Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

DESIGN & REALIZATION ON GPS/INS INTEGRATED NAVIGATION SYSTEM

This paper deals with the research of the GPS/INS integrated navigation system applying Extended Kalman Filter, which involves integrated principles, scheme and technology of combining with real INS and GPS receiver data. Emphases are placed on the modeling of system errors and implementation of the integrated system. Both loose and tightly coupled GPS/INS integrated in schemes are analyzed. On the basis of our experience accumulated in the research of GPS/INS for many years, the GPS/INS integrated navigation developing system is developed. It can be put into efficient and economic use in the study and design of integrated navigation system. It plays an important role in the aeronautical and astronautical fields in China. This system is not only a computer aided design software but also a semi physical simulation system by obtaining real INS and GPS receiver data. So the key software unit of the developing system could be conveniently transferred into practical engineering software in actual hardware integrated system. The application of this system shows that the design ideas and integrated scheme of this development system are successful, and can achieve good navigation result.

Tightly-coupled model for INS/WSN integrated navigation based on Kalman filter

Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightly-coupled integration based on the Kalman filter （KF）. When the WSN is available, the difference between the distances from the blind node（BN） to the reference nodes （RNs） measured by the INS and those measured by the WSN are used as measurement information for the KF due to its better observability and independence, which can effectively improve the accuracy of the KF. Simulations show that the proposed approach reduces the mean error of the position by about 50% compared with loosely-coupled integration, while the mean error of the velocity is a little higher than that of loosely-coupled integration.

Unequal-interval data fusion algorithm for inertial/gravity matching integrated navigation system

Inertial/gravity matching integrated navigation system can effectively improve the longendurance navigation ability of underwater vehicles.Through the analysis of the matching process,the problem of unequal-interval in matching trajectory is addressed by an unequal-interval data fusion algorithm which is based on the unequal-interval characteristics analysis of the matching trajectory.Compared with previously available methods,the proposed algorithm improves the location precision.In conclusion,simulations of the integrated navigation system demonstrated the effectiveness and superiority of the proposed algorithm.

SINS/CNS/GPS integrated navigation algorithm based on UKF

A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.

IAE-adaptive Kalman filter for INS/GPS integrated navigation system

A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

An INS/GNSS integrated navigation in GNSS denied environment using recurrent neural network

In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.

Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation

To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.

A tightly coupled rotational SINS/CNS integrated navigation method for aircraft

Strapdown inertial navigation system(SINS)/celestial navigation system(CNS)integrated navigation is widely used to achieve long-time and high-precision autonomous navigation for aircraft.In general,SINS/CNS integrated navigation can be divided into two integrated modes:loosely coupled integrated navigation and tightly coupled integrated navigation.Because the loosely coupled SINS/CNS integrated system is only available in the condition of at least three stars,the latter one is becoming a research hotspot.One major challenge of SINS/CNS integrated navigation is obtaining a high-precision horizon reference.To solve this problem,an innovative tightly coupled rotational SINS/CNS integrated navigation method is proposed.In this method,the rotational SINS error equation in the navigation frame is used as the state model,and the starlight vector and star altitude are used as measurements.Semi-physical simulations are conducted to test the performance of this integrated method.Results show that this tightly coupled rotational SINS/CNS method has the best navigation accuracy compared with SINS,rotational SINS,and traditional tightly coupled SINS/CNS integrated navigation method.

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

Application of unscented Kalman filter to novel terrain passive integrated navigation system

To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.

APPLYINGSUCCESSIVEORTHOGONALIZATIONDECENTRALIZEDKALMANFILTERTOGPS／INSINTEGRATEDNAVIGATIONSYSTEM

The Successive Orthogonalization Decentralized Kalman Filter (SODKF ) is a new method which is used for large system state estimation. It can be applied not only to large system decentralization, but also to precision realization at approximately the same level of the global filter, thus, making possible the engineering operation as well as shortening the computing time. This paper discusses the principles and features of SODKF when used in GPS/INS integrated navigation system. The system will be firstly divided into three subsystems and then corrected in both open and closed loops. The system simulation results by two integrated patterns show that SODKF is efficient and realizable. While the three subsystems are simulated in series, the computing speed doubles that of the global system. In addition, its optimal estimating precision remains unchanged. It can be concluded from this paper that large integrated navigation systems with GPS, INS, Terrain Match, Loran C, Doppler Radar and Radio Altimeter can be made more efficient by this multi subsystem of navigation.

INS-GNSS Integrated Navigation Algorithm Based on TransGAN

With the rapid development of autopilot technology,a variety of engi-neering applications require higher and higher requirements for navigation and positioning accuracy,as well as the error range should reach centimeter level.Single navigation systems such as the inertial navigation system(INS)and the global navigation satellite system(GNSS)cannot meet the navigation require-ments in many cases of high mobility and complex environments.For the purpose of improving the accuracy of INS-GNSS integrated navigation system,an INS-GNSS integrated navigation algorithm based on TransGAN is proposed.First of all,the GNSS data in the actual test process is applied to establish the data set.Secondly,the generator and discriminator are constructed.Borrowing the model structure of generator transformer,the generator is constructed by multi-layer transformer encoder,which can obtain a wider data perception ability.The generator and discriminator are trained and optimized by the production countermeasure network,so as to realize the speed and position error compensa-tion of INS.Consequently,when GNSS works normally,TransGAN is trained into a high-precision prediction model using INS-GNSS data.The trained Trans-GAN model is emoloyed to compensate the speed and position errors for INS.Through the test analysis offlight test data,the test results are compared with the performance of traditional multi-layer perceptron(MLP)and fuzzy wavelet neural network(WNN),demonstrating that TransGAN can effectively correct the speed and position information when GNSS is interrupted,with the high accuracy.

The Performance Evaluation of the Integration of Inertial Navigation System and Global Navigation Satellite System with Analytic Constraints

The integration of GNSS （Global Navigation Satellite System） and INS （Inertial Navigation System） using IMU （Inertial Measurement Unit） is now widely used for MMS （Mobile Mapping System） and navigation applications to seamlessly determine position, velocity and attitude of the mobile platform. With low cost, small size, ligh weight and low power consumtion, the MEMS （Micro-Electro-Mechanical System） IMU and low cost GPS （Global Positioning System） receivers are now the trend in research and using for many applications. However, researchs in the literature indicated that the the performance of the low cost INS/GPS systems is still poor, particularly, in case of GNSS-noise environment. To overcome this problem, this research applies analytic contrains including non-holonomic constraint and zero velocity update in the data fusion engine such as Extended Kalman Filter to improve the performance of the system. The benefit of the proposed method will be demonstrated through experiments and data analysis.

Suppression of the G-sensitive drift of laser gyro in dual-axis rotational inertial navigation system

The dual-axis rotational inertial navigation system(INS)with dithered ring laser gyro(DRLG)is widely used in high precision navigation.The major inertial sensor errors such as drift errors of gyro and accelerometer can be averaged out,but the G-sensitive drifts of laser gyro cannot be averaged out by indexing.A 16-position rotational simulation experiment proves the G-sensitive drift will affect the long-term navigation error for the rotational INS quantitatively.The vibration coupling and asymmetric structure of the DRLG are the main errors.A new dithered mechanism and optimized DRLG is designed.The validity and efficiency of the optimized design are conformed by 1 g sinusoidal vibration experiments.An optimized inertial measurement unit(IMU)is formulated and measured experimentally.Laboratory and vehicle experimental results show that the divergence speed of longitude errors can be effectively slowed down in the optimized IMU.In long term independent navigation,the position accuracy of dual-axis rotational INS is improved close to 50%,and the G-sensitive drifts of laser gyro in the optimized IMU are less than 0.0002°/h.These results have important theoretical significance and practical value for improving the structural dynamic characteristics of DRLG INS,especially the highprecision inertial system.

Error Model of Rotary Ring Laser Gyro Inertial Navigation System

To improve the precision of inertial navigation system（INS） during long time operation,the rotation modulated technique（RMT） was employed to modulate the errorr of the inertial sensors into periodically varied signals,and,as a result,to suppress the divergence of INS errors.The principle of the RMT was introduced and the error propagating functions were derived from the rotary navigation equation.Effects of the measurement error for the rotation angle of the platform on the system precision were analyzed.The simulation and experimental results show that the precision of INS was ① dramatically improved with the use of the RMT,and ② hardly reduced when the measurement error for the rotation angle was in arc-second level.The study results offer a theoretical basis for engineering design of rotary INS.

DGPS/INS Data Fusion for Land Navigation

The interest for land navigation has increased for the recent years. With the advent of the Global Position System (GPS) we have now the ability to determine the absolute position anywhere on the globe. The problem is that the GPS systems work well only in open environments with no overhead obstructions and they are subject to large unavoidable errors when the reception from some of the satellites are blocked. This occurs frequently in urban environments, forests and tunnels. GPS systems require at least four “visible” satellites to maintain a good position fix. In many situations in which higher level of accuracy is required, the navigation cannot be achieved by GPS alone. This paper discusses the design of a reliable multisensor fusion algorithm using GPS and Inertial Navigation System in order to decrease the implementation cost of such systems on land vehicles. The major contribution of this paper is in the definition of the possible developments and research axes in land navigation.

Point mass filter based matching algorithm in gravity aided underwater navigation

Gravity-aided inertial navigation is a hot issue in the applications of underwater autonomous vehicle(UAV). Since the matching process is conducted with a gravity anomaly database tabulated in the form of a digital model and the resolution is 2’ × 2’,a filter model based on vehicle position is derived and the particularity of inertial navigation system(INS) output is employed to estimate a parameter in the system model. Meanwhile, the matching algorithm based on point mass filter(PMF) is applied and several optimal selection strategies are discussed. It is obtained that the point mass filter algorithm based on the deterministic resampling method has better practicability. The reliability and the accuracy of the algorithm are verified via simulation tests.

复杂环境下GPS+BDS-3 PPP/INS紧组合算法性能分析

针对城市复杂环境下精密单点定位(PPP)连续高精度定位受限的问题,本文基于北斗三号全球卫星导航系统(BDS-3)与全球定位系统(GPS)双系统,利用PPP与惯性导航系统(INS)紧组合算法来改善城市复杂环境下定位性能。通过构建GPS+BDS-3双系统PPP/INS紧组合滤波模型,分别对GPS PPP、GPS+BDS-3 PPP、GPS PPP/INS紧组合、GPS+BDS-3PPP/INS紧组合4种方案在城区开阔、复杂环境下的定位、测速、定姿性能进行了分析和评估。实验结果表明,开阔环境下PPP/INS紧组合相比PPP定位精度略有提升;而在复杂环境下PPP/INS紧组合可显著解决PPP定位结果连续性和有效性较差的问题;引入BDS-3后,PPP/INS紧组合导航性能进一步提升,GPS+BDS-3 PPP/INS紧组合相比GPS PPP/INS紧组合,在东、北、天方向上,开阔环境下定位精度分别提升18.5%、32.7%、43.2%,复杂环境下定位精度分别提升35.9%、49.1%、56.5%。

基于BDS/INS的列车组合定位建模仿真方法研究

基于北斗卫星导航系统(BDS)的列车定位可减少列控系统地面设备,降低建设成本。然而列车运行环境复杂多变,如车站、隧道等场景可能导致卫星信号被遮挡,仅依靠BDS难以保障列车定位的连续性和准确性。为此,采用卡尔曼滤波算法实现BDS与惯性导航系统(INS)的组合解算,以提高列车定位的连续性和定位精度。采用基于Simulink的形式化建模方法,根据BDS/INS组合定位架构及其数据处理算法仿真逻辑,构建完整的BDS/INS组合系统仿真模型,利用青藏铁路现场采集的数据验证组合定位模型及算法的有效性。仿真结果表明:基于BDS/INS的列车组合定位在隧道场景下仍能输出定位结果,保障了列车定位的连续性;在开阔场景下,与单BDS定位模式相比,BDS/INS组合定位北向精度提高11.20%,东向精度提高4.17%,水平方向精度提高9.41%。