Using Allan variance to improve stochastic modeling for accurate GNSS/INS integrated navigation

MEMS-based integrated system of a global navigation satellite system (GNSS) and an inertial navigation system (INS) has been widely used in various navigation applications. However, such integration encounters some major limitations. On the one hand, the noisy MEMS-based INS undermines the accuracy with time during the frequently occurring GNSS outages caused by signal blockage or attenuation in certain situations such as urban canyon, tunnels, and high trees. On the other hand, the model mismatch between actual GNSS error and the assumed one would also degrade the obtained accuracy even with continuous GNSS aiding. To improve the overall performance for GNSS/MEMS-INS, better error models can be obtained using Allan variance (AV) analysis technique for modeling inertial sensor errors instead of the commonly recommended auto-regressive processes, and on the other hand, the measurement update in Kalman filter is improved using innovation filtering and AV calculation. The performance of each method and the combined algorithm is evaluated by a field test with either differential GNSS (DGNSS) or single-point positioning (SPP) as external aid. In addition to the considerable navigation enhancement brought by each method, the experimental results show the combined algorithm accomplishes overall accuracy improvements by about 18% (position), 8% (velocity), and 38% (attitude) for integration with DGNSS, and by about 15% (position), 75% (velocity), and 77% (attitude) for that with SPP, compared with corresponding traditional counterparts.     

一种GPS/GLONASS/INS数据融合算法

针对卫星导航系统和惯性导航系统(INS)的不同特性,提出了一种GPS/GLONASS/INS数据融合算法。采用差分自适应检测算法、改进码平均相位算法以及位置联合解算方法实现了GPS/GLONASS数据融合,借助于改进的粒子滤波器、INS误差模型建立系统状态方程和观测方程,完成GPS/GLONASS系统速度值和INS系统速度值数据融合,提高组合导航系统精度和可靠性。使用真实数据对数据融合算法性能进行仿真分析,结果表明所设计算法是有效的,能够处理非线性非高斯条件下的滤波估计,提高滤波精度和系统可靠性。     

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.     

Adaptive GPS/INS integration for relative navigation

Relative navigation based on GPS receivers and inertial measurement units is required in many applications including formation flying, collision avoidance, cooperative positioning, and accident monitoring. Since sensors are mounted on different vehicles which are moving independently, sensor errors are more variable in relative navigation than in single-vehicle navigation due to different vehicle dynamics and signal environments. In order to improve the robustness against sensor error variability in relative navigation, we present an efficient adaptive GPS/INS integration method. In the proposed method, the covariances of GPS and inertial measurements are estimated separately by the innovations of two fundamentally different filters. One is the position-domain carrier-smoothed-code filter and the other is the velocity-aided Kalman filter. By the proposed two-filter adaptive estimation method, the covariance estimation of the two sensors can be isolated effectively since each filter estimates its own measurement noise. Simulation and experimental results demonstrate that the proposed method improves relative navigation accuracy by appropriate noise covariance estimation.     

A JTIDS/INS/DGPS navigation system with pseudorange differential information transmitted over Link-16: design and implementation

As the battle environment becomes more complicated, the demand for higher accuracy and better anti-jam capacity of navigation has been increasing. The conventional JTIDS/INS/GPS integrated navigation cannot meet the demands of certain situations such as precision strike and formation flight. A new system that introduces the differential GPS into JTIDS/INS/GPS integration system is proposed to improve the navigation performance in the modern combined operations. In this system, the differential information of DGPS is transmitted through the communication data link of Link-16. As a result, the system resources are efficiently utilized and the controllability and anti-jam performance of the system are significantly enhanced. A hybrid slot allocation protocol (HSAP) that combines a static slot allocation algorithm and a dynamic slot allocation algorithm and the corresponding source-chosen mechanisms are proposed. The performance of the JTIDS/INS/GPS integration navigation using the differential GPS information from one or multiple base stations is studied and compared with that of the system without using the differential GPS information. Furthermore, the performance of the integration navigation using HSAP is compared with that of the system using static slot allocation algorithm. We show that navigation accuracy based on the differential GPS is improved, and using HSAP also leads to higher localization accuracy.     

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。     

New jump trajectory determination method using low-cost MEMS sensor fusion and augmented observations for GPS/INS integration

Objective information on athletic maneuvers for performance evaluation has become highly desired in sports such as skiing, snowboarding, and mountain biking. Body-mounted devices, incorporating low-cost microelectromechanical, inertial navigation units, and global positioning system (GPS) receivers, to calculate sport-specific key performance variables (KPVs) and provide real-time feedback, are now commercially available. However, algorithms implemented for such purposes still lack accuracy and power efficiency. A new GPS/INS (inertial navigation system) integration algorithm is proposed to determine the trajectory of an athlete executing jumps while skiing, snowboarding, mountain biking etc. KPVs, such as jump horizontal distance, vertical height, and drop, are calculated from the trajectory. A new sensor error compensation scheme is developed using sensor fusion and linear Kalman filters (LKF). The LKF parameters are varied to address the fluctuating dynamics of the athlete during a jump. The extended Kalman filter used for GPS/INS integration has an observation vector augmented with sensor error measurements derived from sensor fusion. The performance of the proposed algorithm is evaluated through experimental field tests. For the determination of jump horizontal distance, height, and drop, the proposed algorithm has errors of 14.3 cm (5.5 %), 1.6 cm (38 %), and 6.7 cm (9.4 %), respectively. Errors in KPVs for a set of jumps were first determined with respect to the true KPVs, and then the errors for all the jumps were averaged to calculate the absolute and percentage errors. The accuracy achieved is deemed to fulfill the expectations of both recreational and professional athletes.     

支持向量回归辅助的GPS/INS组合导航抗差自适应算法

卡尔曼滤波残差分量受到观测信息误差和动力学模型误差的双重影响,由于GPS/INS松耦合导航系统中观测值个数少于状态参数个数,导致异常检测时难以正确区分误差来源,提出一种支持向量回归辅助的组合导航抗差自适应算法。该算法克服了组合系统观测信息无冗余情况下异常检测的局限性,基于遗传算法参数寻优构建回归模型,预测次优观测值,结合整体异常检验法自主选择抗差或自适应滤波,进而调整观测值或动力学模型对导航解的贡献,进行导航预报。最后利用车载实测数据进行验证,结果表明：该算法能够对存在的异常故障智能判定,减弱观测值异常和动力学模型误差影响,保证组合导航精度,提高导航解可靠性。     

Avoiding numerical stability problems of long duration DGPS/INS Kalman filters

A current pursuit of the geodetic community is the optimal integration of differential GPS (DGPS) and inertial navigation system (INS) data streams for precise and efficient position and gravity vector surveying. Therein a complete INS and multiple-antenna GPS receiver payload, mounted on a moving platform, is used in conjunction with a network of ground-fixed single antenna GPS receivers. This paper presents a complete, GPS-based, external updating measurement model for the applicable Kalman filter. The model utilizes four external observation types for every GPS satellite in-view: DGPS range differences, single phase differences, and single phase-rate differences; as well as the mobile, multipleantenna GPS receiver's measurement of theerrors in the INS's estimate of the phase difference between any two vehicle-borne GPS antennae. Although not widely conveyed in the geodetic world, the inertial navigation community has long known that traditional Kalman filter covariance propagation recurrences are inherently unstable when such highly accurate external updates are repeatedly applied (every 1 second) over long time durations. A hybrid square root covariance/U — D covariance factorization approach is a numerically stable alternative and is reviewed herein. The hybrid makeup of the algorithm is necessitated by the correlated nature of the fourth type of GPS external measurement listed above (each vehicle-borne GPS antenna formstwo baselines). Such measurement correlations require a functional transformation of the overall external updating model to permit the multiple updates (simultaneously available at each updating epoch) to be sequentially (and efficiently) processed. An appropriate transformation is given. Stable covariance propagation relationships are presented and the transformed Kalman gain is also furnished and its use in the determination of the externally updated error states is discussed. Specific DGPS/INS instabilities produced by the traditional recurrences are displayed. The stable alternative method requires about 25% more CPU time than the traditional Kalman recurrences. With the ever-increasing computational speeds of microprocessors, this added CPU time is of no real concern.     

神经网络辅助的GPS/INS组合导航故障检测算法

针对GPS/INS松组合导航系统观测信息无冗余,而且观测信息可能存在故障的情形,提出一种神经网络辅助的组合导航故障检测算法。该算法克服了基于模型的故障检测算法受模型误差影响的局限性;能够自动地对观测信息进行故障的检测、定位和剔除;能够基于故障检测后可靠的观测信息进一步调整动力学模型信息对导航解的贡献;能够在GPS失锁时,较好地进行导航预报。最后利用车载实测数据进行验证,结果表明该算法能够很好地从模型误差中分离出观测信息含有的故障信息,降低了故障检测算法存在的虚警率,避免故障信息对导航解的影响;且GPS失锁时,神经网络的预报输出在一定程度上能够进一步提高导航解的精度。     

GNSS+INS组合定位在矿山无人驾驶卡车的应用

面向矿山无人驾驶卡车场景,针对GNSS定位不连续且容易被干扰、INS存在累计误差的缺点,本文提出了一种基于GNSS+INS组合的导航算法,该算法融合了两种算法的优点,提高了定位的精度和可靠性。分别将RTK算法和组合导航算法结果与开源软件RTKLIB和NovAtel板载输出结果对比。试验结果表明,本文算法在精度上与NovAtel板载输出结果基本持平,明显优于RTKLIB软件。本文算法平面和高程误差均值及STD均优于5 cm,姿态误差均值和STD优于1°,可以满足矿用无人驾驶卡车的定位精度需求。     

New results in airborne vector gravimetry using strapdown INS/DGPS

A method for airborne vector gravimetry has been developed. The method is based on developing the error dynamics equations of the INS in the inertial frame where the INS system errors are estimated in a wave estimator using inertial GPS position as update. Then using the error-corrected INS acceleration and the GPS acceleration in the inertial frame, the gravity disturbance vector is extracted. In the paper, the focus is on the improvement of accuracy for the horizontal components of the airborne gravity vector. This is achieved by using a decoupled model in the wave estimator and decorrelating the gravity disturbance from the INS system errors through the estimation process. The results of this method on the real strapdown INS/DGPS data are promising. The internal accuracy of the horizontal components of the estimated gravity disturbance for repeated airborne lines is comparable with the accuracy of the down component and is about 4–8 mGal. Better accuracy (2–4 mGal) is achieved after applying a wave-number correlation filter (WCF) to the parallel lines of the estimated airborne gravity disturbances.     

GPS/INS navigation precision and its effect on airborne radio occultation retrieval accuracy

An airborne radio occultation (RO) system has been developed to retrieve atmospheric profiles of refractivity, moisture, and temperature. The long-term objective of such a system is deployment on commercial aircraft to increase the quantity of moisture observations in flight corridors in order to improve weather forecast accuracy. However, there are several factors important to operational feasibility that have an impact on the accuracy of the airborne RO results. We investigate the effects of different types of navigation system noise on the precision of the retrieved atmospheric profiles using recordings from the GNSS Instrument System for Multistatic and Occultation Sensing (GISMOS) test flights, which used an Applanix POS/AV 510 Global Positioning System (GPS)/Inertial Navigation System (INS). The data were processed using a carrier phase differential GPS technique, and then the GPS position and inertial measurement unit data were combined in a loosely coupled integrated inertial navigation solution. This study quantifies the velocity precision as a function of distance from GPS reference network sites, the velocity precision with or without an inertial measurement unit, the impact of the quality of the inertial measurement unit, and the compromise in precision resulting from the use of real-time autonomous GPS positioning. We find that using reference stations with baseline lengths of up to 760?km from the survey area has a negligible impact on the retrieved refractivity precision. We also find that only a small bias (less than 0.5% in refractivity) results from the use of an autonomous GPS solution rather than a post-processed differential solution when used in an integrated GPS/INS system. This greatly expands the potential range of an operational airborne radio occultation system, particularly over the oceans, where observations are sparse.     

基于B1C新信号的BDS/INS深组合改进的跟踪环路设计

针对北斗卫星导航系统/惯性导航系统(BDS/INS)的深组合定位系统,提出了一种利用惯性导航系统(INS)辅助B1C正交分量的信号跟踪算法,以解决定位过程中信号较弱致深组合定位系统失锁的问题. 该算法使用了考虑INS数据的卡尔曼滤波算法,并同时利用导频分量和数据分量构成本地码,对信号进行跟踪. 由该算法对实测数据计算,并利用传统算法进行对比,可以得出在弱信号的环境下跟踪环路较为稳定,伪距精度较高.      

Integrated data processing method for GPS and INS field test over Rocky Mountain

The method of integrated data processing for GPS and INS(inertial navigation system) field test over the Rocky Mountains using the adaptive Kalman filtering technique is presented. On the basis of the known GPS outputs and the offset of GPS and INS, state equations and observations are designed to perform the calculation and improve the navigation accuracy. An example shows that with the method the reliable navigation parameters have been obtained.     

Integrated Data Processing Method for GPS and INS Field Test over Rocky Mountain

IntroductionGPS/INS integrated system exploited the INSand differential GPS pseudo-range and carrierphase technique to promote the accuracy of thedynamic platform navigation and positioning,and increase the reliability and stabilization.Incalculation, Kal…     

INS／DGPS支持的机载线阵推扫影像几何校正

论述了利用INS／DGPS系统的导航输出计算外方位元素的原理，介绍了行方位数据支持下机载线阵CCD影像直接和间接法几何校正的基本方法，重点分析并解决了直接法纠正中的目标定位、灰度重采样及间接法校正中最佳扫描行的确定问题。PHI高光谱影像的实验结果表明，文中的校正算法可达到优于1．5m的绝对定位精度及0．7m的相对精度。     

抗差估计在RTK／INS紧组合中的应用研究

针对动态环境下GNSS／INS导航定位结果常受粗差影响的问题,提出了一种基于抗差卡尔曼滤波的GPS／BDS双系统RTK／INS紧组合导航定位算法,根据方差膨胀模型,建立抗差卡尔曼算法,得到GNSS／INS紧组合抗差解,并通过两个不同区域的实测车载实验进行了算法验证. 实验结果表明:本方法相较于传统方法，在N、E、D三个方向的导航精度分别提高1.4～4.6 cm,0.7～9 cm,1.5～2 cm,模糊度固定成功率提高10.3％～25.6％,导航精度及可靠性得到显著提高,对动态环境下车载或自动驾驶等应用具有一定的理论参考和实用价值.      

INS/GNSS/ODO嵌入式系统的容错技术研究

车载低成本嵌入式组合导航系统的可靠性容易受到多种传感器故障和环境的影响,基于全球卫星导航系统(GNSS)状态的惯性导航系统(INS)/GNSS/里程计(ODO)抗差组合导航算法,提出了一种两级故障检测处理方法. 其中,第一级检测使用了基于解析冗余的残差卡方检验法,第二级检测使用了改进的双状态传播卡方检验算法. 利用自主研制的GN310低成本嵌入式系统采集路测数据. 结果表明:相对于传统算法,水平定位精度提升了39.7%;另外在半实物仿真下,水平定位误差保持在3 m以内,表明该容错方法能够有效地处理ODO、INS故障和GNSS软硬故障.      

CNS+GNSS+INS船载高精度实时定位定姿算法改进研究

天文导航（CNS）、卫星导航（GNSS）和惯性导航（INS）3种系统组合可提供高精度的定位定姿结果。实际工程中因INS长时间误差累积，以及系统硬件传输存在不可忽略的时间延迟，导致INS提供给CNS的预报粗姿态误差较大，恶劣海况下难以保障快速搜星，造成天文导航可靠性下降、姿态测量精度较低的问题。为此，本文提出了一种CNS+GNSS+INS高精度信息融合实时定位定姿框架，引入了等角速度外推措施，有效地解决了惯导信息延迟问题。通过高精度转台模拟恶劣海况下载体大角速度摇摆，验证了本文提出的改进算法的有效性。试验结果表明，该算法架构简单，性能可靠，显著提高了恶劣环境下星敏感器的快速、准确搜星能力，保障了三组合姿态测量的精度和可用性。