance. Video footage was captured for each session to establish criterion values. Absolute error, standardized effect, and Pearson correlation coefficient were used to determine the validity of the wearable device against video footage for total swim distance, total stroke count, mean stroke count, and mean velocity. A Fisher exact test was used to analyze the accuracy of stroke-type identification. Results: Total swim distance was underestimated by the device relative to video analysis. Absolute error was consistently higher for total and mean stroke count, and mean velocity, relative to video analysis. Across all sessions, the device incorrectly detected total time spent in backstroke, breaststroke, butterfly, and freestyle by 51% (15%). The device did not detect time spent in drill. Intraclass correlation coefficient results demonstrated excellent intrarater reliability between repeated measures across all swimming metrics. Conclusions: The wearable device investigated in this study does not accurately measure distance, stroke count, and velocity swimming metrics or detect stroke type. Its use as a training monitoring tool in swimming is limited.

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