利用神经网络预测的GPS/SINS组合导航系统算法研究

提出了一种基于神经网络预测的GPS/SINS组合导航系统算法。GPS信号可用时,该算法分别将惯性传感器的输出以及卡尔曼滤波器的输出信息作为神经网络的输入及理想输出信息,并进行在线训练;当GPS信息失锁时,利用已经训练好的神经网络预测各导航参数误差,并校正SINS。地面静态实验与动态跑车实验结果证明了该方法的可行性与有效性。     

三星定位性能分析及其与SINS的融合方法

针对三星定位技术,本文首先分析其定位算法,并推导其误差分布模型。同时根据输出信息的特点,给出三星定位与SINS（捷联惯性导航系统）进行组合的算法。然后,对三星定位系统的定位误差特性进行仿真试验,从而揭示该系统的误差分布特性。最后,对三星定位／SINS组合系统进行半实时的静态与动态试验,组合结果证明该组合系统定位精度良好,在中纬度区域接近于GPS的精度。     

GPS短时失效条件下SINS/GPS组合导航数据处理方法

在SINS/GPS组合导航实际应用中GPS短时失效的情况难以避免,这会导致组合导航的效果下降。针对该问题,本文提出了基于偏最小二乘PLSR辅助高斯过程回归GPR的SINS/GPS组合导航的无味四元数估计器USQUE,以解决组合导航中的GPS短时失效问题。该方法以PLSR估计的位置误差作为输入,以GPS提供的位置信息作为输出对GPR进行训练。在组合导航系统出现GPS短时失效后,使用通过PLSR辅助粒子群算法优化超参数的GPR直接对辅助导航设备的位置进行预测,作为USQUE算法的量测量,从而使得USQUE算法可以正常进行量测更新。在实验中,使用车载MEMS/GPS数据,将PLSR-GPR-USQUE,PLSR-USQUE与隔离量测量的USQUE算法组合导航的效果进行比较。实验结果表明,在GPS短时失效的情况下,PLSR-GPR-USQUE具有良好的估计精度。     

LSTM辅助车载GNSS/INS组合导航算法及性能分析

针对车载GNSS/惯性导航系统(inertial navigation system,INS)组合导航系统在GNSS信号失锁时定位精度下降甚至发散的问题，提出了一种长短期记忆(long short-term memory,LSTM)神经网络辅助组合导航的算法来提高定位精度，实现可靠连续稳定的定位.通过移动集成平台进行实验，结果表明：当GNSS信号失锁30 s时，LSTM辅助组合导航系统在东(east,E)、北(north,N)方向的位置误差最大值分别降低了77.45%、17.39%，均方根误差(root mean square error,RMSE)分别降低了79.53%、42.36%；当GNSS信号失锁100 s时，LSTM辅助GNSS/INS在E、N、天顶(up,U)三个方向上的位置误差最大值分别降低了60.07%、98.30%、84.65%,RMSE分别降低了61.96%、97.98%、84.65%. LSTM辅助较大地提升了车载GNSS/INS组合导航系统的导航性能.     

一种结合H~∞滤波的SINS/GPS组合无人机定位算法

高动态复杂环境下的无人机移动定位中,捷联式惯性导航(SINS)系统存在误差漂移,全球定位系统(GPS)可能发生信号失锁等问题。本文针对无人机定位方法进行研究,基于其速度信息和位置信息,给出了一套无人机测算目标物精准位置信息的方法。该方法结合GPS定位技术与SINS定位技术,采用H~∞滤波算法对量测数据进行融合,使两定位技术优势互补,进一步降低了定位误差,特别适合于长时间导航定位。试验结果表明:将H~∞滤波引入组合式导航定位时,既能提高滤波的收敛性,又可以保持较高的定位精度。     

融合TOA/AOD的5G/SINS紧组合导航定位算法分析

针对5G定位和捷联惯性导航单一定位方式的可靠性和定位精度较差的问题,本文以扩展卡尔曼滤波为基础,提出了融合5G信号到达时间和信号离开角的5G/SINS紧组合导航算法。该算法首先利用惯性传感器输出信息解算用户的位置、速度和姿态,在此基础上利用已知的基站坐标反算出一组虚拟的5G观测值,然后使用该观测值和实际的5G测量值建立统一的观测方程进行滤波解算。仿真试验结果表明,5G/SINS紧组合的定位成功率可达99%以上,且能够有效改善惯导航位推算的发散问题,其定位精度相比单纯的5G定位有了大幅提高,相比5G/SINS松组合受基站数量和基站几何分布的影响较小。融合TOA/AOD的5G/SINS紧组合导航的定位结果有超过99%的历元在3 m以内。在5G观测值中存在系统误差时,5G/SINS紧组合的定位表现优于5G定位和5G/SINS松组合导航。     

PPP/SINS紧耦合系统仿真的研究与分析

在捷联惯导(SINS)和GPS卫星接收机进行紧耦合的研究中,采用差分定位进行紧耦合的方法比较成熟,而关于精密单点定位(PPP)与捷联惯导紧耦合的研究还比较少。本文对精密单点定位与捷联惯导紧组合系统进行了Matlab仿真,利用数学解析的方法产生机载运动轨迹,通过设置系统的参数,获得仿真SINS和GPS数据;然后,通过PPP/SINS紧组合系统的仿真程序解算,将定位结果与PPP的结果比较,表明PPP/SINS紧组合导航定位的结果比PPP的精度和可靠性好,而且收敛的速度更快,同时也验证了算法的正确性。最后,分析了不同等级惯导对定位精度的影响。     

超宽带/GNSS/SINS融合定位模型与方法研究EI北大核心CSCD

全球卫星导航系统(GNSS)与捷联惯性导航系统(strapdown inertial navigation system,SINS)的组合可在室外环境或卫星信号短暂失锁的条件下提供连续、可靠的定位服务。但针对室内或弱GNSS信号区域,组合系统的服务受限;超宽带(ultra-wideband,UWB)系统以其可提供厘米级的理论测距精度且布设方便的优势,可为该区域提供有效的测距信息。论文以车载试验平台为依托,分别就UWB/SINS、GNSS/SINS和UWB/GNSS/SINS 3类室内外定位模型及相应的模型参数估计方法展开研究,主要研究成果如下。     

BDS-3/GPS在遮挡环境下定位性能分析

针对在全球卫星导航系统(GNSS)信号易遮挡地区,单一系统可见卫星数较少,定位性能不理想甚至难以满足定位需求的问题,分析北斗三号(BDS-3)在不同区域遮挡环境下对定位性能的改善. 通过全球不同区域MGEX(Multi-GNSS Experiment)监测站的观测数据,采用GPS、BDS-3、BDS-3/GPS组合定位三种模式,在不同模拟遮挡环境下进行伪距单点定位,分析了各模式下可见卫星颗数、历元利用率、几何精度衰减因子(GDOP)值和定位精度. 结果表明:在北半球区域,相较于其他方向遮挡,GPS模式在低纬度地区南面遮挡的定位稳定性和精度最高,在中高纬度地区北面遮挡的定位稳定性和精度最高,BDS-3和BDS-3/GPS组合模式在低纬度地区各方向遮挡定位精度相当,在中纬度和中高纬度地区,北面遮挡的精度明显优于其他方向遮挡的定位精度. BDS-3/GPS组合定位模式,大大增加了可见卫星颗数,历元利用率提高,卫星空间几何结构改善,GDOP值降低,稳定性和定位精度明显优于单系统.      

微弱信号下基于模糊度搜索的辅助式GPS定位算法研究

针对微弱信号下GPS接收机无法测量得到完整信号发射时刻的问题,提出了一种基于模糊度搜索的辅助式GPS定位算法。基于该算法接收机不需要位同步、帧同步和解调导航电文,仅对GPS信号进行伪码相位测量,在获取卫星星历、卫星钟差参数等辅助信息的基础上完成定位解算。论文从数学上严格推导了消除信号发射时刻模糊度的条件,并对五颗以上的观测卫星建立了定位解算方程,给出了算法流程。利用实测数据仿真验证了该算法的有效性。比较表明,该算法的定位精度与常规GPS定位算法(信号发射时刻不存在模糊度)相当。     

改进Sage-Husa算法在飞机组合导航中的应用

针对机载组合导航系统,考虑不同飞行阶段的气压高度,提出一种改进的Sage-Husa自适应滤波算法,以提高组合导航系统定位精度. 该算法通过引入气压高度,实时计算并修正滤波异常判定的调节因子,以满足飞机不同飞行阶段的滤波需求. 通过捷联式惯性导航系统(SINS)、全球卫星导航系统(GNSS)定位误差特性仿真、卡尔曼滤波组合算法仿真、以及改进的Sage-Husa自适应滤波算法仿真,并对相关结果进行比较验证. 仿真结果表明,改进Sage-Husa自适应滤波可以提高滤波的自适应性,降低组合导航系统定位误差,取得较好的效果.      

Integrated Navigation System and Experiment of a Low-Cost and Low-Accuracy SINS/GPS

When SINS (strap-down inertial navigation system) is combined with GPS, the observability of the course angle is weak. Although the course angle error is improved to some extent through Kalman filtering, the course angle still assumes a divergent trend. This trend is aggravated further when using low-cost and low-accuracy SINS. In order to restrain this trend, a method that uses AHRS to substitute for SINS course angle information is put forward aimed at the hardware component characteristic of the low-cost and low-accuracy SINS including AHRS (attitude and heading reference system) and IMU (inertial measurement unit). Real static and dynamic experiments show that the method can restrain the divergent trend of the navigation system angle effectively, and the positioning accuracy is high.     

Integrated navigation system and experiment of a low-cost and low-accuracy SINS/GPS

When SINS (strap-down inertial navigation system) is combined with GPS, the observability of the course angle is weak. Although the course angle error is improved to some extent through Kalman filtering, the course angle still assumes a divergent trend. This trend is aggravated further when using low-cost and low-accuracy SINS. In order to restrain this trend, a method that uses AHRS to substitute for SINS course angle information is put forward aimed at the hardware component characteristic of the low-cost and low-accuracy SINS including AHRS (attitude and heading reference system) and IMU (inertial measurement unit). Real static and dynamic experiments show that the method can restrain the divergent trend of the navigation system angle effectively, and the positioning accuracy is high.     

视觉辅助下的室内惯导位姿修正

惯性导航系统可以短期内提供连续的高精度信息，但是误差会随时间增大，不能长期独立工作。而在大型仓库、地下停车场等室内卫星信号薄弱的场景中，传统的惯导+卫星组合方法也不再适用。针对该问题，本文提出了一种视觉与惯导组合定位的方法。本文研究的惯导+视觉组合的定位方法中，采用基于合作目标的单目视觉定位方法对惯导误差进行修正。对于惯导误差的修正方法，本文利用视觉定位的位姿信息建立量测方程，进行卡尔曼滤波，并选取合适的试验设备，通过实际试验对比验证了该算法对惯导系统误差的修正具有良好的效果。     

Comparing DGPS corrections prediction using neural network, fuzzy neural network, and Kalman filter

Position information obtained from standard global positioning system (GPS) receivers has time variant errors. For effective use of GPS information in a navigation system, it is essential to model these errors. A new approach is presented for improving positioning accuracy using neural network (NN), fuzzy neural network (FNN), and Kalman filter (KF). These methods predict the position components’ errors that are used as differential GPS (DGPS) corrections in real-time positioning. Method validity is verified with experimental data from an actual data collection, before and after selective availability (SA) error. The result is a highly effective estimation technique for accurate positioning, so that positioning accuracy is drastically improved to less than 0.40 m, independent of SA error. The experimental test results with real data emphasize that the total performance of NN is better than FNN and KF considering the trade-off between accuracy and speed for DGPS corrections prediction.     

多关节深海航行器组合导航算法

为解决多关节水下航行器定位问题,本文提出了基于捷联惯导+航位推算的组合导航算法。该方法利用捷联惯导系统测量航行器的位置,采用航位推算的方法得到航行器在下一时刻的位置,然后将测得的信息采用Kalman滤波处理,得到高精度的位置信息。通过Matlab/SIMULINK平台对比其他两种单一导航系统的性能,仿真结果表明,采用捷联惯导+航位推算算法时,位置误差可控制在5 m以内,满足多关节水下航行器的定位需求。     

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.     

基于卡尔曼滤波和改进DBSCAN聚类组合的GPS定位算法

实时获取智能移动终端的地理位置信息是增强现实（AR）实景智能导航系统实现的关键,为了提高智能终端GPS定位的精度,提出了一种基于卡尔曼滤波与改进的具有噪声的基于密度的聚类方法（DBSCAN）结合的GPS组合定位优化方法. 通过对GPS系统采集到的位置坐标数据进行卡尔曼滤波,去除较大的数据波动,控制定位误差范围,采用DBSCAN聚类算法进行分类去噪和二次聚类,对类中数据求得算术均值和类间数据总数进行加权求重心,确定位置坐标. 实验结果表明,提出的算法能有效提高GPS单点定位精度,减少定位误差,同时很好地满足了AR实景智能导航系统实时性和鲁棒性的要求.      

State transformation extended Kalman filter for GPS/SINS tightly coupled integration

Extended Kalman filter (EKF) is a widely used estimator for integrated navigation systems, and it works well in general situations. However, in adverse conditions such as partially observable environments and highly dynamic maneuvers, the performance of the traditional EKF-based strap-down inertial navigation system (SINS)/GPS integrated navigation system is easily to be affected by the dynamic changes of the specific force, thus leading to the problem of error covariance inconsistency. Though the inconsistency problem can be overcome to some extent if the system matrix, the states and the error covariance matrix are propagated as fast as possible in the SINS calculation rate, the problem cannot be fully solved. State transformation extended Kalman filter (ST-EKF) mechanization, with a new converted velocity error model for the SINS, is proposed, which can also be used to solve the inconsistency problem. In the ST-EKF, the specific force vector in the system error model is replaced by the nearly constant gravity vector for local navigation. Since the propagation and the updating of the ST-EKF can be executed simultaneously in the updating interval, the computation cost is greatly reduced compared with the traditional EKF. Experiments for the GPS/SINS tightly coupled navigation, including linear vibration Monte Carlo test and an unmanned aerial vehicle flight test, are implemented to evaluate the performance of the proposed ST-EKF. The results show that the proposed ST-EKF has superior performance to the traditional EKF, especially in partially observable situations.