UKF在INS/GPS直接法卡尔曼滤波中的应用

提出将Unscented卡尔曼滤波(UKF)用于INS/GPS组合导航系统的直接法卡尔曼滤波,避免了对非线性的系统状态方程进行线性化.以INS输出的导航参数及平台误差角等作为系统状态,惯导力学编排方程和姿态误差方程作为系统状态方程,GPS输出的导航参数作为量测,采用UKF方法对系统导航参数直接进行估计.仿真结果表明,UKF方法有效地解决了直接法卡尔曼滤波中系统状态方程的非线性问题,并使INS/GPS组合导航系统具有较高的导航定位精度.     

Constant-round zero-knowledge proofs of knowledge with strict polynomial-time extractors for NP

Barak and Lindell showed that there exist constant-round zero-knowledge arguments of knowledge with strict polynomial-time extractors.This leaves the open problem of whether it is possible to obtain an analogous result regarding constant-round zero-knowledge proofs of knowledge for NP.This paper focuses on this problem and gives a positive answer by presenting a construction of constant-round zero-knowledge proofs of knowledge with strict polynomial-time extractors for NP.     

基于因子图算法的INS/GPS信息滞后处理方法

针对全球定位系统（GPS）信息滞后导致惯性导航系统（INS）/GPS组合导航系统实时性差的问题,利用因子图算法可以在一个信息融合时刻处理各信息源不同时刻量测信息的特点,提出了一种INS/GPS信息滞后处理方法。在系统接收到GPS信息之前,因子图模型中只添加关于INS信息的因子节点,经增量推理求出组合导航结果,保证系统的实时性。待系统接收到GPS信息之后,再将关于GPS信息的因子节点添加到因子图模型中,修正INS误差,从而保证系统长时间高精度运行。仿真结果表明,当上一时刻实时导航状态量对INS误差修正效果随GPS信息滞后时间变长而逐渐变差时,可以采用上一时刻刚刚完成量测更新的导航状态量实现INS误差的有效修正。因子图算法在保证系统精度的前提下,避免了GPS信息滞后对INS/GPS组合导航系统实时性的不良影响。     

行人导航系统中航迹推算参数估计方法的研究

将GPS/DR组合导航技术应用到行人导航系统中。利用加速度计对行人步态进行判别,将神经网络用于行人步幅信息的标定。同时利用电子罗盘实现方位角的测量,并结合加速度计信息进行倾斜角度的误差补偿。现场测试结果表明,本文提出的DR参数估计方法不但提高参数估计的精度,而且能够满足行人导航定位的要求。     

GPS信号自适应抗干扰系统

针对GPS卫星信号易受干扰,不稳定的问题,提出INS/GPS组合导航抗干扰的方法并用硬件电路进行实现验证。给出了惯性器件的误差模型,采用松散组合方式,设计卡尔曼滤波器,取姿态、速度、位置的误差作为状态变量。提出以INS与GPS输出的东北天向速度误差作为滤波器观测量的方案。通过计算机的仿真和实验验证,对系统的精度进行了分析,证明该方案是可行的,实现实时滤波计算,并能满足导航的精度要求。     

路网辅助的车载INS/GPS组合导航方法

针对车载INS/GPS组合导航系统在GPS无效时精度迅速下降的问题,提出了将车辆行驶的路网约束作为虚拟传感器,采用多传感器数据融合的方式,与INS和GPS组成INS/GPS/路网组合导航系统.当GPS失效时,使用路网辅助INS.仿真结果表明,在GPS无效时间段,该方法能有效减小系统定位误差.     

基于MLP神经网络改进组合导航算法

为解决GPS信号失锁条件下,GPS/INS(inertial navigation system)组合导航系统解算精度降低甚至发散的问题,提出采用多层感知机神经网络(multilayer perceptron neural networks,MLPNN)来辅助组合导航系统。在GPS信号有效时对神经网络进行训练,在GPS失锁时利用神经网络对INS的导航误差进行修正,实现组合导航的连续性。跑车实验数据表明,GPS信号失锁360 s左右时,MLPNN辅助的组合导航方法最大位置误差在40 m以内,相对于纯惯性导航推算降低了35%的误差。该算法对GPS失锁后抑制GPS/INS组合导航系统误差快速发散、提高导航解算精度有显著效果。     

基于子集p值检验的惯性/ 卫星紧组合多星故障识别方法

传统的 RAIM 算法对多颗卫星同时发生故障的识别能力较差,鉴于此,提出一种基于子集 值检验的多卫星故障检测与识别方法.该方法在惯导信息辅助的基础上,采用 值检验对观测量子集进行显著性水平分析,通过决策得到各卫星观测量质量值,并进行故障识别和系统重构.仿真结果表明,该方法在可见星数?5时即可进行故障识别,且对于多颗卫星同时发生故障的情况具有较好的识别效果,提高了紧组合导航系统容错性和精度.     

TCM3电子罗盘的特性与应用

电子罗盘正广泛应用于各类导航定位系统中。介绍了TCM3电子罗盘的特性,详细说明了TCM3的控制编程和航向、俯仰、横滚等参数的读取,利用TCM3和GPS组合实现在农业机器人控制平台上的导航应用。经试验测试,TCM3在农业机器人导航平台上航向精度为±1℃,能够满足大部分精准农业应用的需要。     

组合导航计算系统的Simulink仿真

采用联邦卡尔曼滤波算法,分析了INS/GPS/ADS/CNS组合导航计算系统的数学模型,然后以MATLAB/Simulink为平台,构建了INS/GPS/ADS/CNS组合导航计算系统的仿真模型并进行了仿真。结果表明:建立的仿真模型能正确仿真组合导航计算系统的工作过程,并且具有良好的可视化效果,为组合导航系统的研究提供了有效的工具。     

基于SVR 的惯性/卫星组合导航系统故障诊断方法

在惯性/卫星组合导航系统中, 针对传统X² 检验法检测出故障但无法准确识别故障子系统的不足, 提出一种基于支持向量回归的故障诊断方法. 采用残差X² 检验法实时对组合导航系统进行故障检测, 并构建基于支持向量机的回归预测模型, 实现对惯性导航系统状态的预测; 根据系统模型输出和预测模型输出之差辅助进行惯性导航系统的故障判别, 诊断出系统故障源. 仿真结果表明, 所提出的方法能够快速准确地识别故障子系统, 并进行有效的系统隔离和重构, 从而使组合导航系统的性能得到保障.

     

多导航传感器的数据融合处理方法研究

在舰船导航过程中为克服单一模型的卡尔曼滤波器对真实系统状态参数发生变化时造成滤波误差过大甚至发散的现象,将多模自适应控制用于导航数据融合处理方法中,设计了组合导航系统的多模型自适应卡尔曼滤波器,通过数字仿真将单一模型的INS/GPS/Doppler组合导航系统与多模自适应控制的组合导航系统的性能进行了比较,表明了多模自适应控制在组合导航系统中可以改善系统的瞬态响应和覆盖大范围的参数不确定性,提高了组合系统的导航精度.     

Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation

The last two decades have shown an increasing trend in the use of positioning and navigation technologies in land vehicles. Most of the present navigation systems incorporate global positioning system (GPS) and inertial navigation system (INS), which are integrated using Kalman filtering (KF) to provide reliable positioning information. Due to several inadequacies related to KF-based INS/GPS integration, artificial intelligence (AI) methods have been recently suggested to replace KF. Various neural network and neuro-fuzzy methods for INS/GPS integration were introduced. However, these methods provided relatively poor positioning accuracy during long GPS outages. Moreover, the internal system parameters had to be tuned over time of the navigation mission to reach the desired positioning accuracy. In order to overcome these limitations, this study optimizes the AI-based INS/GPS integration schemes utilizing adaptive neuro-fuzzy inference system (ANFIS) by implementing, a temporal window-based cross-validation approach during the update procedure. The ANFIS-based system considers a non-overlap moving window instead of the commonly used sliding window approach. The proposed system is tested using differential GPS and navigational grade INS field test data obtained from a land vehicle experiment. The results showed that the proposed system is a reliable modeless system and platform independent module that requires no priori knowledge of the navigation equipment utilized. In addition, significant accuracy improvement was achieved during long GPS outages.     

动基座条件下组合导航初始对准研究

初始对准是影响惯性导航系统的导航精度的重要因素,通过对惯性导航(INS)/全球定位系统(GPS)组合导航系统初始对准进行研究,利用GPS信息来辅助INS进行初始对准,来提升初始对准的精度和对准时间.在粗对准阶段,利用载机主惯导对子惯导进行装订,完成子惯导的粗对准;在精对准阶段,有效地利用GPS信息,采用速度匹配方式,并设计了组合对准滤波器,进行仿真验证.仿真结果表明:组合导航系统的初始对准能在15 s内完成对准,且能够达到7mrad的精度.     

一种模糊QR滤波及在组合导航中的仿真研究

总结了常用的自适应滤波的方法，并提出了一种基于模糊逻辑的自适应卡尔曼滤波技术，用模糊逻辑自适应推理器来“在线”修正卡尔曼滤波系统噪声协方差Q和测量噪声协方差R，从而使滤波器不断执行最优估计。仿真结果表明该方法可以提高GPS／INS组合导航系统的精度和可靠性。     

基于回算机制的GPS/INS时间延迟处理方法

针对组合导航中由于GPS时延而导致的定位精度下降的问题,提出了一种适用于低成本制导炸弹的时延处理方法.该方法在GPS数据输出延时过程中,利用预设存储器存储数据,基于回算机制完成GPS信息更新时刻的数据融合、GPS数据输出时刻的导航输出,减小时间不同步对组合导航数据融合的影响.该回算机制可控制计算量,不增加程序复杂性,适用于GPS数据丢失或异常等多种情况.针对回算机制提出了一种工程实践中的计算优化算法,在回算时取消卡尔曼滤波计算中的时间更新环节.该计算优化可节约回算过程的计算时间,避免整体数据延迟,同时不影响导航定位精度,可满足短时间内的低成本组合导航系统要求.靶试结果验证,时延处理方法及计算优化算法适用于低成本制导炸弹,具有一定的工程实用性.     

Enhancing positioning accuracy of GPS/INS system during GPS outages utilizing artificial neural network

Integrated global positioning system and inertial navigation system (GPS/INS) have been extensively employed for navigation purposes. However, low-grade GPS/INS systems generate erroneous navigation solutions in the absence of GPS signals and drift very fast. We propose in this paper a novel method to integrate a low-grade GPS/INS with an artificial neural network (ANN) structure. Our method is based on updating the INS in a Kalman filter structure using ANN during GPS outages. This study focuses on the design, implementation and integration of such an ANN employing an optimum multilayer perceptron (MLP) structure with relevant number of layers/perceptrons and an appropriate learning. As a result, a land test is conducted with the proposed ANN + GPS/INS system and we here provide the system performance with the land trials.     

基于FPGA的数字磁罗盘开发

针对电子罗盘的定位导航应用,开发了一种基于现场可编程门阵列(FPGA)作为微控制器的数字磁罗盘,具有成本低、速度快、功耗小的优点。FPGA负责完成对数字磁阻传感器和数字加速度计的数据采集、方位角计算、LED显示以及串口输出等处理。对罗盘的误差来源进行了分析,重点对其中的硬干扰误差进行了补偿修正。为了验证设计效果,对数字磁罗盘实物进行了测试,测试结果表明:磁罗盘在进行误差补偿后,精度范围能够达到1°～2°左右,且长期运行时稳定,可适用于普通导航领域。     

Amplified wavelet-ANFIS-based model for GPS/INS integration to enhance vehicular navigation system

Inertial navigation system (INS) relying on gyroscopes and accelerometers has been recently utilized in land vehicles. These INS sensors are integrated with Global Positioning System (GPS) to provide reliable positioning solutions in case of GPS outages that commonly occur in urban canyons. The major inadequacies of INS navigation sensors are the high noise level and the large bias instabilities that are stochastic in nature. The effects of these inadequacies manifest themselves as large position errors during GPS outages. Wavelet analysis is a signal processing method which is recently auspicious by many researchers due to its advantageous adaptation to non-stationary signals and able to perform analysis in both time and frequency domain over other signal processing methods such as the fast Fourier transform in some fields. This research proposes the utilization of wavelet de-nosing to improve the signal-to-noise ratio of each of the INS sensors. In addition, a neuro-fuzzy module is used to provide a reliable prediction of the vehicle position during GPS outages. The results from a road test experiment show the effectiveness of the proposed wavelet—neuro-fuzzy module.     

基于结构分解的GPS/INS组合导航粒子滤波技术

针对粒子滤波应用于GPS/INS组合导航系统时难以保证滤波实时性的问题,提出一种基于线性/非线性结构分解的改进粒子滤波算法.改进算法对状态方程进行线性/非线性结构分解,分别采用重点采样和线性卡尔曼方式进行一步预测递推,充分发挥粒子滤波和卡尔曼滤波的特点,有效降低了粒子滤波的计算量,在保证GPS/INS组合导航系统滤波精度的条件下提高了组合滤波的实时性.