中航时平台惯导系统导航精度的全误差评估方法

平台惯导系统(PINS)初始对准误差和标定参数重复性是评估PINS精度的主要参数.应用给定精度评定指标无法准确评估PINS导航精度,PINS全误差导航模型能够准确描述PINS误差传播过程,但对多批次中航时PINS精度评估需消耗大量时间,缺乏实用性.结合实际应用中需要对每一台PINS中航时导航精度进行快速准确评估的需求,通过对PINS全误差导航模型传播规律进行分析,得到PINS误差参数对导航精度的单项评估公式和简化综合评估公式.数值仿真验证表明:该方法能够对中航时PINS导航精度进行快速准确估算,在PINS选择中具有很好的实用性.同时该方法可以指导惯导系统生产调试,找到影响惯导系统的主要误差因素,对改进设计、提高精度和精度评定具有参考价值.     

惯性导航系统的精度测试方法

介绍一种测试惯性导航系统精度的方法,包括对位置、航向、横滚、俯仰以及速度等5个导航参数的精度测试。     

AGV混合导引系统的研究与实现

AGV混合导引系统结合了电磁导引与射频识别(Radio Frequency Identification,RFID),并使用3种不同的路径选择方法实现了系统的导航功能。该系统对AGV硬件电路及导航信号源进行了精密设计,同时实现了包括无线通信的软件控制系统及四级保护的安全开发系统。测试结果显示,系统的测量与控制信号具有较高的精度和稳定性,AGV混合导引系统解决了国内电磁导航技术不能适应复杂路径的局限性,在导航、定位和路径规划上实现了技术突破,提高了AGV的生产效率。     

北斗高精度授时终端测试技术研究

针对北斗授时终端提出了基于模拟环境下的定时精度及守时精度性能测试方法,使用卫星导航信号模拟器、铷钟、计数器搭建了模拟环境下北斗授时终端测试系统,北斗授时终端通过解算卫星信号,解算出北斗时间后,输出秒脉冲1PPS信号,实现和北斗模拟时间同步。测试系统解算北斗授时终端和卫星导航信号模拟器输出的秒脉冲1PPS信号之间的时间间隔,获取终端定时精度,测试结果表明测试方法具有较好的一致性和可重复性,实现了纳秒级别的定时精度测试。     

基于足部零速约束的行人自主导航微系统设计

随着智能健康管理水平的提升,对传感装备及其测量精度的技术要求也在不断提高。针对低精度微型惯性测量单元的不足,设计一种可穿戴鞋式的足部约束行人自主惯性导航微系统,分析行人行进过程的足部运动特征,构造基于足部零速约束的滤波算法模型,实现行进过程中的自主导航误差连续反馈修正,研制相应的硬件集成微系统并实现算法片上解算输出。多边形路线的行走实验测试结果表明,引入零速修正后的导航算法误差为行走距离的1.6%,导航精度提升约两个数量级。     

非合作导航信号盲检测与参数盲估计

为了实现非合作导航信号的监测,研究了非合作导航信号的多普勒频率、伪码速率、伪码周期、信号功率、码序列等参数的估计.在描述全球卫星导航系统( GNSS)信号模型和GNSS监测系统体系结构的基础上,给出了GNSS信号检测与参数估计流程,提出了一套非合作导航信号盲检测与参数盲估计方法,即利用导航信号循环平稳特性估计信号频率、...     

单海底应答器声学导航及应答器绝对位置校准

给出了一种局部海域潜器高精度声学定位导航方法.该方法利用安装在潜器上的超短基线(USBL)定位系统和锚定于海底的单只应答器构成定位导航系统,为潜器在局部海域提供高精度定位导航,系统复杂性和操作难度低,可靠性高.为提高应答器绝对位置的校准精度,提出了一种声线弯曲修正方法.2006年中国南海试验表明,试验系统可对锚定于3700m海深的海底应答器精确校准,并且利用校准后的应答器为对安装有精度为0.5%斜距的超短基线的水面船进行导航,导航精度为25m.     

基于卫星导航信号模拟仿真法的北斗接收机导航精度校准测试

随着我国自主设计的北斗卫星导航系统稳步建设,北斗接收机相关技术取得突破,但当前北斗接收机校准体系尚不完善,相关测试方法亟待研究。本文提出卫星导航信号模拟测试法,通过多星座导航信号模拟器设计静态、动态等仿真场景模拟相关载体运动状态下真实的卫星信号,实现对北斗接收机在相关载体运动状态下导航精度的校准测试。该方法具有准确度高、简单易操作和重复性强等优点,可实现实验室条件下检测接收机性能。实验结果表明:北斗系统的定位和测速精度已基本接近GPS系统,但就系统的稳定性,北斗系统与GPS系统相比还存在一定差距。     

导航星座自主导航技术研究

导航星座自主导航日益成为新一代卫星导航系统的主要研究方向。在系统地论述导航星座自主导航的信息处理流程的基础上，重点提出了导航星座自主导航的关键技术，包括卫星星历与时钟参数的长期预报技术、星间测距与通信链路的建立和维持技术、星座卫星自主时间同步技术、星座卫星自主星历更新技术、自主导航信息处理的鲁棒滤波技术、星座整体旋转建模技术，以及地球自转及极移参数的长期预报技术。详细分析了关键技术实施途径，论证了相关数学模型。最后，对星座卫星自主时间同步与星历更新算法进行了系统仿真，结果表明：通过星载滤波器处理星间双向测     

向小天体着陆的自主导航研究

研究了空间飞行器向小天体着陆阶段基于导航相机和激光测距仪的自主光学导航方案。采用扩展卡尔曼滤波方法设计了导航滤波器。利用数值仿真验证了其系统性能，并着重分析了测量误差、初始状态误差、参数误差等各种误差源对导航滤波器的影响。仿真表明滤波器的性能对目标天体的自旋角速度误差较为敏感。     

基于GNSS/INS联合仿真的组合导航终端测试方法研究

为解决GNSS/INS组合导航终端动态定位性能的实验室测试问题,本文在转台惯性仿真测试和卫星导航仿真测试技术的基础上,提出了GNSS/INS联合仿真两步法,即先通过惯性传感器实物测试获得其特征误差模型,再使用测试场景中载体初始条件和轨迹数据驱动该误差模型产生惯性传感器的模拟输出数据流,最后同步GNSS信号仿真的方法实现GNSS/INS联合仿真的过程。仿真测试结果与外场实测对比后,证明该方法获得的测试数据准确度满足预期指标、结果可靠,而且比其他传统测试方法的成本低、效率高。     

惯导系统传递对准精度实验的半物理仿真方案

提出了一种惯导系统传递对准精度试验的半物理仿真方案,该方案可用于评估空空导弹捷联惯导系统的传递对准精度.该半物理仿真系统模拟空中传递对准的"真实过程",评估出导弹惯导系统相对于基准系统的失准角,搭建了给予该方案的半物理仿真试验系统.经在某型惯导产品的传递对准精度试验中的实际应用,表明该方案是可行的和实用的.     

Non-Exchangeable Error Compensation for Strapdown Inertial Navigation System in High Dynamic Environment

Strapdown non-exchangeable error compensation technology in high dynamic environment is one of the key technologies of strapdown inertial navigation system. Mathematical platform is used in strapdown inertial navigation system instead of physical platform in traditional platform inertial navigation system, which improves reliability and reduces cost and volume of system. The maximum error source of attitude matrix solution is the non-exchangeable error of rotation due to the non-exchangeable of finite rotation of rigid bodies. The rotation non-exchangeable error reaches the maximum in coning motion, although it can be reduced by shortening the correction period and increasing the real-time calculation. The equivalent rotation vector method is used to modify the attitude to reduce the coning error in this paper. Simulation experiments show that the equivalent rotation vector method can effectively suppress the non-exchangeable error and improve the accuracy of attitude calculation.     

Improvement on Azimuth in Rotary Inertial Navigation System Using Photoelectric Encoder and Fiber Optic Gyro

For its obvious high accuracy in a long term, rotary inertial navigation system becomes popular. However, as errors increasing with time are changed into periodic errors with zero mean, the accuracy of its azimuth angle decreases in view of short term and can’t fully satisfy the need of users such as flight control system. The precision of azimuth angle is studied by analyzing its influence factor and establishing the scale factor error model and drift models of fiber optic gyro and the measurement error model of photoelectric encoder. Correction for fiber optic gyro and photoelectric encoder based on their mutual assistance is designed to improve the short-term accuracy of rotary inertial navigation system’s azimuth angle. Experimental results in a fiber optic gyro based rotary inertial navigation system verify the proposed method.     

Optimizing of ANFIS for estimating INS error during GPS outages

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.     

Distributed data fusion algorithms for inertial network systems

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.     

Fast Compass Alignment for Strapdown Inertial Navigation System

Initial alignment is the precondition for strapdown inertial navigation system (SINS) to navigate. Its two important indexes are accuracy and rapidity, the accuracy of the initial alignment is directly related to the working accuracy of SINS, but in selfalignment, the two indexes are often contradictory. In view of the limitations of conventional data processing algorithms, a novel method of compass alignment based on stored data and repeated navigation calculation for SINS is proposed. By means of data storage, the same data is used in different stages of the initial alignment, which is beneficial to shorten the initial alignment time and improve the alignment accuracy. In order to verify the correctness of the compass algorithm based on stored data and repeated navigation calculation, the simulation experiment was done. In summary, when the computer performance is sufficiently high, the compass alignment method based on the stored data and the forward and reverse navigation calculation can effectively improve the alignment speed and improve the alignment accuracy.     

光纤陀螺组件误差标定ARLS算法

捷联惯性导航系统误差参数标定的准确程度对于系统的导航和定位精度具有重要影响.针对常规速率标定法不能辩识陀螺零偏,未充分预热时光纤陀螺的误差标定易受温度变化影响这两个问题,提出了一种用于光纤陀螺捷联惯性导航系统的新标定算法--自适应递推最小二乘法(ARLS).在建立光纤陀螺误差及其补偿模型的基础上,通过大量温度实验研究了自适应遗忘因子的求取方法,详细推导了ARLS算法及其实现思路.最后通过算法仿真和速率试验证明了在器件特性不稳定条件下,ARLS算法能有效辨识陀螺的误差参数及减小温度变化对光纤陀螺误差标定的影响.     

多传感器融合的智能车定位导航系统设计

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。