The Utilization of Artificial Neural Networks for Multisensor System Integration in Navigation and Positioning Instruments

Inertial-navigation system (INS) and global position system (GPS) technologies have been widely applied in many positioning and navigation applications. INS determines the position and the attitude of a moving vehicle in real time by processing the measurements of three-axis gyroscopes and three-axis accelerometers mounted along three mutually orthogonal directions. GPS, on the other hand, provides the position and the velocity through the processing of the code and the carrier signals of at least four satellites. Each system has its own unique characteristics and limitations. Therefore, the integration of the two systems offers several advantages and overcomes each of their drawbacks. The integration of INS and GPS is usually implemented utilizing the Kalman filter, which represents one of the best solutions for INS/GPS integration. However, the Kalman filter performs adequately only under certain predefined dynamic models. Alternatively, this paper suggests an INS/GPS integration method based on artificial neural networks (ANN) to fuse uncompensated INS measurements and differential GPS (DGPS) measurements. The proposed method suggests two different architectures: the position update architecture (PUA) and the position and velocity PUA (PVUA). Both architectures were developed utilizing multilayer feed-forward neural networks with a conjugate gradient training algorithm.     

Adaptive neuro-fuzzy module for inertial navigation system/global positioning system integration utilising position and velocity updates with real-time cross-validation

Recently, methods based on artificial intelligence (AI) have been suggested to provide reliable positioning information for different land vehicle navigation applications. The majority of these applications utilise both the global positioning system (GPS) and the inertial navigation system (INS). These AI modules were trained to mimic the latest vehicle dynamics so that, in case of GPS outages, the system relies on INS and the recently updated AI module to provide the vehicle position. Several neural networks and neuro-fuzzy techniques were implemented in real-time in a de-centralised fashion and provided acceptable accuracy for short GPS outages. It was reported that these methods provided poor positioning accuracy during relatively long GPS outages. In order to prevail over this limitation, this study optimises the Al-based INS/GPS integration schemes utilising adaptive neuro-fuzzy inference system with performing, in real-time, both GPS position and velocity updates. In addition, a holdout cross validation method during the update procedure was utilised in order to ensure generalisation of the model. The proposed system is tested using differential GPS and both navigational and tactical grades INS field test data obtained from a land vehicle experiment. The results showed that the effectiveness of the proposed system over both the existing Al-based and the conventional INS/GPS integration techniques, especially during long GPS outages. This method may have one limitation related to the unusual significant changes of the vehicle dynamics between the update and the prediction stages of operation which may influence the overall positioning accuracy.     

基于GNSS/INS联合仿真的组合导航终端测试方法研究

为解决GNSS/INS组合导航终端动态定位性能的实验室测试问题,本文在转台惯性仿真测试和卫星导航仿真测试技术的基础上,提出了GNSS/INS联合仿真两步法,即先通过惯性传感器实物测试获得其特征误差模型,再使用测试场景中载体初始条件和轨迹数据驱动该误差模型产生惯性传感器的模拟输出数据流,最后同步GNSS信号仿真的方法实现GNSS/INS联合仿真的过程。仿真测试结果与外场实测对比后,证明该方法获得的测试数据准确度满足预期指标、结果可靠,而且比其他传统测试方法的成本低、效率高。     

Inertial sensor technology trends

This paper presents an overview of how inertial sensor technology is applied in current applications and how it is expected to be applied in nearand far-term applications. The ongoing trends in inertial sensor technology development are discussed, namely interferometric fiber-optic gyros, micro-mechanical gyros and accelerometers, and micro-optical sensors. Micromechanical sensors and improved fiber-optic gyros are expected to replace many of the current systems using ring laser gyroscopes or mechanical sensors. The successful introduction of the new technologies is primarily driven by cost and cost projections for systems using these new technologies are presented. Externally aiding the inertial navigation system (INS) with the global positioning system (GPS) has opened up the ability to navigate a wide variety of new large-volume applications, such as guided artillery shells. These new applications are driving the need for extremely low-cost, batch-producible sensors     

多传感器信息融合技术及其在组合导航系统中的应用

针对组合导航系统在数据处理时存在的计算量大和故障数据相互污染的问题。提出了一种基于信息融合的导航参数最优估计滤波方法。文中首先介绍了信息融合的基本原理，关键技术以及常用方法，然后以INS／GPS组合为例，对组合导航系统的工作原理和模型建立进行了详细的分析，最后通过计算机仿真证明了该方法可提高导航系统的计算精度和速度，有较好的容错性和环境适应性，具有实际使用价值。     

A New Optimized Algorithm with Nonlinear Filter for Ultra-Tightly Coupled Integrated Navigation System of Land Vehicle

The extended particle filter (EPF) assisted by the Takagi-Sugeno (T-S) fuzzy logic adaptive system (FLAS) is used to design the ultra-tightly coupled GPS/INS (inertial navigation system) integrated navigation, which can maneuver the vehicle environment and the GPS outages scenario. The traditional integrated navigation designs adopt a loosely or tightly coupled architecture, for which the GPS receiver may lose the lock due to the interference/jamming scenarios, high dynamic environments, and the periods of partial GPS shading. An ultra-tight GPS/INS architecture involves the integration of I (in-phase) and Q (quadrature) components from the correlator of a GPS receiver with the INS data. The EPF is a particle filter (PF) which uses the extended Kalman filter (EKF) to generate the proposal distribution. The PF depends mostly on the number of particles in order to achieve a better performance during the high dynamic environments and GPS outages. The T-S FLAS is one of these approaches that can prevent the divergence problem of the filter when the precise knowledge on the system models is not available. The results show that the proposed fuzzy adaptive EPF (FAEPF) can effectively improve the navigation estimation accuracy and reduce the computational load as compared with the EPF and the unscented Kalman filter (UKF).     

基于MEMS的分散式低阶AHPRS系统设计

设计一种基于MEMS陀螺、加速度计、磁强计以及GPS模块姿态航向位置参考系统(AHPRS).首先,姿态航向参考系统主要由姿态估计卡尔曼滤波器与补偿卡尔曼滤波器构成,通过补偿滤波器周期修正姿态估计滤波器,从而弥补了由于机体的刚体运动而导致姿态角的估计误差;其次,采用分散式卡尔曼滤波器的设计思路,以估计的误差姿态角作为导航系统卡尔曼滤波器的输入量,有效降低了导航滤波方程的阶次,减小了对姿态解算计算机的性能要求;最后,通过仿真与飞行试验验证该AHPRS有效地克服了动态环境下对系统姿态估计偏差大的缺点,提高了系统的姿态航向与速度位置估计精度.     

Support Vector Machine Assisted GPS Navigation in Limited Satellite Visibility

This paper investigates performance improvement via the incorporation of the support vector machine (SVM) into the vector tracking loop (VTL) for the Global Positioning System (GPS) in limited satellite visibility. Unlike the traditional scalar tracking loop (STL), the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user. The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage. Similar to the neural network, the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training. The SVM is employed for predicting adequate numerical control oscillator (NCO) inputs, i.e., providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system. When the navigation processing is in good condition, the SVM is at the training stage, and the output information from the discriminator and navigation filter is adopted as the inputs. Other machine learning (ML) algorithms such as the radial basis function neural network (RBFNN) and the Adaptive Network-Based Fuzzy Inference System (ANFIS) are employed for comparison. Performance evaluation for the SVM assisted architecture as compared to the RBFNN- and ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented. The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage.     

INS/GNSS组合导航系统校准方法

惯性导航系统(INS)和全球导航卫星系统(GNSS)组合的导航系统应用越来越广泛,它的校准问题亟待解决。本文简要介绍了目前广泛使用的各种导航系统,论述了INS/GNSS组合导航系统的工作原理及组合算法;详细分析了INS/GNSS组合导航系统各种测试及校准方法,比较了各种校准方法的优缺点;提出了INS/GNSS组合导航系统校准装置应具备的条件:可产生标准的空间位置和速度、可产生标准的姿态角度变化、可有效地接收GNSS卫星信号、统一的时间基准与同步信号。     

Variational Bayesian Based IMM Robust GPS Navigation Filter

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.     

Robust positioning technique in low-cost DR/GPS for land navigation

This paper describes a dead-reckoning (DR) construction for land navigation and sigma-point-based receding-horizon Kalman finite-impulse response (SPRHKF) filter for DR/GPS integration system. A simple DR construction is adopted to improve the performance of both pure land DR navigation and DR/GPS integration system. In order to overcome the flaws of the extended Kalman filter (EKF), the sigma-point KF (SPKF) is merged with the receding-horizon strategy. This filter has several advantages over the EKF, the SPKF, and the RHKF filter. The advantages include the robustness to the system model uncertainty, the initial estimation error, temporary unknown bias, etc. The computational burden is reduced. Especially, the proposed filter works well even in the case of exiting the unmodeled random walk of the inertial sensors, which can occur in the microelectromechanical systems' inertial sensors by temperature variation. Therefore, the SPRHKF filter can provide the navigation information with good quality in the DR/GPS integration system for land navigation seamlessly.     

多传感器融合的智能车定位导航系统设计

车辆定位导航系统实时、准确获取车辆位置对实现智能驾驶具有重要意义。针对传统定位导航系统存在的精度低、成本高、鲁棒性差等问题，基于GPS/INS(global positioning system /inertial navigation system,全球定位系统/惯性导航系统)、机器视觉和超声波雷达技术，设计了一种多传感器融合的智能车定位导航系统，旨在实现智能车在简单、结构化道路环境下的自动驾驶。利用GPS/INS技术实现智能车地理坐标获取，利用机器视觉技术实现智能车前方车道线检测，利用超声波雷达技术实现道路边沿检测，并对地理坐标、车道线和道路边沿数据进行深度融合，实现车道级定位导航。最后，进行了智能车定位导航现场测试，结果表明该系统满足车道级定位导航性能要求。研究结果表明，在简单、结构化道路环境下，多传感器融合的智能车定位导航系统结构简单，实际运行状况良好，可极大提高定位导航精度。     

光纤陀螺与精确制导

阐述了光纤陀螺和惯性导航系统的发展历史及现状 ,详细分析了光纤陀螺产品在惯性导航系统中的地位 .同时对惯性导航系统的两大支撑技术——加速度计和全球定位系统 ( GPS)及其应用前景进行了简要的介绍 ,并且给出了最新 HARM精确制导系统的应用实例     

基于GPS与惯性组件的复合测姿系统设计

针对微小型载体空间有限而无法安装大型测姿系统,提出了一种低成本的微型复合测姿方案.复合测姿系统由单天线GPS和MEMS惯性组件构成.利用扩展卡尔曼滤波将单天线GPS和MEMS惯性组件信息融合,提高了姿态估计精度.室外车载实验结果表明,利用该复合测姿系统解算的滚角和俯仰角误差可控制在1°以内,航向角误差可控制在2°以内.     

Estimation of attitudes from a low-cost miniaturized inertial platform using Kalman Filter-based sensor fusion algorithm

Due to costs, size and mass, commercially available inertial navigation systems are not suitable for small, autonomous flying vehicles like ALEX and other UAVs. In contrast, by using modern MEMS (or of similar class) sensors, hardware costs, size and mass can be reduced substantially. However, low-cost sensors often suffer from inaccuracy and are influenced greatly by temperature variation. In this work, such inaccuracies and dependence on temperature variations have been studied, modelled and compensated in order to reach an adequate quality of measurements for the estimation of attitudes. This has been done applying a Kaiman Filter-based sensor fusion algorithm that combines sensor models, error parameters and estimation scheme. Attitude estimation from low-cost sensors is first realized in a Matlab/Simulink platform and then implemented on hardware by programming the micro controller and validated. The accuracies of the estimated roll and pitch attitudes are well within the stipulated accuracy level of ±5‡ for the ALEX. However, the estimation of heading, which is mainly derived from the magnetometer readings, seems to be influenced greatly by the variation in local magnetic field     

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established. The GPS navigation algorithm based on kernel entropy related principles, including the MEE criterion and the MCC will be performed, which is utilized not only for the time-varying adaptation but the outlier type of interference errors. The kernel entropy based design is a new approach using information from higher-order signal statistics. In information theoretic learning (ITL), the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to MSE. To improve the performance under non-Gaussian environments, the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error (MMSE) is utilized for mitigation of the heavy-tailed type of multipath errors. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.     

基于MEMS平面弹簧的微小载荷测试方法

本文提出一种微小载荷测量方法,搭建了基于MEMS平面弹簧的测量装置,主要由支撑臂、MEMS平面弹簧和激光位移探测器组成.通过激光位移探测器测量MEMS平面弹簧的微小变形,换算得到所施加的微小载荷.采用深反应离子刻蚀(DRIE)等微加工工艺制作了平面微弹簧,并对其进行理论计算和仿真模拟,给出了刚度范围,最后通过实际测量的方式进行了实验标定.结果显示:MEMS平面弹簧的标定刚度为7.88 mN/μm,其结果与理论及仿真结果较接近,测量精度可达0.08 mN.使用该装置和精密电子天平分别对10组微小质量试块进行测量,其平均误差率为5.75%.     

On the optimal control of steel annealing processes via various versions of genetic and particle swarm optimization algorithms

7 This paper elucidates the computation of optimal controls for steel annealing processes as hybrid systems which comprise of one or more furnaces integrated with plant-wide planning and scheduling operations. A class of hybrid system is considered to capture the trade-off between metallurgical quality requirement and timely product delivery. Various optimization algorithms including particle swarm optimization algorithm (PSO) with time varying inertia weight methods, PSO with globally and locally tuned parameters (GLBest PSO), parameter free PSO (pf-PSO) and PSO like algorithm via extrapolation (ePSO), real coded genetic algorithm (RCGA) and two-phase hybrid real coded genetic algorithm (HRCGA) are considered to solve the optimal control problems for the steel annealing processes (SAP). The optimal solutions including optimal line speed, optimal cost, and job completion time and convergence rate obtained through all these optimization algorithms are compared with each other and also those obtained via the existing method, forward algorithm (FA). Various statistical analyses and analysis of variance (ANOVA) test and hypothesis t-test are carried out in order to compare the performance of each method in solving the optimal control problems of SAP. The comparative study of the performance of the various algorithms indicates that the PSO like algorithms, pf-PSO and ePSO are equally good and are also better than all the other optimization methods considered in this chapter.     

A particle swarm‐optimized support vector machine for reliability prediction

System reliability depends on inherent mechanical and structural aging factors as well as on operational and environmental conditions, which could enhance (or smoothen) such factors. In practice, the involved dependences may burden the modeling of the reliability behavior over time, in which traditional stochastic modeling approaches may likely fail. Empirical prediction methods, such as support vector machines (SVMs), become a valid alternative whenever reliable time series data are available. However, the prediction performance of SVMs depends on the setting of a number of parameters that influence the effectiveness of the training stage during which the SVMs are constructed based on the available data set. The problem of choosing the most suitable values for the SVM parameters can be framed in terms of an optimization problem aimed at minimizing a prediction error. In this work, this problem is solved by particle swarm optimization (PSO), a probabilistic approach based on an analogy with the collective motion of biological organisms. SVM in liaison with PSO is then applied to tackle reliability prediction problems based on time series data of engineered components. Comparisons of the obtained results with those given by other time series techniques indicate that the PSO + SVM model is able to provide reliability predictions with comparable or great accuracy. Copyright © 2011 John Wiley & Sons, Ltd.     

KERNEL: Enabler to build smart surrogates for online optimization and knowledge discovery

KERNEL – A novel parameter-free surrogate building algorithm using Adaptive Neuro Fuzzy Inference System (ANFIS) is presented to provide an intelligent and robust technology to optimally estimate the configuration of ANFIS along with Sobol-based fast sample size determination (SSD) methodology. The proposed algorithm is capable of fine-tuning the existing knowledge base about the physics of the process in terms of human experience. It also enables knowledge discovery through a multi-objective optimization problem (MOOP) solved by non-dominated sorting genetic algorithm, NSGA-II, thus presenting machine-invented physics of the process. Experimentally validated polymerization reaction network model is considered and ANFIS surrogates are built using KERNEL. Surrogate-based optimization was found to be nine times faster than conventional optimization using the time expensive model, thus enabling its online implementation. Comparison of ANFIS with Kriging is also included.