An integrated GPS–accelerometer data processing technique for structural deformation monitoring

Global Positioning System (GPS) is being actively applied to measure static and dynamic displacement responses of large civil engineering structures under winds. However, multipath effects and low sampling frequencies affect the accuracy of GPS for displacement measurement. On the other hand, accelerometers cannot reliably measure static and low-frequency structural responses, but can accurately measure high-frequency structural responses. Therefore, this paper explores the possibility of integrating GPS-measured signals with accelerometer-measured signals to enhance the measurement accuracy of total (static plus dynamic) displacement response of a structure. Integrated data processing techniques using both empirical mode decomposition (EMD) and an adaptive filter are presented. A series of motion simulation table tests are then performed at a site using three GPS receivers, one accelerometer, and one motion simulation table that can simulate various types of motion defined by input wave time histories around a pre-defined static position. The proposed data processing techniques are applied to the recorded GPS and accelerometer data to find both static and dynamic displacements. These results are compared with the actual displacement motions generated by the motion simulation table. The comparative results demonstrate that the proposed technique can significantly enhance the measurement accuracy of the total displacement of a structure.     

Using GPS for monitoring tall-building response to wind loading: filtering of abrupt changes and low-frequency noise,variography and spectral analysis of displacements

We compare two methods for monitoring the dynamic response of tall buildings to wind loading, using data from a 280-m-high building in Singapore. The first method is based on accelerometer measurements. The second method is based on the Global Positioning System (GPS) technology. The GPS can in principle detect absolute displacements with lower frequencies than the accelerometers, but the GPS positioning signal is usually very noisy. We propose a systematic procedure for modeling the stochastic and systematic components of the GPS displacement time series and for extracting the weak structural response from the dominant noise. The spectrum of the building response obtained from the filtered GPS data exhibits a dominant peak at 0.19 Hz. The frequency of the peak coincides with that obtained from the analysis of the accelerometer data. The proposed analysis of the GPS signal provides a method for cross-validating the GPS and accelerometer measurements, and shows that “educated” filtering of the GPS signal can reveal essential features of the building’s response to wind loading.     

Low-cost attitude estimation with MIMU and two-antenna GPS for Satcom-on-the-move

We detail a low-cost method to estimate vehicle attitude for Satcom-on-the-move (SOTM) using micro inertial measurement unit and two-antenna global positioning system (GPS). The attitude is calculated by integrating gyros, while accelerometers and GPS are used as an aiding system to calibrate the gyro biases. In order to accurately eliminate the accelerometer errors induced by maneuvering accelerations, a sideslip compensation is proposed to calibrate the acceleration estimation derived from the GPS-measured velocity. An adaptive unscented Kalman filter is then developed to deal with the failure of the two-antenna GPS to provide the yaw and sideslip angles. When the yaw angle is unavailable and the vehicle is in straight motion, the yaw angle observation is substituted by velocity heading. During turning, the yaw angle observation is turned off and the yaw angle estimates are obtained from integrating gyros only. Meanwhile, the sideslip angle is estimated by differencing the yaw angle estimate and the velocity heading. The performance of the proposed method is demonstrated with driving tests, suggesting that this technique is a viable candidate for low-cost SOTM.     

GPS单点测速的误差分析及精度评价

首先从理论和实测数据模拟两方面分析了SA取消后各类误差源对GPS测速的影响,推导并计算了GPS单点测速可能达到的精度水平。然后用静态数据模拟动态测速试验和实测动态数据测速与同步高精度惯导测速的动态试验进行验证。结果表明,采用载波相位导出的多普勒观测值使用静态数据模拟动态测速,其精度可以达到mm/s级;用接收机输出的多普勒观测值进行测速时,其精度为cm/s级。在动态测速试验中,GPS单点测速方法（即多普勒观测值测速与导出多普勒观测值测速）间的符合精度达到cm/s级,与高精度的惯导测速结果的符合精度为dm/s级,而且和运动载体的动态条件（如加速度和加速度变化率的大小）具有很强的相关性。     

GOCE卫星重力梯度校准与无阻尼控制效果分析

GOCE卫星是首颗搭载高精度梯度仪,通过加速度计差分测量确定地球重力场的现代重力卫星。该卫星设计为无阻尼飞行状态（沿轨方向）,加速度计并未安置在卫星质心,这些特点使得GOCE与标准的卫星跟踪卫星重力测量模式有着显著的区别。本文首先指出GOCE任务中普通模式加速度校准存在不严密性问题,并提出了分别校准6个加速度计,分离偏差参数的方案。利用GOCE任务期内的几何法精密轨道,采用动力法完成校准,并分析了无阻尼控制的效果,发现：①虽然GOCE所在轨道高度的中性大气密度较GRACE高两到三个量级,但GOCE卫星在沿轨方向的残余非保守力比GRACE卫星的对应分量小一个量级,充分显示了无阻尼控制系统的补偿效果;②通过精密轨道内插的轨道速度与动力法轨道速度的比较可以得出,卫星无阻尼控制系统对GOCE卫星速度的显著影响;③计算了GOCE卫星所受的非保守力。获得了GOCE任务期间的加速度计校准参数,并讨论了利用其辅助重力梯度仪数据预处理的可能方法。     

GPS/DR组合导航中一种新的数据融合算法

在分析以往多传感器组合观测数据融合算法基础上,提出了一种新的数据融合算法。仿真计算结果证实了该方法的可行性。     

电视塔中心线垂直度及塔身扭转GPS监测方法

介绍了利用GPS静态观测事后处理分析的方法监测四川广播电视塔中心线的垂线度及塔身扭转的有关问题。内容包括基准网的建立与稳定性分析；塔心及塔轴线基准数据的确定；塔中心线的垂直度及塔身扭转的监测方法及数据处理。对实测数据的分析研究表明，采用GPS监测方法取得的结果是令人满意的。     

A methodology for the performance evaluation of low-cost accelerometer and magnetometer sensors in geomatics applications

This paper presents a methodology and its software implementation for the performance evaluation of low-cost accelerometer and magnetometer sensors for use in geomatics applications. A known mathematical calibration model has been adopted. The method was completed with statistical methodologies for adjusting observations and has been extended to calculate accuracies for the attitude, heading, and tilt angles estimation that are of interest to geomatics applications. The evaluation method consists of two stages. First, the evaluation method reviews the total magnitude of acceleration or the strength of the magnetic field. Second, the evaluation is more detailed and concerns the determination of mathematical parameters that describe both accelerometer and magnetometer working model. A software tool that implements the evaluation model has been developed and is applied both in accelerometer and magnetometer measurement data-sets acquired from a low-cost sensor system.     

GOCE gradiometer: estimation of biases and scale factors of all six individual accelerometers by precise orbit determination

A method has been implemented and tested for estimating bias and scale factor parameters for all six individual accelerometers that will fly on-board of GOCE and together form the so-called gradiometer. The method is based on inclusion of the individual accelerometer observations in precise orbit determinations, opposed to the baseline method where so-called common-mode accelerometer observations are used. The method was tested using simulated data from a detailed GOCE system simulator. It was found that the observations taken by individual accelerometers need to be corrected for (1) local satellite gravity gradient (SGG), and (2) rotational terms caused by centrifugal and angular accelerations, due to the fact that they are not located in the satellite’s center of mass. For these corrections, use is made of a reference gravity field model. In addition, the rotational terms are derived from on-board star tracker observations. With a perfect a priori gravity field model and with the estimation of not only accelerometer biases but also accelerometer drifts, scale factors can be determined with an accuracy and stability better than 0.01 for two of the three axes of each accelerometer, the exception being the axis pointing along the long axis of the satellite (more or less coinciding with the flight direction) for which the scale factor estimates are unreliable. This axis coincides with the axis of drag-free control, which results in a small variance of the signal to be calibrated and thus an inaccurate determination of its scale factor in the presence of relatively large (colored) accelerometer observation errors. In the presence of gravity field model errors, it was found that still an accuracy and stability of about 0.015 can be obtained for the accelerometer scale factors by simultaneously estimating empirical accelerations.     

Sensing strides using EMG signal for pedestrian navigation

Navigation applications and location-based services are now becoming standard features in smart phones. However, locating a mobile user anytime anywhere is still a challenging task, especially in GNSS (Global Navigation Satellite System) degraded and denied environments, such as urban canyons and indoor environments. To approach a seamless indoor/outdoor positioning solution, Micro-Electro-Mechanical System sensors such as accelerometers, digital compasses, gyros and pressure sensors are being adopted as augmentation technologies for a GNSS receiver. However, the GNSS degraded and denied environments are typically contaminated with significant sources of error, which disturb the measurements of these sensors. We introduce a new sensor, the electromyography (EMG) sensor, for stride detection and stride length estimation and apply these measurements, together with a digital compass, to a simple pedestrian dead reckoning (PDR) solution. Unlike the accelerometer, which senses the earth gravity field and the kinematic acceleration of the sensor, the EMG sensor senses action potentials generated by the muscle contractions of the human body. The EMG signal is independent of the ambient environment and its disturbance sources. Therefore, it is a good alternative sensor for stride detection and stride length estimation. For evaluating the performance of the EMG sensor, we carried out several field tests at a sports field and along a pedestrian path. The test results demonstrated that the accuracy of stride detection was better than 99.5%, the errors of the EMG-derived travelled distances were less than 1.5%, and the performance of the corresponding PDR solutions was comparable to that of the global positioning system solutions.     

BDS/GPS组合精密单点定位精度分析与评价

精密单点定位（precise point positioning,PPP）已经广泛应用于许多领域,如测绘、交通、导航、地震监测等。近些年来,随着卫星数量的增多,多系统组合呈现越来越明显的趋势。利用全球MGEX（Multi-GNSS Experiment）网数据研究了BDS（BeiDou navigation satellite system）/GPS（global positioning system）组合精密单点定位技术,并与BDS单系统和GPS单系统进行了对比。结果表明,在静态定位中,BDS PPP在E、N、U方向的均方根误差分别为4.35 cm、3.01 cm、6.40 cm;GPS PPP在E、N、U方向的均方根误差分别为1.21 cm、0.48 cm、1.79 cm;BDS/GPS组合PPP在E、N、U方向的均方根误差分别为1.21 cm、0.50 cm、1.87 cm。在动态定位中,BDS PPP外符合精度水平方向优于10 cm,高程方向优于15 cm;GPS PPP和BDS/GPS组合PPP的外符合精度水平方向均优于5 cm,高程方向均优于8 cm。另外,无论是在静态还是动态的PPP中,组合系统相对于单系统,能大大缩短收敛时间,减少定位结果抖动,尤其是相对于BDS PPP来说,优势更为明显。     

融合GNSS和加速度计监测数据的超高建筑动态特性分析

GNSS和加速度计是目前动态监测超高建筑环境载荷变形的主要手段。GNSS具有无需通视、可直接获取三维位移等优点,但受精度和采样率的限制,其对微变形及高频振动信息不敏感;而加速度计具有高精度和高采样率等优点,但无法监测超高建筑低频的似静态变形。为充分发挥这两种传感器的各自优势,提出利用多速率Kalman滤波和RTS平滑方法对超高建筑GNSS和加速度计监测数据进行融合处理。试验结果表明,与单一的GNSS监测技术相比,该方法有利于削弱GNSS高频噪声的影响,提高位移数据的采样率,可有效识别超高建筑的低频和高频振动频率,提高对微变形振动的监测能力;与单一的加速度计监测技术相比,该方法可以准确监测超高建筑的低频变形信息,具有良好的工程应用价值。     

Impact of GPS satellite and pseudolite geometry on structural deformation monitoring: analytical and empirical studies

Global positioning systems (GPS) have in recent years been increasingly used to monitor the deformations of large structures, particularly the deflections of long suspension bridges. When appropriately employed, and with the presence of a strong satellite geometry, GPS can supply timely and accurate structural deformation information. However, the three-dimensional (3-D) positioning accuracies in a local coordinate system are uneven. For instance, the vertical component of 3-D coordinates is less accurate than the horizontal component. In addition, GPS satellite availability tends to be a function of the latitude of the observation site and its surrounding obstructions. As a consequence, the accuracy of the north–south component is typically worse than that of the east–west component in mid-latitude areas (>45), and in some of the worst situations the horizontal positioning accuracy could even degrade to the same level as that of the vertical component. With such measurements it might not be possible to correctly interpret the real structural deformations. Furthermore, an insufficient number of satellites, caused by signal obstruction, could make it impossible to use GPS alone for kinematic positioning, even when integrated with other sensors such as triaxial accelerometers. With the aim of improving 3-D positioning accuracies for the monitoring of structural deflections, especially in vertical and northern directions, the optimal location selection of an array of ground-based pseudolites to augment GPS satellite geometry using an analytical simulation technique proposed by the authors is considered. Achievable 3-D positioning accuracies are estimated by simulating a real bridge deformation scenario using augmented transmitter geometry and compared with actual positioning accuracies calculated from the measurements gathered from a bridge trial. The results show that with an augmented satellite geometry and multipath mitigation it is possible to achieve uniform 3-D positioning accuracies of a few millimetres.     

Monitoring the dynamic characteristics of tall buildings by GPS technique

Dynamic characteristics of large structures,such as tall buildings,long-span suspension,cable-stayed bridges and tall chimneys,are key to assess their drift and stress conditions.The dynamic characteristics of large structures are difficult to measure directly under the condition of earthquakes or strong winds using traditional techniques such as laser collimator,total station and accelerometers.Therefore there is a great need for developing new method or technique for this purpose.Recent advances in Global Positioning System (GPS) technology provide a great opportunity to monitor long-period changes of structures reliably.GPS receivers capable to gauge the motion at the centimeter or sub-centimeter level with sampling frequency 10Hz or even 20 Hz are now available from several manufacturers.To the authors' knowledge,the capability of identifying dynamic characteristics from GPS observations has not been widely verified.For the feasibility study on using kinematic GPS technology to identify the dynamic characteristics of tall buildings,some experiments were conducted in a simulative environment.This paper discusses in detail the experiment device,and the ways through them GPS data are recorded,processed and analyzed.With post-processing version of NovAtel's Softsurv software and auto-regressive (AR) spectral analysis method,relative displacements and corresponding vibrating frequencies have been derived from GPS observations.The results indicate that the dynamic characteristics can be identified accurately by kinematic GPS technology.     

Using multiple portable/wearable devices for enhanced misalignment estimation in portable navigation

Global navigation satellite system (GNSS), such as global positioning system (GPS), has been widely used for vehicular and outdoor navigation. Accuracy is one, among many, of the advantages of using GNSS in the open sky. However, GNSS finds difficulty in achieving similar results in portable navigation, where users spend most of their time indoors or in urban canyons, places where GNSS signals suffer from multipath error or signal blockage. One of the most common solutions for providing location services in such challenging environments is integrating GNSS with inertial sensors, such as accelerometers and gyroscopes. However, the arbitrary orientation of the portable device can present a more difficult challenge when using inertial sensors for portable navigation. In order to obtain a navigation solution using inertial sensors, an accurate heading estimation is required. Resolving the heading misalignment angle between the portable navigation device and the moving platform, such as using the device while walking or in a vehicle while driving, is critical to obtaining an accurate heading estimation. We present a solution for resolving the misalignment between the portable device and the moving platform, which exploits multiple portable devices like smartphones or tablets and/or smart wearable devices such as smart watches, smart glasses, and/or smart fitness and activity trackers/monitors. Several real field test experiments using portable devices were conducted to examine the performance of the proposed method. Results show how a portable navigation solution can be improved by further enhancing misalignment estimation.     

Exploring the possibilities for star-tracker assisted calibration of the six individual GOCE accelerometers

A method has been developed and tested for estimating calibration parameters for the six accelerometers on board the Gravity field and steady-state Ocean Circulation Explorer (GOCE) from star tracker observations. These six accelerometers are part of the gradiometer, which is the prime instrument on board GOCE. It will be shown that by taking appropriate combinations of observations collected by the accelerometers, by modeling acceleration terms caused by gravity gradients from an a priori low-degree spherical harmonic expansion, and by modeling rotational acceleration terms derived from star-tracker observations, scale factors of each of the accelerometers can be estimated for each axis. Simulated observations from a so-called end-to-end simulator were used to test the method. This end-to-end simulator includes a detailed model of the GOCE satellite, its instruments and instrument errors, and its environment. Results of the tests indicate that scale factors of all six accelerometers can be determined with an accuracy of around 0.01 for all components on a daily basis.     

Influence of clock jump on the velocity and acceleration estimation with a single GPS receiver based on carrier-phase-derived Doppler

Since the Selective Availability was turned off, the velocity and acceleration can be determined accurately with a single GPS receiver using raw Doppler measurements. The carrier-phase-derived Doppler measurements are normally used to determine velocity and acceleration when there is no direct output of the raw Doppler observations in GPS receivers. Due to GPS receiver clock drifts, however, a GPS receiver clock jump occurs when the GPS receiver clock resets itself (typically with 1 ms increment/decrement) to synchronize with the GPS time. The clock jump affects the corresponding relationship between measurements and their time tags, which results in non-equidistant measurement sampling in time or incorrect time tags. This in turn affects velocity and acceleration determined for a GPS receiver by the conventional method which needs equidistant carrier phases to construct the derived Doppler measurements. To overcome this problem, an improved method that takes into account, GPS receiver clock jumps are devised to generate non-equidistant-derived Doppler observations based on non-equidistant carrier phases. Test results for static and kinematic receivers, which are obtained by using the conventional method without reconstructing the equidistant continuous carrier phases, show that receiver velocity and acceleration suffered significantly from clock jumps. An airborne kinematic experiment shows that the greatest impact on velocity and acceleration reaches up to 0.2 m/s, 0.1 m/s² for the horizontal component and 0.5 m/s, 0.25 m/s² for the vertical component. Therefore, it can be demonstrated that velocity and acceleration measurements by using a standalone GPS receiver can be immune to the influence of GPS receiver clock jumps with the proposed method.     

Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter

Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.     

动力法校准GRACE星载加速度计

研究了GRACE星载加速度计的动力法校准。联合精密轨道、星间距离变率同时估计重力场模型参数和加速度计校准参数,获得了SRF(satellite reference frame)下的比例系数和偏差参数时间序列,100阶重力场模型的大地水准面累积误差为4cm,在相应波段上优于CSR(center for space research)同时段的月重力场模型精度。以上述整体解算的结果为参照,对固定重力场模型参数的校准方案进行了检验,发现SRF框架下的非保守力差值最高可达10-8 m·s-2量级,认为固定重力场模型的方法难以充分发挥GRACE加速度计的测量能力。本文研究结果可为后续的大规模卫星重力测量数据处理提供科学的依据,也能为开展相关科学应用提供可靠的非保守力数据。