共查询到20条相似文献，搜索用时 31 毫秒
1.
室内定位方法的可行性及准确性成为位置信息服务体系的研究热点,针对移动智能终端普及率的提高,提出了基于内置微惯性传感器的定位理念。通过采用Allan方差分析惯性器件误差参数,并基于频谱分析采用滤波降噪方案提高器件输出信噪比,结合人体运动特征准确判定步态,为零速修正算法的实现提供依据,通过建立局部区域的地磁信息数据库,实现磁匹配零速修正卡尔曼组合误差修正。实验结果验证了基于微惯性技术的室内定位方法的可行性。 相似文献
2.
CNS+GNSS+INS船载高精度实时定位定姿算法改进研究 总被引：2，自引：1，他引：1
天文导航(CNS)、卫星导航(GNSS)和惯性导航(INS) 3种系统组合可提供高精度的定位定姿结果。实际工程中因INS长时间误差累积,以及系统硬件传输存在不可忽略的时间延迟,导致INS提供给CNS的预报粗姿态误差较大,恶劣海况下难以保障快速搜星,造成天文导航可靠性下降、姿态测量精度较低的问题。为此,本文提出了一种CNS+GNSS+INS高精度信息融合实时定位定姿框架,引入了等角速度外推措施,有效地解决了惯导信息延迟问题。通过高精度转台模拟恶劣海况下载体大角速度摇摆,验证了本文提出的改进算法的有效性。试验结果表明,该算法架构简单,性能可靠,显著提高了恶劣环境下星敏感器的快速、准确搜星能力,保障了三组合姿态测量的精度和可用性。 相似文献
3.
车载导航系统常用惯性测量元件（IMU）与全球卫星导航系统（GNSS）技术组合以提高系统的稳定性。由于车载导航系统的应用场景限制,对初始对准速度有着较高要求。为了提高传统车载组合导航系统中低成本微机电系统（MEMS）陀螺仪的初始对准速度,降低初始对准过程中的计算量,本文提出了一种适用于任意失准角下的基于网络RTK辅助与无损Kalman滤波（UKF）的MEMS陀螺仪初始对准算法。同时针对车载系统的特点,简化了IMU系统误差方程,分析了简化带来的误差。在诺瓦泰ProPak6和诺瓦泰IMU-IGM-S1组成的导航系统中验证了本文提出的算法。试验结果表明,在以诺瓦泰双天线GNSS输出航向角为"真值"的情况下,本文提出的算法基本可以在5 s内完成陀螺仪的初始对准,对准精度达0.3°。 相似文献
4.
Adaptive GPS/INS integration for relative navigation 总被引：1，自引：0，他引：1
Je Young Lee Hee Sung Kim Kwang Ho Choi Joonhoo Lim Sebum Chun Hyung Keun Lee 《GPS Solutions》2016,20(1):63-75
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. 相似文献
5.
基于GNSS系统的导航定位设备在封闭或受阻环境下导航精度受限,为此,提升地下空间或室内定位精度,摆脱对GNSS的依赖是当前的研究热点。针对该问题,本文研究了LiDAR+IMU+DMI多源传感器导航定位技术,通过将LiDAR控制标靶数据带入卡尔曼滤波方程,计算IMU+DMI组合的误差状态向量,限制其误差发散,从而获取设备的高精度位置。该技术能使移动检测设备完全摆脱对GNSS信号的依赖,实现地下封闭空间移动测量设备精确定位,便于地下空间检测。通过在武汉某地铁试验表明,本文算法适用于地下、室内空间封闭环境中无GNSS信号的移动测量设备高精度导航定位。 相似文献
6.
The timing error between global navigation satellite system (GNSS) and inertial navigation system (INS) processes limits the integration performance in GNSS/INS integrated systems. In a deeply coupled system, this timing error affects not only the integrated navigation solution, but also the GNSS signal tracking. We propose a time-domain model of INS-aided second-order phase-locked loops (PLLs) in consideration of the INS aiding delay, and analyze the effect of INS aiding delay on the tracking errors in details. In addition, an integrated hardware deeply coupled system platform was developed to verify the impact of time delay on INS-aided PLLs. Simulation and field vehicles testing results demonstrate that the tracking error of the INS-aided PLL caused by aiding delay increases with the lengthening of the delay time, the compression of the bandwidth, and the increase in the acceleration. Testing results verify the proposed model. 相似文献
7.
陆地导航中GNSS/陀螺仪组合实时测姿方法 总被引：2，自引：0，他引：2
在陆地导航系统中使用GNSS/INS组合导航会增加系统成本,多天线GNSS测姿精度受基线长度影响,且存在的模糊度固定问题。本文提出仅利用一个陀螺仪和单天线GNSS组合来进行实时测姿。先由单天线GNSS计算姿态角3参数,航向角为陆地导航的关键参数,为此将陀螺信息与GNSS导出的航向角进行融合。分析了单天线测姿在载体静止或低速运动时精度很差的原因,提出了在组合滤波中进行解决的方案。推导了GNSS和陀螺信息融合的滤波模型,将陀螺仪信息作为状态模型的控制输入,以GNSS航向为滤波观测值。实验结果表明,GNSS/陀螺仪组合计算的航向角精度和可靠性相对GNSS测姿结果均有很大提升。 相似文献
8.
MEMS-based integrated system of a global navigation satellite system (GNSS) and an inertial navigation system (INS) has been widely used in various navigation applications. However, such integration encounters some major limitations. On the one hand, the noisy MEMS-based INS undermines the accuracy with time during the frequently occurring GNSS outages caused by signal blockage or attenuation in certain situations such as urban canyon, tunnels, and high trees. On the other hand, the model mismatch between actual GNSS error and the assumed one would also degrade the obtained accuracy even with continuous GNSS aiding. To improve the overall performance for GNSS/MEMS-INS, better error models can be obtained using Allan variance (AV) analysis technique for modeling inertial sensor errors instead of the commonly recommended auto-regressive processes, and on the other hand, the measurement update in Kalman filter is improved using innovation filtering and AV calculation. The performance of each method and the combined algorithm is evaluated by a field test with either differential GNSS (DGNSS) or single-point positioning (SPP) as external aid. In addition to the considerable navigation enhancement brought by each method, the experimental results show the combined algorithm accomplishes overall accuracy improvements by about 18% (position), 8% (velocity), and 38% (attitude) for integration with DGNSS, and by about 15% (position), 75% (velocity), and 77% (attitude) for that with SPP, compared with corresponding traditional counterparts. 相似文献
9.
GNSS/INS紧组合导航系统自主完好性监测分析 总被引：2，自引：0，他引：2
可靠性和对故障的可区分性是评价系统完好性的两个重要因素。本文将多GNSS系统与不同精度的INS系统进行组合,由此分析不同因素对组合系统内部的可靠性和故障探测与隔离能力的影响。仿真结果表明,集成多GNSS系统可以改善卫星星座的几何分布结构,从而提高系统的内部可靠性和对故障的区分能力;当GNSS系统与INS系统相结合时,也能大幅度提高系统的可靠性和区分性;相较于低精度的INS系统,采用高精度的INS系统能够进一步的提高系统的可靠性,并增强对故障的区分能力。 相似文献
10.
Jörg F. Wagner 《GPS Solutions》2005,9(3):179-193
During the last years, the International Symposia on Precision Approach and Automatic Landing (ISPA) have shown a considerable change in the significance of integrated landing systems. At the first ISPA conference in 1995, the combination of inertial sensors and GNSS receivers was thoroughly discussed and appeared to be a very promising concept, especially with respect to ‘Integrity’ and ‘Continuity’. Ever since, this particular combination of sensors has received little attention. A comprehensive discussion of the technical background of this setback for integrated landing systems did not take place, primarily because of the popular opinion that the Kalman filter algorithm, which is the system integration kernel, is not sufficiently stable. The true reason has meanwhile been identified. The aiding of an inertial navigation system by only one GNSS antenna is insufficient in phases of low aircraft dynamics such as in the case of final approach. Instead, a multi-antenna system is required with antennas widely distributed over the aircraft structure. This latter approach, however, causes problems due to structural flexibilities. To show that an integrated system based on inertial sensors and widely distributed GNSS antennas is technically feasible, the paper discusses the following topics. (1) Unstable system performance during final approach for 1-antenna-aiding. (2) Improving the system performance prior to using additional antennas. (3) Effect of the antenna distribution. (4) Integrated systems for distributed sensors and flexible aircraft structures. The paper shows that integrated systems are still an attractive candidate for automatic landing equipment preserving the advantages with respect to ‘Integrity’ and ‘Continuity’. 相似文献
11.
目前,普通智能终端的平面定位精度在5 m左右。2016年,Google公司推出Android系统7.0版本,开始支持输出GNSS（global navigation satellite system）原始观测值。通过改正和计算,可以获得Android智能终端的伪码和载波相位观测值,从而实现较高精度的GNSS定位。研究采用华为P9手机进行,在观测条件良好的情况下,采集静态的GNSS原始观测数据,并采用多种方式分别进行定位解算,并分析其定位精度。同时,在相邻观测点放置NovAtel DL-V3-L1型接收机进行对比。实验表明通过静态下精密单点定位或者载波相位差分定位的方式,可以显著提升智能终端的定位精度,达到分米级水平。 相似文献
12.
准确、连续、可靠的位置信息是车载导航应用的基础条件,在不增加额外传感器的前提下,集成GNSS与MEMS及车载CAN总线传感器,并融入车辆运动约束信息,是最为简单有效且低成本的车载多源导航方案。在车辆运动约束中,合理配置相关参数是约束条件能否充分发挥作用的关键,本文重点针对车辆非完整性约束,采用多元回归和深度学习方法,构建了参数自主学习的车辆运动约束模型。同时,提出了在观测域直接学习侧向/垂向速度参数的新思路,相比原有方差域调参方法具有更好的约束效果。实测分析表明,相比于方差域调整参数的传统方法,在观测域进行参数自主学习的新模型具有显著的精度提升,采用多元回归模型的惯性推算误差在水平位置上减小了69.6%~81.2%,而利用深度学习则减小了60.0%~77.3%,同时,水平相对定位精度分别改善了75.2%和65.0%,新模型能够有效提升GNSS失效时车载定位精度维持能力。 相似文献
13.
14.
全球卫星导航系统(GNSS)在弱信号环境下,GNSS信号易受到遮挡或者电磁干扰,严重影响导航定位的可靠性、连续性和精度. 针对此问题,本文作者研究了一种GNSS和视觉观测紧组合导航定位方法. 首先基于相机采集图像数据,利用ORB-SLAM2开源平台求解得到视觉位置结果增量,再联合GNSS伪距观测数据采用卡尔曼滤波(KF)进行组合定位解算. 采用实测的GNSS伪距观测数据和图像数据进行测试,试验结果表明:该算法不仅能有效地提升GNSS弱信号环境下导航定位的连续性和精度,还能在卫星数少于4颗时保持持续导航定位. 相似文献
15.
In the complex urban environments, land vehicle navigation purely relying on GNSS cannot satisfy user needs due to the loss of satellite signals caused by obstructions such as buildings, tunnels, and trees. To solve this problem, we introduce a GPS-/MSINS-/magnetometer-integrated urban navigation system based on context awareness. In this system, the data from the Micro Strapdown Inertial Navigation System (MSINS) are used to analyze and detect the context knowledge of vehicles, whose sensor errors can be compensated by the heuristic drift reduction algorithm for different motion situations. When GPS is available, the vehicle position can be estimated by unscented Kalman Filter, whereas in the case of GPS outages, the vehicle attitude is provided by an attitude and heading reference system and the motion constraints-aided algorithm is used to complete the positioning. In the experiment validation, the integrated navigation system is set up by low-cost inertial sensors. The result shows that the proposed system can achieve high accuracy when GPS is available. For most of the time without GPS, the system can guarantee the positioning precision of 10 m and compensate the errors of MSINS effectively, which fully satisfies positioning needs in complex urban environments. 相似文献
16.
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. 相似文献
17.
采用全球卫星导航系统（Global Navigation Satellite System,GNSS）模糊度固定解可提高GNSS/惯性导航系统（inertial navigation system,INS）组合导航定位精度,而在复杂环境下,单频GNSS难以实现完善的实时动态周跳探测,影响GNSS模糊度保持。研究了星间单差与站星双差的INS辅助GNSS单频周跳探测检验量,重点分析检验量的误差特性。分析得出检验量误差主要与INS增量误差有关,受接收机至待检星与参考星之间星地矢量夹角的影响。提出了选取两颗参考星并优选探测检验量的方法,降低方位角因素的影响,提高周跳探测性能。周跳探测的阈值在滑动窗口内估计,对INS误差被GNSS误差淹没的部分进行抑制,充分反映INS误差影响,阈值估计具有较强的自适应性。 相似文献
18.
研究了静止条件下零角度修正在GNSS/INS组合导航中的应用。分析了静止条件下载体航向角解算漂移的原因,给出零角度修正的具体表达式。通过更改卡尔曼滤波量测方程的方法实现航向角误差修正,并用实测数据进行实验验证。结果表明,在静止条件下单独使用零角度修正可以提高载体的航向角精度,并利于垂向陀螺零偏的估计;在零角度修正基础上结合零速修正技术,航向角精度可以进一步改善。 相似文献
19.
描述了一种低成本的GPS/INS组合导航系统,经过理论推导得到了系统具体的实现方法,并通过市场上大量使用的低成本GPS模块和MEMS陀螺和加速度计实现了该系统。实际路测数据结果显示,该系统基本达到了理论预期。 相似文献
20.
A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications
The combined navigation system consisting of both global positioning system (GPS) and inertial navigation system (INS) results in reliable, accurate, and continuous navigation capability when compared to either a GPS or an INS stand-alone system. To improve the overall performance of low-cost micro-electro-mechanical systems (MEMS)-based INS/GPS by considering a high level of stochastic noise on low-cost MEMS-based inertial sensors, a highly complex problems with noisy real data, a high-speed vehicle, and GPS signal outage during our experiments, we suggest two approaches at different steps: (1) improving the signal-to-noise ratio of the inertial sensor measurements and attenuating high-frequency noise using the discrete wavelet transform technique before data fusion while preserving important information like the vehicle motion information and (2) enhancing the positioning accuracy and speed by an extreme learning machine (ELM) which has the characteristics of quick learning speed and impressive generalization performance. We present a single-hidden layer feedforward neural network which is employed to optimize the estimation accuracy and speed by minimizing the error, especially in the high-speed vehicle and real-time implementation applications. To validate the performance of our proposed method, the results are compared with an adaptive neuro-fuzzy inference system (ANFIS) and an extended Kalman filter (EKF) method. The achieved accuracies are discussed. The results suggest a promising and superior prospect for ELM in the field of positioning for low-cost MEMS-based inertial sensors in the absence of GPS signal, as it outperforms ANFIS and EKF by approximately 50 and 70%, respectively. 相似文献