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.     

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.     

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.     

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.     

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。     

基于不确定融合的GPS/INS 组合导航滤波算法

针对实际车载组合导航系统测量中不确定噪声的问题,提出一种基于不确定融合估计的GPS/INS组合导航滤波算法,建立了导航系统的状态方程和观测方程;通过多信源不确定融合估计,得到多传感器的等效测量值以及误差方差阵;对系统方程进行滤波处理,得到车辆的准确位置。车载系统的实测数据表明,不确定噪声下的融合估计结果优于独立白噪声假设下的融合估计,并验证了所提出算法的有效性和实用性。     

组合导航计算机高效多串口通讯技术实现

多串口通讯技术的可靠性、稳定性将直接影响到组合导航系统数据源的准确和安全,在工程控制上有一定困难,体现在数据竞争、数据保护和时间同步等问题的处理上;进行了基于WINDOWS NT的组合导航计算机中所采用的通讯技术的分析,重点分析了程序实现的核心问题自定义系统通讯类的设计工作,建立了基于Windows API函数和Pcomm函数库串行通信类设计方法,讨论了多线程控制及同步技术、多缓冲的数据保护技术、Pcomm函数使用方式、各数据时间同步修正等问题;实现了系统10串口的双向通讯功能,传输数据高效、稳定和可靠。     

Simplified ultra-tightly coupled BDS/INS integrated navigation system

Due to advantages such as low cost, small size, low weight, low power consumption, powerful capability of anti-jamming, and high dynamics, ultra-tightly coupled GNSS/MIMU integrated navigation systems are extensively used in projectile and precision-guided munitions. To overcome the nonlinearity of baseband signal processing and the complexity of the navigation filter, in this paper, we design a novel architecture for the ultra-tightly coupled (UTC) Beidou navigation satellite system (BDS)/INS integration based on the BDS B3 signal, including the combination of code phase and carrier phase dynamics baseband prefilter model, and the navigation filter navigation error propagation and measurement models. To eliminate the clock state components, i.e., the receiver clock offset and clock drift, the pseudorange, delta pseudorange, and delta pseudorange rate residuals obtained from baseband signal prefilters are differenced between two satellites. Based on the BDS/INS simulator, a test system for the UTC BDS/INS integrated navigation system is developed and UTC performance is evaluated. Some preliminary conclusions are formed about the effectiveness of the UTC BDS/INS integration approach.     

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.     

Fuzzy fault tree analysis based on T–S model with application to INS/GPS navigation system

A novel fault tree analysis (FTA) technique based on the Takagi and Sugeno (T–S) model is proposed in this paper. In the proposed technique, referred to as the TS-FTA, the events in the conventional FTA can be expressed in terms of fuzzy possibilities, and the gates that represent the relations among the top event and the primary events are replaced by T–S fuzzy gates derived from the T–S model. The magnitudes of the faults in the system are expressed in term of fuzzy variables. Since the proposed TS-FTA is derived from fuzzy logic and the T–S model, it can readily handle fuzzy information and uncertainties in the relationships among events. Therefore, the TS-FTA is suitable for systems where exact information on the fault probabilities of the components and the failure mechanisms are not available. The proposed TS-FTA is applied to analyze the reliability of an Inertial Navigation System and Global Position System (INS/GPS) integrated navigation system.     

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.     

基于计算机视觉/INS的无人机自主着陆组合导航研究

提出了一种基于单目视觉/INS组合导航的无人机自主着陆末端导航信息提取的新方法;它只需要检测跑道上的两个特征点再结合?导系统的姿态角信息,就可以直接解算出飞机相对于机场的位置矢量;由于直线具有明显的可观性,所以对于特征点较难提取的可以通过对两条跑道边缘线和一条着陆阈值线进行检测,然后取阈值线与跑道边缘线的交点作为特征点;依据成像的几何原理,对算法进行了详细的推导,最后通过着陆实验表明该方法的提取与转换是切实可行的。     

Magnetic compass fault detection method for GPS/INS/magnetic compass integrated navigation systems

This paper proposes a magnetic compass fault detection method for GPS/INS/Magnetic compass integrated navigation systems. The fault is assumed to be caused by the hard iron and soft iron effect and modeled as an abrupt change in the magnetic compass output. In order to detect the fault, a test statistic related with only azimuth error measurement is determined. When a fault is detected, the GPS/INS/Magnetic compass integrated navigation system is changed into a GPS/INS integrated navigation system mode. In order to show the validity of the proposed method, computer simulation and van testing are carried out. The simulation and van test results show that the proposed navigation system gives more accurate outputs than the GPS/INS/Magnetic compass without the proposed method.     

信息融合技术在INS/GPS/TAN/SMN四组合系统中的应用

主要介绍了组合导航系统中多传感器信息融合技术的国内外发展状况,对INS/GPS/T AN/SMN(惯性系统/卫星导航系统/地形辅助导航/景象匹配导航)组合系统中的多传感器信息 融合的层次结构与融合方法,首次提出INS/GPS/TAN/SMN组合导航系统信息融合的层次结构 ,并提出了多源图像融合制导的思想与方法．本文指出比较有应用前景的信息融合研究方法 是基于模糊逻辑、小波分析、神经网络等人工智能的新方法,以及这些新方法与传统的随机 类方法相结合的随机 人工智能的信息融合方法．     

基于极线约束的图像目标识别可信度评价方法

在图像辅助的惯性末制导系统中,提出了一种基于极线约束的图像目标识别可信度评价方法.当惯导系统给出的摄像机姿态或图像中目标的像素点位置存在误差时,为了平衡极线约束方程需要引入一个冗余量(residual),该冗余量可由惯导系统给出的摄像机两位置的相对运动及相应图像中目标点的像素位置计算得到.当惯导系统的姿态误差小于0.3°时,冗余量可以用来评价图像目标识别的可信度.理论推导及仿真实验证明,冗余量作为图像目标识别可信度的评价指标,具有良好的特性.     

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.     

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.     

Merits and limitations of using fuzzy inference system for temporal integration of INS/GPS in vehicular navigation

Most of the present vehicular navigation systems rely on global positioning system (GPS) combined with inertial navigation system (INS) for reliable determination of the vehicle position and heading. Integrating both systems provide several advantages and eliminate their individual shortcomings. Kalman filter (KF) has been widely used to fuse data from both systems. However, KF-based integration techniques suffer from several limitations related to its immunity to noise, observability and the necessity of accurate stochastic models of sensor random errors. This article investigates the potential use of adaptive neuro-fuzzy inference system (ANFIS) for temporal integration of INS/GPS in vehicular navigation. An ANFIS-based module named “P–δP” is designed, developed, implemented and tested for fusing INS and GPS position information. The fusion process aims at providing continuous correction of INS position to prevent its long-term growth using GPS position updates. In addition, it provides reliable prediction of the vehicle position during GPS outages. The P–δP module was examined using real navigation system data compromising an Ashtech Z12 GPS receiver and a Honeywell LRF-III INS. The proposed module proved to be successful as a modeless and platform independent module that does not require a priori knowledge of the navigation equipment utilized. Limitations of the ANFIS module are also discussed.     

自适应P值映射的惯性/天文角度组合导航算法

惯性/天文角度组合导航在应用于高动态飞行器时,动态飞行环境变更会导致星光角度观测量发生程度不等的偏差,使得常规组合滤波方法误差显著增大.为此,本文提出了基于P值映射的观测质量自主评估及自适应滤波方法,并应用于惯性/天文角度组合导航系统.该方法根据历年可见导航星情况分解冗余观测子集,再由P值度量其含有观测量偏差的显著性水平.在此基础上,通过遍历每颗导航星所隶属子集得到其观测量质量值,最后对惯性/天文角度组合滤波增益进行自适应调节.仿真结果表明,本文方法能够实现天文高度角观测质量的自主在线评估,有效提高星光观测质量下降情况下惯性/天文角度组合导航的精度和适应性.