Showing papers on "GPS/INS published in 2016"

Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation

Abstract: The need for indoor pedestrian navigators is quickly increasing in various applications over the last few years. However, indoor navigation still faces many challenges and practical issues, such as the need for special hardware designs and complicated infrastructure requirements. This paper originally proposes a pedestrian navigator based on tightly coupled (TC) integration of low-cost microelectromechanical systems (MEMS) sensors and WiFi for handheld devices. Two other approaches are proposed in this paper to enhance the navigation performance: 1) the use of MEMS solution based on pedestrian dead reckoning/inertial navigation system (PDR/INS) integration and 2) the use of motion constraints, such as non-holonomic constraints, zero velocity update, and zero angular rate update for the MEMS solution. There are two main contributions in this paper: 1) TC fusion of WiFi, INS, and PDR for pedestrian navigation using an extended Kalman filter and 2) better heading estimation using PDR and INS integration to remove the gyro noise that occurs when only vertical gyroscope is used. The performance of the proposed navigation algorithms has been extensively verified through field tests in indoor environments. The experiment results showed that the average root mean square position error of the proposed TC integration solution was 3.47 m in three trajectories, which is 0.01% of INS, 10.38% of PDR, 32.11% of the developed MEMS solution, and 64.58% of the loosely coupled integration. The proposed TC integrated navigation system can work well in the environment with sparse deployment of WiFi access points.

Why GPS makes distances bigger than they are

Abstract: Global navigation satellite systems such as the Global Positioning System GPS is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error C. We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.

A Tightly-Coupled GPS/INS/UWB Cooperative Positioning Sensors System Supported by V2I Communication

Abstract: This paper investigates a tightly-coupled Global Position System (GPS)/Ultra-Wideband (UWB)/Inertial Navigation System (INS) cooperative positioning scheme using a Robust Kalman Filter (RKF) supported by V2I communication. The scheme proposes a method that uses range measurements of UWB units transmitted among the terminals as augmentation inputs of the observations. The UWB range inputs are used to reform the GPS observation equations that consist of pseudo-range and Doppler measurements and the updated observation equation is processed in a tightly-coupled GPS/UWB/INS integrated positioning equation using an adaptive Robust Kalman Filter. The result of the trial conducted on the roof of the Nottingham Geospatial Institute (NGI) at the University of Nottingham shows that the integrated solution provides better accuracy and improves the availability of the system in GPS denied environments. RKF can eliminate the effects of gross errors. Additionally, the internal and external reliabilities of the system are enhanced when the UWB observables received from the moving terminals are involved in the positioning algorithm.

High-Precision Vehicle Navigation in Urban Environments Using an MEM's IMU and Single-Frequency GPS Receiver

Abstract: Many applications demand high-precision navigation in urban environments. Two frequency real-time kinematic (RTK) Global Positioning System (GPS) receivers are too expensive for low-cost or consumer-grade projects. As single-frequency GPS receivers are getting less expensive and more capable, more people are utilizing single-frequency RTK GPS techniques to achieve high accuracy in such applications. However, compared with dual-frequency receivers, it is much more difficult to resolve the integer ambiguity vector using single-frequency phase measurements and therefore more difficult to achieve reliable high-precision navigation. This paper presents a real-time sliding-window estimator that tightly integrates differential GPS and an inertial measurement unit to achieve reliable high-precision navigation performance in GPS-challenged urban environments using low-cost single-frequency GPS receivers. Moreover, this paper proposes a novel method to utilize the phase measurements, without resolving the integer ambiguity vector. Experimental results demonstrate real-time position estimation performance at the decimeter level. Furthermore, the novel use of phase measurements improves the robustness of the estimator to pseudorange multipath error.

Differential GNSS and Vision-Based Tracking to Improve Navigation Performance in Cooperative Multi-UAV Systems.

Abstract: Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information.

Optical Flow Sensor/INS/Magnetometer Integrated Navigation System for MAV in GPS-Denied Environment

Abstract: The drift of inertial navigation system (INS) will lead to large navigation error when a low-cost INS is used in microaerial vehicles (MAV). To overcome the above problem, an INS/optical flow/magnetometer integrated navigation scheme is proposed for GPS-denied environment in this paper. The scheme, which is based on extended Kalman filter, combines INS and optical flow information to estimate the velocity and position of MAV. The gyro, accelerator, and magnetometer information are fused together to estimate the MAV attitude when the MAV is at static state or uniformly moving state; and the gyro only is used to estimate the MAV attitude when the MAV is accelerating or decelerating. The MAV flight data is used to verify the proposed integrated navigation scheme, and the verification results show that the proposed scheme can effectively reduce the errors of navigation parameters and improve navigation precision.

Tracking drone orientation with multiple GPS receivers

Abstract: Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a fail-safe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters -- a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.

Navigation System Heading and Position Accuracy Improvement through GPS and INS Data Fusion

Abstract: Commercial navigation systems currently in use have reduced position and heading error but are usually quite expensive. It is proposed that extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) be used in the integration of a global positioning system (GPS) with an inertial navigation system (INS). GPS and INS individually exhibit large errors but they do complement each other by maximizing the advantage of each in calculating the heading angle and position through EKF and UKF. The proposed method was tested using low cost GPS, a cheap electronic compass (EC), and an inertial management unit (IMU) which provided accurate heading and position information, verifying the efficacy of the proposed algorithm.

Systematic Calibration for Ultra-High Accuracy Inertial Measurement Units

Abstract: An inertial navigation system (INS) has been widely used in challenging GPS environments. With the rapid development of modern physics, an atomic gyroscope will come into use in the near future with a predicted accuracy of 5 × 10−6°/h or better. However, existing calibration methods and devices can not satisfy the accuracy requirements of future ultra-high accuracy inertial sensors. In this paper, an improved calibration model is established by introducing gyro g-sensitivity errors, accelerometer cross-coupling errors and lever arm errors. A systematic calibration method is proposed based on a 51-state Kalman filter and smoother. Simulation results show that the proposed calibration method can realize the estimation of all the parameters using a common dual-axis turntable. Laboratory and sailing tests prove that the position accuracy in a five-day inertial navigation can be improved about 8% by the proposed calibration method. The accuracy can be improved at least 20% when the position accuracy of the atomic gyro INS can reach a level of 0.1 nautical miles/5 d. Compared with the existing calibration methods, the proposed method, with more error sources and high order small error parameters calibrated for ultra-high accuracy inertial measurement units (IMUs) using common turntables, has a great application potential in future atomic gyro INSs.

Application of Helmert Variance Component Based Adaptive Kalman Filter in Multi-GNSS PPP/INS Tightly Coupled Integration

Abstract: The integration of the Global Positioning System (GPS) and the Inertial Navigation System (INS) based on Real-time Kinematic (RTK) and Single Point Positioning (SPP) technology have been applied as a powerful approach in kinematic positioning and attitude determination. However, the accuracy of RTK and SPP based GPS/INS integration mode will degrade visibly along with the increasing user-base distance and the quality of pseudo-range. In order to overcome such weaknesses, the tightly coupled integration between GPS Precise Point Positioning (PPP) and INS was proposed recently. Because of the rapid development of the multi-constellation Global Navigation Satellite System (multi-GNSS), we introduce the multi-GNSS into the tightly coupled integration of PPP and INS in this paper. Meanwhile, in order to weaken the impacts of the GNSS observations with low quality and the inaccurate state model on the performance of the multi-GNSS PPP/INS tightly coupled integration, the Helmert variance component estimation based adaptive Kalman filter is employed in the algorithm implementation. Finally, a set of vehicle-borne GPS + BeiDou + GLONASS and Micro-Electro-Mechanical-Systems (MEMS) INS data is analyzed to evaluate the performance of such algorithm. The statistics indicate that the performance of the multi-GNSS PPP/INS tightly coupled integration can be enhanced significantly in terms of both position accuracy and convergence time.

Adaptive GPS/INS integration for relative navigation

Abstract: Relative navigation based on GPS receivers and inertial measurement units is required in many applications including formation flying, collision avoidance, cooperative positioning, and accident monitoring. Since sensors are mounted on different vehicles which are moving independently, sensor errors are more variable in relative navigation than in single-vehicle navigation due to different vehicle dynamics and signal environments. In order to improve the robustness against sensor error variability in relative navigation, we present an efficient adaptive GPS/INS integration method. In the proposed method, the covariances of GPS and inertial measurements are estimated separately by the innovations of two fundamentally different filters. One is the position-domain carrier-smoothed-code filter and the other is the velocity-aided Kalman filter. By the proposed two-filter adaptive estimation method, the covariance estimation of the two sensors can be isolated effectively since each filter estimates its own measurement noise. Simulation and experimental results demonstrate that the proposed method improves relative navigation accuracy by appropriate noise covariance estimation.

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

Abstract: The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

Improving MEMS-IMU/GPS integrated systems for land vehicle navigation applications

Abstract: The cost of inertial navigation systems (INS) has decreased significantly during recent years using micro-electro-mechanical system technology in production of inertial measurement units (IMUs). However, these IMUs do not provide the accuracy and stability of their classical mechanical counterparts which limit their applications. Hence, the error control of such systems is of the great importance which is achievable using external information via an appropriate fusion algorithm. Traditionally, this external information can be derived from global positioning system (GPS). But it is well known that GPS data availability and accuracy are vulnerable to signal-degrading circumstances and satellite visibility. We introduce a standalone attitude and heading reference system (AHRS) algorithm which employs the IMU and magnetometers data in an averaging manner. The averaging method is different from a simple smoothing procedure, since it takes the rotations of the platform (during the averaging interval) into account. The proposed AHRS solution is further used to provide additional attitude updates with adaptive noise variances for the integrated INS/GPS system during GPS outages via a refined loosely coupled filtering procedure, making the error growth well restrained. Functionality of the algorithm has been assessed via a field test. The results indicate that the proposed procedure outperforms the traditional integration scheme in different situations, while the latter almost loses track of the movements of the vehicle after 60-second GPS outages.

Kalman filter-based INS monitor to detect GNSS spoofers capable of tracking aircraft position

Abstract: In this work, we propose an innovation-based INS spoofing monitor that utilizes a tightly-coupled INS-GNSS integration in a Kalman filter. The performance of the monitor is evaluated when a spoofer tracks and estimates the aircraft position. To create the worst case spoofing conditions, we analytically derive a Kalman filter-based worst-case sequence of spoofed GNSS measurements. Utilizing this worst-case spoofing attack scenario during a Boeing 747 (B747) final approach, we prove that unless the spoofer's position-tracking devices have unrealistic accuracy and no-delay, the proposed INS monitor performance is highly effective in detecting spoofing attacks.

On Real Time Performance Evaluation of the Inertial Sensors for INS/GPS Integrated Systems

Abstract: In this paper, a new performance evaluation method is proposed for the inertial navigation system (INS) aided by the global positioning system (GPS). Inertial sensors in the INS are the key components for motion measurement. However, they are vulnerable to external disturbances, and performance degradation is likely to happen. Thus, a performance evaluation method is essential for real-time monitoring of motion measurement. On the other hand, conventional evaluation methods are usually based on the assumption that the sensor noises are Gaussian white, whereas it may be violated in practice. The statistics of sensor noise may be unknown yet norm bounded. Hence, a more general evaluation method to cover this case is needed. To achieve real-time performance evaluation of the inertial sensors subject to norm-bounded noises, a new evaluation function is proposed first. Then, we convert the evaluation issue into finding a minimum of the evaluation function, which can be viewed as an index to assess the sensor qualities. Next, for ease of online implementation, the evaluation function is reformulated and simplified by innovation projection. Moreover, an evaluation scheme is designed for the application of INS/GPS systems. Finally, two flight tests using the INS/GPS system for remote sensing are considered to validate the proposed method.

Sensor fusion of INS, odometer and GPS for robot localization

Abstract: This paper presents data fusion of three sensors Inertial Navigation System (INS), Global positioning systems (GPS) and odometer for determining the correct location of a differential drive mobile robot. The data from INS and odometer is combined using Kalman Filter (KF) based sensor fusion technique. The KF filtered signal and the GPS signal is fused by assigning weights. The proposed technique is tested in simulation. Mathematical models of the three sensors as well as the robot model is developed in MATLAB/Simulink environment to generate the data for simulation purpose. It has been demonstrated that with the proposed sensor fusion architecture exact geo-location of a differential drive wheeled robot can be determined to a greater degree of accuracy in an indoor or outdoor environment.

Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

Abstract: This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.

Federated ultra-tightly coupled GPS/INS integrated navigation system based on vector tracking for severe jamming environment

Abstract: Considering all integration methods currently available for global positioning system (GPS) and inertial navigation system (INS) integrated navigation system, ultra-tightly coupled (UTC) GPS/INS system is the best choice for accurate and robust navigation. However the performance of UTC GPS/INS system based on scalar tracking (ST) degrades in severe jamming environments. To solve this problem an innovative federated UTC GPS/INS system based on vector tracking (VT) is proposed. On the basis of UTC integration, three Kalman filters are developed to accomplish the goals of signal amplitude estimation, VT and integration navigation. By replacing the traditional ST loops with VT filter, the tracking ability is remarkably reinforced. More importantly, a new mathematical model of VT is proposed and the key innovation of this model is that it can provide adaptation ability to integration Kalman filter, so that the navigation performance can be improved under various jamming conditions. A simulation platform covering all procedures of GPS/INS integrated navigation is employed to verify the effectiveness of the proposed architecture. Tracking and navigation results show that the federated UTC GPS/INS integrated system based on VT improves both the tracking and navigation performance significantly under severe jamming environments compared with that of ST system.

A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations:

Abstract: Integrating celestial navigation system (CNS) and inertial navigation system (INS) is an effective way to realize autonomous navigation for hypersonic cruise vehicles. In multi-stars synchronous ob...

Matrix weighted multisensor data fusion for INS/GNSS/CNS integration

Abstract: Inertial navigation system (INS)/global navigation satellite system (GNSS)/ celestial navigation system (CNS) integration is a promising solution to improve the performance of navigation due to the complementary characteristics of INS, GNSS, and CNS. Nevertheless, the information fusion involved in INS/GNSS/CNS integration is still an open issue. This paper presents a matrix weighted multisensor data fusion methodology with two-level structure for INS/GNSS/CNS integrated navigation system. On the first level, GNSS and CNS are integrated with INS by two local filters respectively to obtain local optimal state estimations. On the second level, two different matrix weighted data fusion algorithms, one based on generic weighting matrices and the other based on diagonal weighting matrices, are developed to fuse the local state estimations for generating the global optimal state estimation. These two algorithms are derived in the sense of linear minimum variance, which provide unbiased fusion results no matter whether the local state estimations are mutually independent or not. Thus, they overcome the limitations of the federated Kalman filter by refraining from the use of the upper bound technique. Compared with the data fusion algorithm based on generic weighting matrices, the computational load involved in the one based on diagonal weighting matrices is significantly reduced, even though its accuracy is slightly lower due to the disregard of the coupled relationship between the components of the local state estimations. The effectiveness of the proposed matrix weighted multisensor data fusion methodology is verified through Monte Carlo simulations and practical experiments in comparison with the federated Kalman filter.

De-noising of GPS structural monitoring observation error using wavelet analysis

Abstract: In the process of the continuous monitoring of the structure's state properties such as static and dynamic responses using Global Positioning System (GPS), there are unavoidable errors in the observation data. These GPS errors and measurement noises have their disadvantages in the precise monitoring applications because these errors cover up the available signals that are needed. The current study aims to apply three methods, which are used widely to mitigate sensor observation errors. The three methods are based on wavelet analysis, namely principal component analysis method, wavelet compressed method, and the de-noised method. These methods are used to de-noise the GPS observation errors and to prove its performance using the GPS measurements which are collected from the short-time monitoring system designed for Mansoura Railway Bridge located in Egypt. The results have shown that GPS errors can effectively be removed, while the full-movement components of the structure can be extracted from the origina...

Fusion of GPS/INS/Odometer measurements for land vehicle navigation with GPS outage

Abstract: This paper deals with the hybridization of Global Positioning System (GPS) with an Inertial Navigation System (INS) and an Odometer, for land vehicle navigation, and more precisely with the calibration of inertial sensor errors. We focus here on the complementary characteristics of GPS and odometer to provide periodic corrections to Inertial Navigation System alternatively in different environmental conditions. In open sky, where GPS signals are available, GPS is integrated with INS. Meanwhile, in degraded GPS environments, where GPS is unreliable or unavailable, odometer replaces GPS to integrate with INS. This estimation problem is solved by a Kalman filter (KF), since it deals with non-linear problems and statistics.

Low cost GPS/IMU integrated accident detection and location system

Abstract: Emergency Rescue Service could save many lives if the accident location could be intimated automatically. Accident location is generally acquired from Global Positioning System (GPS). However, location cannot be determined during GPS outage. This paper proposes an accident detection system by determining the deceleration from the accelerometers of a low cost Micro Electro Mechanical Systems (MEMS) based Inertial Measurement Unit (IMU). The three axes accelerometers, gyroscopes and magnetometers data of the IMU were also programmed as Attitude Heading Reference System (AHRS) to determine the orientation and the position of the vehicle during a GPS outage. Error in positional information were corrected by a Kalman filter during the availability of GPS. A communication module was programmed to send the accident data to the base station in real time. The test result showed the correct functioning of the accident detection and location information.

Modification of the nonlinear kalman filter in a correction scheme of aircraft navigation systems

Abstract: An algorithm for correction of the navigation data of an aircraft that uses an inertial navigation system and the global positioning system (GPS) is considered. Estimation algorithms, namely, linear and nonlinear Kalman filters, are investigated. A method for modification of the nonlinear Kalman filter with the use of a genetic algorithm has been developed. To estimate the operability and accuracy of the modified nonlinear Kalman filter, simulation based on the data of a laboratory experiment conducted with the use of real navigation systems KIND34-059 and AIST-350 has been performed. The results of the laboratory experiments conducted with the use of real navigation systems have demonstrated high accuracy of data processing by the nonlinear Kalman filter modified by the genetic algorithm.

Computationally Efficient Carrier Integer Ambiguity Resolution in Multiepoch GPS/INS: A Common-Position-Shift Approach

Abstract: Integer ambiguity resolution is a challenging technical issue that exists in real-time kinematic (RTK) global positioning system (GPS) navigation. Once the integer vector is resolved, centimeter-level positioning estimation accuracy can be achieved using the GPS carrier phase measurements. Recently, a real-time sliding window Bayesian estimation approach to RTK GPS and inertial navigation was proposed to provide reliable centimeter accurate-state estimation, via integer ambiguity resolution utilizing a prior along with all inertial measurement unit and GPS measurements within the time window. One challenge to implementing that approach in practice is the high computation cost. This paper proposes a novel implementation approach with significantly lower computational requirements and includes a thorough theoretical analysis. The implementation results show that the proposed method resolves an integer vector identical to that of the original method and achieves state estimation with centimeter global positioning accuracy.

Three-stage filter for position and velocity estimation from long baseline measurements with unknown wave speed

Abstract: In this paper, an inertial navigation system (INS) aided by a long baseline (LBL) ranging system is presented and shown to have globally exponentially stable (GES) error dynamics. It uses a novel formulation for relating pseudo-range and pseudo-range-rate measurements linearly to position, velocity, and a multiplicative bias parameter, the latter parameter to correct for unknown wave speed, i.e. propagation speed in the medium.

Evaluation and comparison of performance analysis of indoor inertial navigation system based on foot mounted IMU

Abstract: This paper proposes to verify the performance & error analysis of pedestrian indoor navigation system using commercially available low-cost inertial sensors. This self-contained approach employs Euler for attitude representation, where the estimation problem is formulated as an Extended Kalman filter (EKF) for INS strap down mechanization equations. The algorithm outputs are the foot kinematic parameters, which include foot orientation, position, velocity, acceleration, and stance phase. The approach is based on a zero-velocity update (ZUPT), Zero angular rate update (ZARU), Heuristic heading drift reduction (HDR) algorithms. The main contribution of the paper is to compare and analyse the heading drift reduction algorithms on Kalman-based IEZ platform and estimating the return position error. Orientation is then determined from the foot's initialized from accelerometer sensor information. Finally, we evaluated using experiments, including both short distance walking with different patterns and long distance walking performed in indoor.

Adjustment and Assessment of the Measurements of Low and High Sampling Frequencies of GPS Real-Time Monitoring of Structural Movement

Abstract: Global Positioning System (GPS) structural health monitoring data collection is one of the important systems in structure movement monitoring. However, GPS measurement error and noise limit the application of such systems. Many attempts have been made to adjust GPS measurements and eliminate their errors. Comparing common nonlinear methods used in the adjustment of GPS positioning for the monitoring of structures is the main objective of this study. Nonlinear Adaptive-Recursive Least Square (RLS), extended Kalman filter (EKF), and wavelet principal component analysis (WPCA) are presented and applied to improve the quality of GPS time series observations. Two real monitoring observation systems for the Mansoura railway and long-span Yonghe bridges are utilized to examine suitable methods used to assess bridge behavior under different load conditions. From the analysis of the results, it is concluded that the wavelet principal component is the best method to smooth low and high GPS sampling frequency observations. The evaluation of the bridges reveals the ability of the GPS systems to detect the behavior and damage of structures in both the time and frequency domains.