An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications

The Kalman filter (KF) has been implemented as the primary integration scheme of the global positioning system (GPS) and inertial navigation systems (INS) for many land vehicle navigation and positioning applications. However, it has been reported that KF-based techniques have certain limitations, which reflect on the position error accumulation during GPS signal outages. Therefore, this article exploits the idea of incorporating artificial neural networks to develop an alternative INS/GPS integration scheme, the intelligent navigator, for next generation land vehicle navigation and positioning applications. Real land vehicle test results demonstrated the capability of using stored navigation knowledge to provide real-time reliable positioning information for stand-alone INS-based navigation for up to 20 min with errors less than 16 m (as compared to 2.6 km in the case of the KF). For relatively short GPS outages, the KF was superior to the intelligent navigator for up to 30 s outages. In contrast, the intelligent navigator was superior to the KF when the length of GPS outages was extended to 90 s. The average improvement of the intelligent navigator reached 60% in the latter scenario. The results presented in this article strongly indicate the potential of including the intelligent navigator as the core algorithm for INS/GPS integrated land vehicle navigation systems.     

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.     

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.     

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.     

A low-cost INS/GPS integration methodology based on random forest regression

This paper, for the first time, introduces a random forest regression based Inertial Navigation System (INS) and Global Positioning System (GPS) integration methodology to provide continuous, accurate and reliable navigation solution. Numerous techniques such as those based on Kalman filter (KF) and artificial intelligence approaches exist to fuse the INS and GPS data. The basic idea behind these fusion techniques is to model the INS error during GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby maintaining the continuity and improving the navigation solution accuracy. KF based approaches possess several inadequacies related to sensor error model, immunity to noise, and computational load. Alternatively, neural network (NN) proposed to overcome KF limitations works unsatisfactorily for low-cost INS, as they suffer from poor generalization capability due to the presence of high amount of noise.In this study, random forest regression has shown to effectively model the highly non-linear INS error due to its improved generalization capability. To evaluate the proposed method effectiveness in bridging the period of GPS outages, four simulated GPS outages are considered over a real field test data. The proposed methodology illustrates a significant reduction in the positional error by 24–56%.     

Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors

Kalman filter (KF) is the most commonly used estimation technique for integrating signals from short-term high performance systems, like inertial navigation systems (INSs), with reference systems exhibiting long-term stability, like the global positioning system (GPS). However, KF only works well under appropriately predefined linear dynamic error models and input data that fit this model. The latter condition is rather difficult to be fulfilled by a low-cost inertial measurement unit (IMU) utilizing microelectromechanical system (MEMS) sensors due to the significance of their long- and short-term errors that are mixed with the motion dynamics. As a result, if the reference GPS signals are absent or the Kalman filter is working for a long time in prediction mode, the corresponding state estimate will quickly drift with time causing a dramatic degradation in the overall accuracy of the integrated system. An auxiliary fuzzy-based model for predicting the KF positioning error states during GPS signal outages is presented in this paper. The initial parameters of this model is developed through an offline fuzzy orthogonal-least-squares (OLS) training while the adaptive neuro-fuzzy inference system (ANFIS) is implemented for online adaptation of these initial parameters. Performance of the proposed model has been experimentally verified using low-cost inertial data collected in a land vehicle navigation test and by simulating a number of GPS signal outages. The test results indicate that the proposed fuzzy-based model can efficiently provide corrections to the standalone IMU predicted navigation states particularly position.     

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.     

Multi-sensor fusion methodology for enhanced land vehicle positioning

It is a main challenge for land vehicles to achieve reliable and low-cost navigation solution in various situations, especially when Global Positioning System (GPS) is not available. To address this challenge, we propose an enhanced multi-sensor fusion methodology to fuse the information from low-cost GPS, MEMS Inertial Measurement Unit (IMU), and digital compass in this paper. First, a key data preprocessing algorithm based on Empirical Mode Decomposition (EMD) interval threshold filter is developed to remove the noises in inertial sensors so as to offer more accurate information for subsequent modeling. Then, a Least-Squares Support Vector Machine (LSSVM)-based nonlinear autoregressive with exogenous input (NARX) model (LSSVM-NARX) is designed and augmented with Kalman filter (KF) to construct a novel LSSVM-NARX/KF hybrid strategy. In case of GPS outages, the recently updated LSSVM-NARX is adopted to predict and compensate for the INS position errors. Finally, the performance of proposed methodology was evaluated with real-world data collected in urban settings including typical driving maneuvers. The results indicate that the proposed methodology can achieve remarkable enhancement in positioning accuracy in GPS-denied environments.     

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.     

A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS

Land Vehicle Navigation (LVN) mostly relies on integrated system consisting of Inertial Navigation System (INS) and Global Positioning System (GPS). The combined system provides continuous and accurate navigation solution when compared to standalone INS or GPS. Different fusion methodology such as those based on Kalman filtering and particle filtering has been proposed that estimates and models the INS error during the GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby improving its accuracy. However, these fusion approaches possess several inadequacies related to sensor error model, immunity to noise and computational load. Alternatively, Neural Network (NN) based approaches has been proposed. In the case of low-cost INS, the NN suffers from poor generalization capability due to the presence of high amount of noises.The paper thus introduces a novel and hybrid fusion methodology utilizing Dempster–Shafer (DS) theory augmented by Support Vector Machines (SVM), known as DS-SVM. The INS and GPS data fusion is carried using DS fusion whereas SVM models the INS error. During GPS availability, DS provides accurate solution; whereas during outages, the trained SVM model corrects the INS error thereby improving the positioning accuracy. The proposed methodology is evaluated against the existing Artificial Neural Network (ANN) and the Random Forest Regression (RFR) methodology. A total of 20–87% improvement in the positional accuracy was found against ANN and RFR.     

Sensor Integration for Satellite-Based Vehicular Navigation Using Neural Networks

Land vehicles rely mainly on global positioning system (GPS) to provide their position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. This letter proposes a data fusion technique based on radial basis function neural network (RBFNN) that integrates GPS with inertial sensors in real time. A field test data was used to examine the performance of the proposed data fusion module and the results discuss the merits and the limitations of the proposed technique     

Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm

Recently, methods based on Artificial Intelligence (AI) have been widely used to improve positioning accuracy for land vehicle navigation by integrating the Global Positioning System (GPS) with the Strapdown Inertial Navigation System (SINS). In this paper, we propose the ensemble learning algorithm instead of traditional single neural network to overcome the limitations of complex and dynamic data cased by vehicle irregular movement. The ensemble learning algorithm (LSBoost or Bagging), similar to the neural network, can build the SINS/GPS position model based on current and some past samples of SINS velocity, attitude and IMU output information. The performance of the proposed algorithm has been experimentally verified using GPS and SINS data of different trajectories collected in some land vehicle navigation tests. The comparison results between the proposed model and traditional algorithms indicate that the proposed algorithm can improve the positioning accuracy for cases of SINS and specific GPS outages.     

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.     

PERFORMANCE ANALYSIS OF VARIOUS ARTIFICIAL INTELLIGENT NEURAL NETWORKS FOR GPS/INS INTEGRATION

An aircraft system mainly relies on a Global Positioning System (GPS) to provide accurate position values consistently. However, GPS receivers may encounter frequent GPS absence because of ephemeric error, satellite clock error, multipath error, and signal jamming. To overcome these drawbacks, generally a GPS is integrated with an Inertial Navigation System (INS) mounted inside the vehicle to provide a reliable navigation solution. INS and GPS are commonly integrated using a Kalman filter (KF) to provide a robust navigation solution. In the KF approach, the error models of both INS and GPS are required; this leads to the complexity of the system. This research work presents new position update architecture (NPUA) which consists of various artificial intelligence neural networks (AINN) that integrate both GPS and INS to overcome the drawbacks of the Kalman filter. The various AINNs that include both static and dynamic networks described for the system are radial basis function neural network (RBFNN), backpropagation neural network (BPN), forward-only counter propagation neural network (FCPN), full counter propagation neural network (Full CPN), adaptive resonance theory-counter propagation neural network (ART-CPN), constructive neural network (CNN), higher-order neural networks (HONN), and input-delayed neural networks (IDNN) to predict the INS position error during GPS absence, resulting in different performances. The performances of the different AINNs are analyzed in terms of root mean square error (RMSE), performance index (PI), number of epochs, and execution time (ET).     

A New Approach to Estimate True Position of Unmanned Aerial Vehicles in an INS/GPS Integration System in GPS Spoofing Attack Conditions

This paper presents a new approach to estimate the true position of an unmanned aerial vehicle (UAV) in the conditions of spoofing attacks on global positioning system (GPS) receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system (INS) and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted particle swarm optimization filter (PSOF) to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle’s experiences, application of PSOF algorithm in the INS/GPS integration system leads to more precise positioning compared with general particle filter (PF) and adaptive unscented particle filer (AUPF) in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estimating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error (RMSE) test.     

基于小波和人工智能技术的车辆无缝定位技术研究

基于自适应神经模糊逻辑推理系统(ANHS),在全球定位系统(GPS)信号阻塞时,为惯性导航系统(INS)提供位置和速度修正量以提高系统的精度和鲁棒性.首先用小波对数据信号进行降噪处理;然后设定INS的位置或速度作为ANHS的输入参数,经训练后输出相应修正量,训练期望值为经小波多分辨率分析得到的位置误差和速度误差.实验表明,无GPS信号时定位精度比同条件下卡尔曼滤波精度提高约40%,因此该方法可为车辆提供可靠有效的导航定位服务.     

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.     

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。     

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。