Periodic Error Compensation for Quartz MEMS Gyroscope Drift of INS

In order to improve the navigation accuracy of an inertial navigation system （INS）, composed of quartz gyroscopes, the existing real-time compensation methods for periodic errors in quartz gyroscope drift and the periodic error term relationship between sampled original data and smoothed data are reviewed. On the base of the results, a new compensation method called using former period characteristics to compensate latter smoothness data （UFCL for short） method is proposed considering the INS working characteristics. This new method uses the original data without smoothing to work out an error conversion formula at the INS initial alignment time and then compensate the smoothed data errors by way of the formula at the navigation time. Both theoretical analysis and experimental results demonstrate that this method is able to cut down on computational time and raise the accuracy which makes it a better real-time compensation approach for periodic error terms of quartz micro electronic mechanical system （MEMS） gyroscope＇s zero drift.     

Navigation sensors and systems in GNSS degraded and denied environments

Position, velocity, and timing(PVT) signals from the Global Positioning System(GPS)are used throughout the world but the availability and reliability of these signals in all environments has become a subject of concern for both civilian and military applications. This presentation summarizes recent advances in navigation sensor technology, including GPS, inertial, and other navigation aids that address these concerns. Also addressed are developments in sensor integration technology with several examples described, including the Bluefin-21 system mechanization.     

Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.     

Navigation message designing with high accuracy for NAV

Navigation message designing with high accuracy guarantee is the key to efficient navigation message distribution in the global navigation satellite system（GNSS）. Developing high accuracy-aware navigation message designing algorithms is an important topic. This paper investigates the high-accuracy navigation message designing problem with the message structure unchanged.The contributions made in this paper include a heuristic that employs the concept of the estimated range deviation（ERD） to improve the existing well-known navigation message on L1 frequency（NAV） of global positioning system（GPS） for good accuracy service; a numerical analysis approximation method（NAAM） to evaluate the range error due to truncation（RET） of different navigation messages; and a basic positioning parameters designing algorithm in the limited space allocation. Based on the predicted ultra-rapid data from the ultra-rapid data from the international GPS service for geodynamic（IGU）, ERDs are generated in real time for error correction.Simulations show that the algorithms developed in this paper are general and flexible, and thus are applicable to NAV improvement and other navigation message designs.     

STUDY OF FAST TRANSFER ALIGNMENT ON MOVING BASE

This paper holds that the key to improve the hitting rate of air-to-air missiles is to decrease the error of initial alignment of the inertial navigation system. Therefore, considering that the alignment should be completed within a specified short time, this paper presents the theory of transfer alignment and the computing way of accuracy in an air-to-air missile, where the platform inertial navigation system, or master INS, is adopted on aircraft, and the strap-down inertial navigation system, or slave INS, is used on missile. It emphasizes the idea of transfer alignment, that is, calibration of the slave INS is based on the master platform, and adopts a reasoning measure to deal with the installing-error-angle. And it is proved by simulation that the transfer alignment can be quickly achieved.     

Determination of optimal samples for robot calibration based on error similarity

Industrial robots are used for automatic drilling and riveting.The absolute position accuracy of an industrial robot is one of the key performance indexes in aircraft assembly,and can be improved through error compensation to meet aircraft assembly requirements.The achievable accuracy and the difficulty of accuracy compensation implementation are closely related to the choice of sampling points.Therefore,based on the error similarity error compensation method,a method for choosing sampling points on a uniform grid is proposed.A simulation is conducted to analyze the influence of the sample point locations on error compensation.In addition,the grid steps of the sampling points are optimized using a statistical analysis method.The method is used to generate grids and optimize the grid steps of a Kuka KR-210 robot.The experimental results show that the method for planning sampling data can be used to effectively optimize the sampling grid.After error compensation,the position accuracy of the robot meets the position accuracy requirements.     

DOA estimation for attitude determination on communication satellites

In order to determine an appropriate attitude of three-axis stabilized communication satellites, this paper describes a novel attitude determination method using direction of arrival （DOA） estimation of a ground signal source. It differs from optical measurement, magnetic field measurement, inertial measurement, and global positioning system （GPS） attitude determination. The proposed method is characterized by taking the ground signal source as the attitude reference and acquiring attitude information from DOA estimation. Firstly, an attitude measurement equation with DOA estimation is derived in detail. Then, the error of the measurement equation is analyzed. Finally, an attitude determination algorithm is presented using a dynamic model, the attitude measurement equation, and measurement errors. A developing low Earth orbit （LEO） satellite which tests mobile communication technology with smart antennas can be stabilized in three axes by corporately using a magnetometer, reaction wheels, and three-axis magnetorquer rods. Based on the communication satellite, simulation results demonstrate the effectiveness of the method. The method could be a backup of attitude determination to prevent a system failure on the satellite. Its precision depends on the number of snapshots and the input signal-to-noise ratio （SNR） with DOA estimation.     

A dimension reduced INS/VNS integrated navigation method for planetary rovers

Inertial navigation system/visual navigation system (INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and current state vectors (position and attitude) of planetary rovers, the performance of the Kalman filter (KF) will be challenged by the time-correlation problem. A state augmentation method, which augments the previous state value to the state vector, is commonly used when dealing with this problem. However, the augmenting of state dimensions will result in an increase in computation load. In this paper, a state dimension reduced INS/VNS integrated nav-igation method based on coordinates of feature points is presented that utilizes the information obtained through INS/VNS integrated navigation at a previous moment to overcome the time rel-evance problem and reduce the dimensions of the state vector. Equations of extended Kalman filter (EKF) are used to demonstrate the equivalence of calculated results between the proposed method and traditional state augmented methods. Results of simulation and experimentation indicate that this method has less computational load but similar accuracy when compared with traditional methods.     

Receiver-channel based adaptive blind equalization approach for GPS dynamic multipath mitigation

Aiming at mitigating multipath effect in dynamic global positioning system (GPS) satellite navigation applications, an approach based on channel blind equalization and real-time recursive least square (RLS) algorithm is proposed, which is an application of the wireless communication channel equalization theory to GPS receiver tracking loops. The blind equalization mechanism builds upon the detection of the correlation distortion due to multipath channels; therefore an increase in the number of correlator channels is required compared with conventional GPS receivers. An adaptive estimator based on the real-time RLS algorithm is designed for dynamic estimation of multipath channel response. Then, the code and carrier phase receiver tracking errors are compensated by removing the estimated multipath components from the correlators’ outputs. To demonstrate the capabilities of the proposed approach, this technique is integrated into a GPS software receiver connected to a navigation satellite signal simulator, thus simulations under controlled dynamic multipath scenarios can be carried out. Simulation results show that in a dynamic and fairly severe multipath environment, the proposed approach achieves simultaneously instantaneous accurate multipath channel estimation and significant multipath tracking errors reduction in both code delay and carrier phase.     

Low C/N_0 Carrier Tracking Loop Based on Optimal Estimation Algorithm in GPS Software Receivers

Suppressing jitter noises in a phase locked loop(PLL) is of great importance in order to keep precise and continuous track of global positioning system(GPS) signals characterized by low carrier-to-noise ratio(C/N0).This article proposes and analyzes an improved Kalman-filter-based PLL to process weak carrier signals in GPS software receivers.After reviewing the optimal-bandwidth-based traditional second-order PLL,a Kalman-filter-based estimation algorithm is implemented for the new PLL by decorrelating the model error noises and the measurement noises.Finally,the efficiency of this new Kalman-filter-based PLL is verified by experimental data.Compared to the traditional second-order PLL,this new PLL is in position to make correct estimation of the carrier phase differences and Doppler shifts with less overshoots and noise disturbances and keeps an effective check on the disturbances out of jitter noises in PLL.The results show that during processing normal signals,this improved PLL reduces the standard deviation from 0.010 69 cycle to 0.007 63 cycle,and for weak signal processing,the phase jitter range and the Doppler shifts can be controlled within ±17° and ±5Hz as against ±25° and ±15 Hz by the traditional PLL.     

一种适用于跑步状态的惯性/零速/GPS室内外无缝组合导航定位方法

目前，行人导航定位技术已经深入社会的众多领域，受到诸多学者的广泛关注。针对行人跑步状态，研究了一种惯性/零速/GPS室内外无缝组合导航定位方法。首先提出了可靠的、适用于行人跑步零速检测的方法，有效提高了在行人跑步状态下的零速检测的准确性。针对GPS信号容易受到高楼、高架等环境的干扰及在室内容易完全丢失的特点，提出了基于BP神经网络的GPS可用信号筛选方法，提高了GPS信息的可靠性与精准性。在此基础上，研究了基于可变量测的Kalman滤波器，实现了惯性/零速/GPS信息的有效融合，显著提高了在行人跑步状态下的导航定位精度。试验结果表明，所提出的这种适用于跑步状态的惯性/零速/GPS室内外无缝组合导航定位方法的平均定位误差可减小到行人跑步总里程的1%以内。     

基于零速修正的行人导航关键技术及研究进展

在室内等卫星导航受限场景下，可采用MEMS惯性传感器对行人进行自主导航定位。采用MEMS惯性传感器的行人自主导航技术主要包括基于步长估计的行人航位推算和基于零速修正的行人导航算法两种方式。首先介绍了两种方式的基本原理，然后重点分析了零速修正的行人导航算法，阐述了相关的关键技术和研究进展。最后，对行人自主导航未来的发展趋势进行了展望，并指出了需要重点关注的技术难点。     

基于改进的磁场检验和主方向算法的行人航向估计

针对以惯导为核心的行人定位算法存在航向角失真的问题，提出了一种融合惯性测量单元(IMU)和磁力计的行人导航算法，利用步幅航向的变化量将行人的运动分为直线行走和曲线行走两部分，当检测到行人直线行走时，提出一种实时建立主方向的自适应航向角漂移修正算法，对航行角误差进行补偿；当行人曲线行走时，辅助地磁场信息，使用磁场强度、地磁倾角、惯性导航系统(inertial navigation system，INS)与磁力计解算的航向角之差这三个特征值构建广义似然比检验量，对磁场进行判断获得稳定状态磁场信息，通过卡尔曼滤波对航向角误差进行补偿。将传感器置于足部进行实验，实验结果表明,两种算法可以实现优势互补，有效地抑制地磁信息的干扰，提高导航精度，算法结果相对于无航向角修正的零速修正算法、零角速度更新算法和单独使用磁力计进行融合的算法，定位误差分别降低了91.02%、82.93%和61.39%，室外和室内终点定位误差分别为4.545 4 m和0.543 6 m，占总路程的0.69%和0.17%。     

基于UWB/SINS组合的行人导航研究

在特殊环境下全球定位系统(GPS)信号强度被严重削弱,此时基于GPS技术的导航设备将受到严重影响。针对不依赖GPS的行人导航定位需求,提出了一种基于微机电捷联惯导系统(SINS)与超宽带(UWB)定位系统相结合的行人导航方法。该系统由捷联惯导系统与超宽带定位系统组成,行人导航算法在传统的捷联算法的基础上引入了零速修正技术用于检测零速时刻,并使用阈值法剔除了超宽带错误信息,通过联邦Kalman滤波融合了零速、位置和航向信息,并对系统速度、位置、航向进行了校正。行人导航实验表明,该方法能够提升系统定位精度,并进一步加强系统的稳定性与可靠性。     

基于PDR和磁场匹配的智能手机室内定位

目前，基于磁场的室内定位方法存在指纹采集无法快速建库的问题，导致匹配特征较少、无法快速进行匹配操作和航向角估计精度低。基于磁场梯度的快速近似，使用高斯牛顿迭代方法结合通过PDR测量的轨迹进行磁场轮廓匹配定位，提高了单点磁场指纹的可分辨性。基于步态模型的更新用作测量信息以改善C-INS的导航性能，提出了一种基于捷联惯性导航系统的行人航位推算算法。基于MFS参考位置，使用扩展Kalman滤波器控制PDR惯性导航的位置漂移误差，进一步提高了轨迹轮廓的精度。测试结果表明，该算法可以获得更好的位置估计和航向估计结果。     

基于对偶四元数的INS/GPS组合导航算法研究

在基于对偶四元数的捷联惯导解算方法的基础上,推导了以惯性系作为导航系的惯导误差方程,在此基础上设计了卡尔曼滤波组合导航算法。通过激光惯导跑车采集数据,进行了仿真分析,试验结果表明,该组合导航算法能有效的消除惯导累积的速度误差和位置误差,相比于目前广泛应用的INS/GPS组合导航算法,本文描述了INS/GPS组合导航的另一种实现方式,获得了相当的精度,具有一定的工程应用价值。     

平流层飞艇升降阶段BPNN惯性导航算法

软式平流层飞艇艇体在上升和下降时经常呈堆叠状态，GPS信号会被艇体间歇性遮挡，因而只能采用惯性导航。为保证在飞艇上升和下降过程中，INS/GPS组合导航系统在被艇体遮挡GPS时仍能够提供满足精度要求的导航信息，设计了一种改进的反向传播神经网络(Back Propagation Neural Network, BPNN）惯性导航算法。采用神经网络，根据惯性导航系统在1s内的速度均值和姿态变化量，预估其在1s末的位置误差和速度误差，并对惯性导航结果进行修正。仿真实验和跑车试验结果表明，在GPS失效的30s内，新算法使得位置误差低于15m，速度误差低于0.7m/s，误差相比纯惯性导航降低了85%。     

基于室内合作场景智能识别的行人导航算法

基于足绑式惯性测量单元(IMU)的惯性导航系统被广泛应用于行人导航中,其通过零速修正(ZUPT)算法可对速度估计误差进行较好的补偿,然而其位置误差会随时间发散。针对于此,提出了一种基于室内合作场景智能识别的行人导航算法。通过随机森林算法,对行人在室内平地步行、上楼梯、下楼梯等不同步态进行训练与辨识,并结合室内先验地图对行人导航的结果进行校正。通过实验表明,行人在室内行走1100m时最大定位误差为1.85m(总行程0.17%),相对无场景识别的方法精度提高了6倍,可以有效提高行人导航精度。     

腰绑式行人导航系统基于缓存区的启发式航向补偿算法研究

腰绑式行人导航系统通过航位推算方法实现行人定位导航，对于实行灾难救援和单兵作战等任务十分便利。行人行进步长和航向的准确估计是实现航位推算的前提。根据腰绑式惯性传感器信号的特征，采用基于三轴合成加速度的峰值检测进行单步划分；利用步频和加速度方差信息构建线性步长模型。针对人员执行任务过程中由于运动方向不确定性导致传统启发式航向漂移消除算法过度修正甚至失效的问题，提出了一种基于缓存区的启发式航向补偿算法。根据运动方式选定N个复步作为一个缓存区，计算缓存区内相邻复步航向差值的方差，依据方差动态调整航向补偿算法的修正强度，避免过度修正。利用所提算法进行了矩形实验和操场实验，终点定位误差小于1%，表明了所提方法的有效性。     

一种对GPS/INS组合导航的曳引式拉偏干扰

为解决GPS/INS组合导航对抗难题,提出一种针对GPS/INS组合导航的曳引式拉偏干扰方法。通过干扰设备产生欺骗干扰信号,使GPS/INS组合中的GPS接收机输出与其实际位置逐渐偏离的导航定位数据,当偏离误差无法被组合导航纠正时发生曳引式拉偏干扰。文中给出干扰方法的定义、信号形式、简化形式,通过半实物仿真实验证明其有效性并对其干扰效果进行了分析。