共查询到18条相似文献,搜索用时 218 毫秒
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《测试技术学报》2016,(2)
近年来INS/GNSS组合导航系统的理论研究已趋于成熟,但是对于理论研究的结果却难于验证.利用高精度的三轴转台完成INS/GNSS组合导航系统半实物仿真试验,对组合导航系统研究的理论与应用具有重要的意义.采用高速的PCI接口,基于已有的GNSS模拟器、高精度三轴转台搭建GNSS/IMU组合导航闭环仿真测试系统,以数字信号处理器(DSP)和现场可编程门阵列(FPGA)作输入输出接口处理机,二者以外部存储器(EMIF)接口相联,并采用大容量SDRAM作为存储单元,处理速度快,满足了组合导航半实物仿真系统对主控计算机在数据吞吐和处理能力以及体积、重量、功耗诸方面的要求.介绍了INS/GNSS组合导航系统的基本构成及其研制思路、构成和半实物仿真系统的工作流程,并给出了INS/GNSS组合导航系统的实验结果. 相似文献
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《中国测试》2019,(10):45-52
GNSS精密定位技术依赖于高精度的载波相位观测信息,而当载体高动态运动时,GNSS接收机的PLL跟踪环路由于承受较大的动态应力可能无法保持对导航卫星信号载波相位的稳定跟踪。针对这一问题,该文设计INS辅助的PLL跟踪环路结构,并且系统地设计基于典型高动态圆周运动的仿真(径向加速度50 g)与实测测试(径向加速度5.2 g)方法。测试结果表明:设计的INS辅助GNSS PLL跟踪环路结构,可以实现高动态条件下对卫星信号载波相位的稳定跟踪以及连续稳定的导航定位解算,且相比于普通环路,可以通过进一步压缩环路带宽和延长相干积分时间,获得更高精度的载波相位观测信息和多普勒估计信息。 相似文献
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阐述了移动测量系统的定位准确度需求,对圆概率法、均方差法和中误差法三种位置准确度评估方法进行分析比较,并在标准试验场内开展了车载GNSS/INS定位准确度测试。实验证明:中误差法的评估结果最准确,其误差小于1 m,明显优于圆概率法和均方差法。 相似文献
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基于磁链理论和Monte-Carlo法,建立了磁流体薄膜(MFF)传感模型和MFF透射模型,分析了磁流体透射特性。采用粒子群算法对MFF透射模型进行了参数辨识,分析了群体数目、迭代次数、惯性权重、加速度因子等参数选值对算法运行结果的影响,并选取了最佳参数组合。搭建了MFF电流传感器实验平台,运用MFF透射模型对MFF电流传感器进行了仿真预测,分析了MFF厚度和粒子浓度对MFF透射性的影响,实验及仿真结果表明该模型预测误差在2.3%以内,该MFF电流传感器的测量灵敏度达到12 μW/A。 相似文献
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《中国计量学院学报》2022,(1):1-6
目的:针对网约车计程检测问题,研究了基于差分GNSS/INS组合定位基准的计程检测技术。方法:首先研究了基于载波GNSS/INS组合定位结果的里程恢复方法,从而为网约车计程检测提供基准;同时对智能手机在未知道路信息环境下的计程误差进行了分析。结果:在建筑遮挡和林荫遮挡的真实道路环境下进行网约车计程精度实验,结果表明组合定位基准结果与实际道路具有较好的一致性,手机端计程误差分别为1.02%和0.25%。结论:组合定位可为网约车在城市环境下的计程检测提供里程基准;手机计程在有地图信息的道路环境下计程误差较小,但在缺乏先验地图信息的未知道路信息环境下,可能会造成较大的计程误差。 相似文献
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卫星导航定位技术GNSS在测绘、航天、军事和遥感领域都得到了广泛的应用。随着技术的发展,如何提高对GNSS中周跳的探测和修复是我们面临的首要问题。本文主要针对GNSS中的周跳问题,阐述了伪距/相位组合,电离层残差法探测和修复周跳的原理与方法。建立仿真模型,确立周跳探测与修复算法评判标准,分别对两种方法进行仿真,通过仿真得出,伪距/相位差法适合修复大周跳,电离层残差法能对小周跳进行探测,在单独使用一种方法时均存在一定的缺陷,仿真分析发现,两种方法联合应用,能克服各自缺点,适合多种条件下的周跳探测与修复。 相似文献
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针对机载北斗卫星导航系统(BDS)接收机无法进行实际测试的问题,基于对机载BDS接收机的首次定位时间(TTFF)、重捕时间和定位精度的分析与仿真,研究某一指标与相关参数的关系,提出利用全球导航卫星系统(GNSS)模拟信号源测试机载BDS接收机性能指标的测试方法。该方法结合真实飞行数据,利用GNSS模拟信号源模拟飞机运动过程,从而对飞行过程中机载BDS接收机的关键指标进行测试。测试结果表明:机载BDS接收机的首次定位时间满足RTCA DO-208对TTFF不大于5 min的要求;重捕时间基本满足TSO-C146d对重捕时间不大于20 s的要求;静态误差满足北斗公开服务性能规范对其不大于10 m的要求,动态误差基本满足RTCA DO-208对其小于230 m的要求,且测试结果均符合理论分析。 相似文献
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Global positioning system (GPS) has been extensively used for land vehicle navigation systems. However, GPS is incapable of providing permanent and reliable navigation solutions in the presence of signal evaporation or blockage. On the other hand, navigation systems, in particular, inertial navigation systems (INSs), have become important components in different military and civil applications due to the recent advent of micro-electro-mechanical systems (MEMS). Both INS and GPS systems are often paired together to provide a reliable navigation solution by integrating the long-term GPS accuracy with the short-term INS accuracy. This article presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution. This alternative approach to Kalman filtering (KF) utilizes artificial intelligence based on adaptive neuro-fuzzy inference system (ANFIS) to fuse data from both systems and estimate position and velocity errors. The KF is usually criticized for working only under predefined models and for its observability problem of hidden state variables, sensor error models, immunity to noise, sensor dependency, and linearization dependency. The training and updating of ANFIS parameters is one of the main problems. Therefore, the challenges encountered implementing an ANFIS module in real time have been overcome using particle swarm optimization (PSO) to optimize the ANFIS learning parameters since PSO involves less complexity and has fast convergence. The proposed alternative method uses GPS with INS data and PSO to update the intelligent PANFIS navigator using GPS/INS error as a fitness function to be minimized. Three methods of optimization have been tested and compared to estimate the INS error. Finally, the performance of the proposed alternative method has been examined using real field test data of MEMS grade INS integrated with GPS for different GPS outage periods. The results obtained outperform KF, particularly during long GPS signal blockage. 相似文献
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Distributed data fusion algorithms for inertial network systems 总被引:1,自引:0,他引:1
New approaches to the development of data fusion algorithms for inertial network systems are described. The aim of this development is to increase the accuracy of estimates of inertial state vectors in all the network nodes, including the navigation states, and also to improve the fault tolerance of inertial network systems. An analysis of distributed inertial sensing models is presented and new distributed data fusion algorithms are developed for inertial network systems. The distributed data fusion algorithm comprises two steps: inertial measurement fusion and state fusion. The inertial measurement fusion allows each node to assimilate all the inertial measurements from an inertial network system, which can improve the performance of inertial sensor failure detection and isolation algorithms by providing more information. The state fusion further increases the accuracy and enhances the integrity of the local inertial states and navigation state estimates. The simulation results show that the two-step fusion procedure overcomes the disadvantages of traditional inertial sensor alignment procedures. The slave inertial nodes can be accurately aligned to the master node. 相似文献
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Recently, methods based on artificial intelligence (AI) have been suggested to provide reliable positioning information for different land vehicle navigation applications. The majority of these applications utilise both the global positioning system (GPS) and the inertial navigation system (INS). These AI modules were trained to mimic the latest vehicle dynamics so that, in case of GPS outages, the system relies on INS and the recently updated AI module to provide the vehicle position. Several neural networks and neuro-fuzzy techniques were implemented in real-time in a de-centralised fashion and provided acceptable accuracy for short GPS outages. It was reported that these methods provided poor positioning accuracy during relatively long GPS outages. In order to prevail over this limitation, this study optimises the Al-based INS/GPS integration schemes utilising adaptive neuro-fuzzy inference system with performing, in real-time, both GPS position and velocity updates. In addition, a holdout cross validation method during the update procedure was utilised in order to ensure generalisation of the model. The proposed system is tested using differential GPS and both navigational and tactical grades INS field test data obtained from a land vehicle experiment. The results showed that the effectiveness of the proposed system over both the existing Al-based and the conventional INS/GPS integration techniques, especially during long GPS outages. This method may have one limitation related to the unusual significant changes of the vehicle dynamics between the update and the prediction stages of operation which may influence the overall positioning accuracy. 相似文献
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车辆定位导航系统实时、准确获取车辆位置对实现智能驾驶具有重要意义。针对传统定位导航系统存在的精度低、成本高、鲁棒性差等问题,基于GPS/INS(global positioning system /inertial navigation system,全球定位系统/惯性导航系统)、机器视觉和超声波雷达技术,设计了一种多传感器融合的智能车定位导航系统,旨在实现智能车在简单、结构化道路环境下的自动驾驶。利用GPS/INS技术实现智能车地理坐标获取,利用机器视觉技术实现智能车前方车道线检测,利用超声波雷达技术实现道路边沿检测,并对地理坐标、车道线和道路边沿数据进行深度融合,实现车道级定位导航。最后,进行了智能车定位导航现场测试,结果表明该系统满足车道级定位导航性能要求。研究结果表明,在简单、结构化道路环境下,多传感器融合的智能车定位导航系统结构简单,实际运行状况良好,可极大提高定位导航精度。 相似文献
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掘进机时走时停的迈步式工作特点导致其难以实现定向掘进,这一直是国内外面临的技术难题。为实现煤矿巷道掘进机的位姿测量,提出一种基于组合方式的掘进机导航系统(即组合导航系统)。首先,以十字激光指向仪的激光束为识别特征,结合自主研制的激光接收标靶,组建光电传感器导航系统,并构建掘进机机身位姿解算模型;然后,通过递推最小二乘(recursive least square, RLS)算法将光电传感器导航系统和光纤惯性导航系统的测量数据融合,解算出掘进机机身的位姿;最后,根据掘进机机身位姿的测量结果,通过PLC(programmable logic controller,可编程逻辑控制器)实现掘进机的纠偏控制,以有效解决其定向掘进问题。以MB670型掘锚一体机为实验对象,通过现场实验对所设计的组合导航系统进行测量误差分析。结果表明:该组合导航系统可以有效实现煤矿巷道掘进机机身的位姿测量;掘进机机身的位置测量误差在±20 mm以内,姿态角测量误差在±0.15°以内,满足煤矿巷道施工精度要求。基于组合方式的掘进机导航系统具有精度高、可靠性好的优点,弥补了单一导航系统的测量缺陷,可为掘进机的定向掘进智能... 相似文献