基于HMM的地形高度匹配算法

Terrain-aided navigation (TAN) uses terrain height variations under an aircraft to render the position estimate to bound the inertial navigation system (INS) error. This paper proposes a new terrain elevation matching(TEM) model, viz. Hidden-Markov-model(HMM) based TEM (HMMTEM) model. With the given model, an HMMTEM algorithm using Viterbi algorithm is designed and implemented to estimate the position error in INS. The simulation results show that HMMTEM algorithm can better improve the positioning precision of autonomous navigation than SITAN algorithm.     

Intelligently tuned wavelet parameters for GPS/INS error estimation

This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of th...     

星光折射定位辅助的惯性/星光全组合导航算法

常规的惯性/星光组合导航多基于姿态信息组合,对位置、速度修正效果较差.针对上述问题,在惯性/星光姿态组合算法基础上,通过引入基于星光折射原理后获得的天文定位信息,设计了惯性/星光姿态、位置全组合导航方案,并提出了基于地心惯性坐标系下的捷联惯性/星光全组合导航算法.仿真结果表明:由于利用星光折射间接敏感地平的方法,在系统中引入了位置相关的观测信息,可确保组合导航系统获得较高的位置、速度和姿态精度.     

The integration of strap-down INS and GPS based on adaptive error damping

The concept and results of integration of a strap-down inertial navigation system (INS) based on low-accuracy inertial sensors and the global positioning system (GPS) have been presented in this paper. This system is aimed for the purposes of navigation, automatic control, and remote tracking of land vehicles. The integration is made by the implementation of an extended Kalman filter (EKF) scheme for both the initial alignment and navigation phases. Traditional integration schemes (centralized and cascaded) are dominantly based on the usage of high-accuracy inertial sensors. The idea behind the suggested algorithm is to use low-accuracy inertial sensors and the GPS as the main source of navigation information, while the acceptable accuracy of INS is achieved by the proper damping of INS errors. The main advantage of integration consists in the availability of reliable navigation parameters during the intervals of absence of GPS data. The influence of INS error damping coefficients is different depending on the fact whether the moving object is maneuvering or is moving with a constant velocity at that time. It is proposed that INS error damping gain coefficients generally should take higher values always when GPS data are absent, while at the same time their values in the error model (EKF prediction phase) can be additionally adapted according to the actual values of vehicle acceleration. The analysis of integrated navigation system performances is made experimentally. The data are acquired along the real land vehicle’s trajectory while the intervals of absence of GPS data are introduced artificially on the parts characterized both by maneuver and by constant velocity.     

基于CenSurE特征的SAR/INS组合导航景象匹配算法

在SAR／INS组合导航系统中,所获取的SAR图像可能存在严重的斑点噪声和几何变形,对此,提出一种基于CenSurE特征的SAR／INS组合导航用景象匹配算法．该算法针对惯性组合导航的工作特点进行设计．首先提取CenSurE特征和垂直的SURF描述符,利用夹角余弦相似度量方法进行特征匹配;然后,采用分组一致采样算法和最小二乘精确匹配算法获取高精度的航向和位置偏差信息．景象匹配性能评价实验表明。在匹配适应性、匹配速度、精度和鲁棒性等方面,CenSurE特征都很优越,可以满足SAR／INS景象匹配导航系统匹配修正的高性能要求．     

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...     

一种基于MFC的转台自动测试系统

三轴转台在惯性测量组件和惯性导航系统的性能测试中起到了重要作用,而原先的转台标定实验需要测试人员通过上位机操纵转台运动到指定位置和速率状态,并手动操纵远程计算机采集惯性导航组件的实验数据.采用VC++中的MFC形成远程控制计算机界面,通过远程控制计算机直接控制三轴位置速率转台,并自动采集惯性导航组件的测试数据.经过远程控制计算机与转台形成闭环实验,测试系统可以完成预期目标.     

Identification for temperature model of accelerometer based on proximal SVR and particle swarm optimization algorithms

The impact of temperature on accelerometer will directly influence the precision of the inertial navigation system(INS).To eliminate the measurement error of accelerometer,this paper proposes a proximal support vector regression(PSVR)algorithm for generating a linear or nonlinear regression which requires the solution to single system of linear equations.PSVR is used to identify the static temperature model of the accelerometer.In order to improve the identifying performance,the kernel parameters and penalty factors of PSVR are optimized by the canonical particle swarm optimization(CPSO).The experiments under different temperature conditions were conducted.The experimental results show that the proposed PSVR can correctly identify the static temperature model of quartz flexure accelerometer and is more efficient than those of the standard SVR and least square algorithm.     

Real-Time Implementation of GPS Aided Low-Cost Strapdown Inertial Navigation System

This work details the study, development, and experimental implementation of GPS aided strapdown inertial navigation system (INS) using commercial off-the-shelf low-cost inertial measurement unit (IMU). The data provided by the inertial navigation mechanization is fused with GPS measurements using loosely-coupled linear Kalman filter implemented with the aid of MPC555 microcontroller. The accuracy of the estimation when utilizing a low-cost inertial navigation system (INS) is limited by the accuracy of the sensors used and the mathematical modeling of INS and the aiding sensors’ errors. Therefore, the IMU data is fused with the GPS data to increase the accuracy of the integrated GPS/IMU system. The equations required for the local geographic frame mechanization are derived. The direction cosine matrix approach is selected to compute orientation angles and the unified mathematical framework is chosen for position/velocity algorithm computations. This selection resulted in significant reduction in mechanization errors. It is shown that the constructed GPS/IMU system is successfully implemented with an accurate and reliable performance.     

移动USBL测距辅助的UUV协同导航定位方法

针对无人水下航行器(UUV) 导航精度受惯性导航(INS) 影响较大的问题, 本文提出一种基于无人水面船 (USV)携带超短基线(USBL)对UUV进行移动式辅助导航定位的方法. 文中以USV上高精度INS和全球导航卫星系 统(GNSS)组合后的导航结果作为基准, 利用USBL测量得到的USV和UUV相对位置和姿态信息, 结合UUV的INS误 差方程, 建立了UUV协同导航系统的状态方程和观测方程, 并基于自适应卡尔曼滤波方法对UUV状态进行滤波估 计. 仿真和湖上实验结果表明, 文中所提方法可有效提升UUV导航精度.     

组合导航系统INS/GNSS/SAR及其降阶模型性能的研究

介绍了高阶组合导航系统惯性导航系统 (INS) /全球导航卫星系统 (GNSS) /合成孔径雷达 (SAR)及其降阶模型. 降阶系统的卡尔曼滤波时间大大缩短了, 但其精度并未有大的降低. 仿真结果证明, INS/GNSS/SAR的降阶模型仍可为飞机提供满意的导航精度.     

Multisensor 3D Posture Determination of a Mobile Robot Using Inertial and Ultrasonic Sensors

This paper presents methodologies and techniques for fusing inertial and ultrasonic sensors to estimate the current posture of a mobile robot navigating over indoor uneven terrain. This new type of pose tracking system is developed by means of fusing an inertial navigation subsystem (INS) and an ultrasonic localization subsystem. Extended Kalman filtering (EKF)-based algorithm for integrating both the subsystems is proposed to obtain reliable attitude and position estimates of the vehicle and to eliminate the accumulation errors caused by wheel slippage and surface roughness. Experimental results are conducted to illustrate feasibility and effectiveness of the proposed system and method.     

神经网络在MEMS-IMU/GPS组合导航中的应用研究

针对基于MEMS传感器组成的INS/GPS组合中GPS信号缺失的情况下,系统误差瞬时增大,滤波迅速退化无法继续工作的问题,本文提出利用神经网络辅助INS/GPS导航系统以解决这一问题的方法.该方法首先建立系统模型,用组合导航的输入作为网络模型的输入,通过网络训练得到输出需要参数,结合卡尔曼滤波用于组合导航以继续使导航系统工作,仿真结果表明该方法可行和有效性的.     

基于GPS与全站仪的惯性导航部件安装校准技术

针对惯性导航系统的初始安装误差无法消除,且该误差会严重影响试验机的惯性导航系统导航精度的问题,研究了一项结合测绘型GPS和全站仪测量的惯性导航部件安装校准技术,通过GPS定位和全站仪测量并解算出试验机航向和真北方向之间的夹角,与试验机上惯导系统指示数据进行对比,从而达到快速准确校准惯导系统安装误差的目的.运用该技术进行...     

基于T-S模糊故障树方法的惯性导航系统可靠性评估

针对传统故障树模型在复杂系统可靠性评估中的局限性,引入T-S模糊故障树建模方法对惯性导航系统进行可靠性评估分析.在建立T-S模糊故障树的基础上,以模糊门算法表征事件之间的逻辑关系,确定了基于基本事件模糊概率和故障程度的复杂系统可靠性评估方法,并对事件进行了概率重要度和关键重要度分析.案例分析表明,T-S模糊故障树分析方...     

Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration

Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.     

Principles of Fusion of Inertial Navigation and Dynamic Vision

The possibility of fusion of navigation data obtained by two separate navigation systems (strap‐down inertial one and dynamic vision based one) is considered in this paper. The attention is primarily focused on principles of validation of separate estimates before their use in a combined algorithm. The inertial navigation system (INS) based on sensors of medium level quality has been analyzed on one side, while a visual navigation method is based on the analysis of a sequence of images of ground landmarks produced by an on‐board TV camera. The accuracy of INS estimations is being improved continuously by optimal estimation of a flying object's angular orientation while the visual navigation system offers discrete corrections during the intervals of presence of landmarks inside the camera's field of view. The concept is illustrated by dynamic simulation of a realistic flight scenario. © 2004 Wiley Periodicals, Inc.     

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.     

Expert system for an INS/DGPS integrated navigator installed in a Bell 206 helicopter

This paper presents a novel real-time application of fuzzy logic for an integrated navigation avionics suite. A knowledge-based system, which uses a fuzzy rule-base for a real-time INS/DGPS integrated navigation system on-board a Bell 206 helicopter, has been designed and developed. This knowledge base is developed in such a way as to detect aircraft maneuvering and tune the integration algorithm (Kalman filter) of the INS/DGPS system accordingly. The signal processing method developed for the integration of INS and DGPS data provides accurate navigation even during dynamic maneuvering of the aircraft, while taking advantage of low-cost modular equipment rather than costly inertial navigation systems.     

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.