Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.

A Fuzzy Logic-Based Framework for Route Choice in Vehicle Navigation Systems

Vehicle Navigation Systems (VNS) is an important component of Intelligent Transportation Systems (ITS). These Systems are designed to assist drivers in making pre trip and enroute travel choice decisions, and typically, they must provide route choice, route guidance and other related services. Although there have been a lot of existed systems in the market, and most of them used lots of contemporary technologies, they are believed short of ″true intelligence″, because they paid little attention to the subjective issues in driver′s route choice behavior, such as travel objectives and personal preferences, etc. \;However, the VNS is designed for its users, and the successful implementation of VNS is largely dependent on the driver′s acceptance. If the driver feels that the VNS can′t give him (her) a satisfactory choice, he (she) will not use it, then, the marketing value of VNS will decline. And on the whole, the transport benefit that is mainly gained by the wide use of ITS will lost. \;Supported by the research project of ″Beijing Intelligent Urban Transportation Systems″, this paper presents a conceptual model to deal with this problem. We first defined the driver′s objective as a linguistic statement that has a set of attributes. These attributes are then treated as the fuzzy sets on the universal of all the existed routes. By determining each attribute′s membership function and assign driver dependent perception to these attributes, we can change the multi criteria route choice problem into a fuzzy logic based decision making problem. Then, to meet the demands of dynamic real time route selection, we use a limited routes set for choice and can swiftly get a satisfactory solution that we think is the driver′s actually needs.

A multi-objective antidisturbance robust filter for SINS/GPS navigation systems

Purpose-A full-order multi-objective anti-disturbance robust filter for SINS/GPS navigation systems with multiple disturbances is designed.Generally,the unmodeled dynamics,the external environmental disturbance and the inertial apparatus random drift may exist simultaneously in an integrated navigation system,which can be classified into three type of disturbances,that is,the Gaussian noise,the norm bounded noise and the time correlated noise.In most classical studies,the disturbances in integrated navigation systems are classified as Gaussian noises or norm bounded noises,where the Kalman filtering or robust filtering can be employed,respectively.While it is not true actually,such assumptions may lead to conservative results.The paper aims to discuss these issues.Design/methodology/approach-The Gaussian noises,the norm bounded noises and the time correlated noises in the integrated navigation system are considered simultaneously in this contribution.As a result,the time correlated noises are augmented as a part of system state of the integrated navigation system error model,the relative integrated navigation problem can be transformed into a full-order multi-objective robust filter design problem for systems with Gaussian noises and norm bounded disturbances.Certainly,the errors of the time correlated noises are estimated and compensated for high precision navigation purpose.Sufficient conditions for the existence of the proposed filter are presented in terms of linear matrix inequalities(LMIs)such that the system stability is guaranteed and the disturbance attenuation performance is achieved.Findings-Simulations for SINS/GPS integrated navigation system given show that the proposed full-order multi-objective anti-disturbance filter,has stronger robustness and better precision when multiple disturbances exist,that is,the present algorithm not only can suppression the effect of white noises and norm bounded disturbance but also can estimate and compensate the modeled disturbance.Originality/value-The proposed algorithm has stronger anti-disturbance ability for integrated navigation with multiple disturbances.In fact,there exist multiple disturbances in integrated navigation system,so the proposed scheme has important significance in applications.

Scene image recognition with knowledge transfer for drone navigation

In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.

ROTATING FIBER OPTIC GYRO STRAP-DOWN INERTIAL NAVIGATION SYSTEM WITH THREE ROTATING AXES

The principle of the inertial navigation system（INS） with rotating inertial measurement unit （IMU） is analyzed. A new IMU is established to rotate round each axis in three directions. Then, the related error models for the designed system during rotating are deduced and the improved system is built. Finally, the performance simulation of the proposed system is provided. The simulation result indicates that the designed system can improve the accuracy of the roll and the pitch as well as heading by rotating three axes, thus guaranting the heading accuracy. Moreover, based on the principle of rotation at six different positions, such structure can carry out real-time calibration, and improve the system performance.

Robust and Accurate Monocular Visual Navigation Combining IMU for a Quadrotor

In this paper, we present a multi-sensor fusion based monocular visual navigation system for a quadrotor with limited payload, power and computational resources. Our system is equipped with an inertial measurement unit (IMU), a sonar and a monocular down-looking camera. It is able to work well in GPS-denied and markerless environments. Different from most of the keyframe-based visual navigation systems, our system uses the information from both keyframes and keypoints in each frame. The GPU-based speeded up robust feature (SURF) is employed for feature detection and feature matching. Based on the flight characteristics of quadrotor, we propose a refined preliminary motion estimation algorithm combining IMU data. A multi-level judgment rule is then presented which is beneficial to hovering conditions and reduces the error accumulation effectively. By using the sonar sensor, the metric scale estimation problem has been solved. We also present the novel IMU+3P (IMU with three point correspondences) algorithm for accurate pose estimation. This algorithm transforms the 6-DOF pose estimation problem into a 4-DOF problem and can obtain more accurate results with less computation time. We perform the experiments of monocular visual navigation system in real indoor and outdoor environments. The results demonstrate that the monocular visual navigation system performing in real-time has robust and accurate navigation results of the quadrotor. © 2014 Chinese Association of Automation.

A Technical Framework for Selection of Autonomous UAV Navigation Technologies and Sensors

The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.

Machine-to-Machine Collaboration Utilizing Internet of Things and Machine Learning

Machine-to-Machine (M2M) collaboration opens new opportunities where systems can collaborate without any human intervention and solve engineering problems efficiently and effectively. M2M is widely used for various application areas. Through this reported project authors developed a M2M system where a drone and two ground vehicles collaborate through a base station to implement a system that can be utilized for an indoor search and rescue operation. The model training for drone flight paths achieves almost 100% accuracy. It was also observed that the accuracy of the model increased with more training samples. Both the drone flight path and ground vehicle navigation are controlled from the base station. Machine learning is utilized for modelling of drone’s flight path as well as for ground vehicle navigation through obstacles. The developed system was implemented on a field trial within a corridor of a building, and it was demonstrated successfully.

Status of UnDifferenced and Uncombined GNSS Data Processing Activities in China

With the continued development of multiple Global Navigation Satellite Systems(GNSS)and the emergence of various frequencies,UnDifferenced and UnCombined(UDUC)data processing has become an increasingly attractive option.In this contribution,we provide an overview of the current status of UDUC GNSS data processing activities in China.These activities encompass the formulation of Precise Point Positioning(PPP)models and PPP-Real-Time Kinematic(PPP-RTK)models for processing single-station and multi-station GNSS data,respectively.Regarding single-station data processing,we discuss the advancements in PPP models,particularly the extension from a single system to multiple systems,and from dual frequencies to single and multiple frequencies.Additionally,we introduce the modified PPP model,which accounts for the time variation of receiver code biases,a departure from the conventional PPP model that typically assumes these biases to be time-constant.In the realm of multi-station PPP-RTK data processing,we introduce the ionosphere-weighted PPP-RTK model,which enhances the model strength by considering the spatial correlation of ionospheric delays.We also review the phase-only PPP-RTK model,designed to mitigate the impact of unmodelled code-related errors.Furthermore,we explore GLONASS PPP-RTK,achieved through the application of the integer-estimable model.For large-scale network data processing,we introduce the all-in-view PPP-RTK model,which alleviates the strict common-view requirement at all receivers.Moreover,we present the decentralized PPP-RTK data processing strategy,designed to improve computational efficiency.Overall,this work highlights the various advancements in UDUC GNSS data processing,providing insights into the state-of-the-art techniques employed in China to achieve precise GNSS applications.

Design and Experiments of Ultrasound Image-Guided Multi-DOF Robot System for Brachytherapy

To implant radioactive seeds through a needle precisely and safely, a novel multi-DOF surgical robotic system is presented in this paper for percutaneous prostate intervention through the patient’s perineum under real-time ultrasound image guidance. The proposed robot, which is designed with 9-DOF, consists of a 3-DOF automatic location platform for position adjustment, 2-DOF for automatic ultrasonic probe adjustment mounted with electromagnetic trackers, and 4-DOF for manually adjusting the guided template. Meanwhile, a new registration method based on the quaternion algorithm and least square method is developed, and the needle insertion is performed under the real-time guidance of a navigation system. Furthermore, the robot system has undergone some preliminary experiments with a laser tracker to evaluate the repeatability and accuracy of the robot system. The location error of the puncture needle tip can be controlled under 0.7 mm in air for the whole robotic system. The acquired results endorse the precision of the robot system for prostate seed implantation brachytherapy. © 2017, Tianjin University and Springer-Verlag GmbH Germany.

Three-point method of differential correction of coordinates and pseudo-range in GPS

This paper presents the interpolation method of generating the differential correction to coordinates and pseudorange.As a consequence,to improve the quality and stability of the generated correction of amendments,it is advisable to use a system of three reference points.Experiment demonstrates the advantages of the interpolation method in comparison with the standard method of differential correction.

NONLINEAR FILTERING ALGORITHM FOR IN S INITIAL ALIGNMENT

The initial alignment error equation of an INS (Inertial Navigation System) with large initial azimuth error has been derived and nonlinear characteristics are included. When azimuth error is fairly small, the nonlinear equation can be reduced to a linear one. Extended Kalman filter, iterated filter and second order filter formulas are derived for the nonlinear state equation with linear measurement equation. Simulations results show that the accuracy of azimuth error estimation using extended Kalman filter is better than that of using standard Kalman filter while the iterated filter and second order filter can give even better estimation accuracy.

INVESTIGATION OF TIGHTLY COUPLED SINS/GPS INTEGRATION MIDCOURSE GUIDANCE FOR AIR-TO-AIR MISSILES

The applied problems of SINS/GPS integration navigation system existing in midcourse guidance of air to air missiles have been investigated recently. In comparison with those investigations existing in current publications, a new tightly coupled SINS/GPS integration navigation system for air to air missiles, based on the decorrelated pseudo range approach, is presented in this paper. Because of high jamming and dynamic of air to air missiles, inertial velocity aiding GPS receiver is used to provide a more accurate, jam resistant measurement for midcourse guidance systems. A tracking error estimator is designed to distinguish the correlation that existed between pseudo range measurements and inertial information. It is found better to regard inertial velocity aiding errors as the noise of which statistical properties are unknown. So using mixed Kalman/minimax filtering theory, one can obtain the new tracking error estimator with simple and robust algorithm through constructing a composite filter consisting of two parts: Kalman filter for the noise of known statistics and minimax filter for the unknown. In order to ensure this simple estimator stability, a new method is proposed to choose its parameters, based on Khargonekars work. Moreover, it is demonstrated that the given method also ensures the proposed estimator optimality. All the work mentioned above is involved in the tightly coupled SINS/GPS integration midcourse system design in which a set of low accuracy inertial components is shared by SINS and autopilot. Simulation results of a certain type of air to air missile are presented. Due to decorrelation by the tracking error estimator, only small white noise of pseudo range measurements remains. So it is shown that application of the new midcourse guidance system results in better guidance accuracy, higher jam resistance.

STUDY OF FAST TRANSFER ALIGNMENT ON MOVING BASE

This paper holds that the key to improve the hitting rate of air-to-air missiles is to decrease the error of initial alignment of the inertial navigation system. Therefore, considering that the alignment should be completed within a specified short time, this paper presents the theory of transfer alignment and the computing way of accuracy in an air-to-air missile, where the platform inertial navigation system, or master INS, is adopted on aircraft, and the strap-down inertial navigation system, or slave INS, is used on missile. It emphasizes the idea of transfer alignment, that is, calibration of the slave INS is based on the master platform, and adopts a reasoning measure to deal with the installing-error-angle. And it is proved by simulation that the transfer alignment can be quickly achieved.

Simulator of GNSS signals for testing of space objects receivers in the earth conditions

A technique for testing space object receivers using global navigation satellite system （GNSS） signal simulator of the navigation field is proposed. Its structure consists of two blocks which allow synthesizing the scenario of reciprocal displacement of the receiver relative to navigation satellites and their signals. In the first block, according to the known coordinates of the receiver which are specified in tabular form or analytically, the distances between the receiver and the navigation satellites are calculated as well as their relative velocities. According to these data, the second block synthesizes the signals of navigational travelers with the specified characteristics which are transmitted via the air or cable with a given attenuation to the receiver. This allows testing on the earth receivers for airplanes and space objects under different scenarios of their movement, which not only reduces the risk of problems during the flight, but also avoids significant economic costs. Based on real data obtained by approaching two spacecraft using a simulator, the receiver was tested, which shows the promise of the proposed technology.

TanDEM-X Autonomous Formation Flying System： Flight Results

The TanDEM-X mission is a scientific and commercial Earth observation mission comprising two satellites flying in close formation. The formation maintenance can be advantageously performed by an onboard autonomous system, which reduces the operational efforts, provides a shorter reaction time in case of contingencies and increases the control performance. The TanDEM-X Autonomous Formation Flying （TAFF） system has been developed for this purpose and is intended to replace the ground-based formation keeping activities during routine operations. TAFF has been activated for the first time in October 2010 for commissioning, during which the autonomous usage of thrusters was prohibited. Afterwards, a closed-loop campaign was successfully conducted in March 2011, demonstrating the capability of TAFF to maintain autonomously the formation. After a brief technical description of the system, the paper presents the key results gained during the commissioning phase and the closed-loop campaign,

Performance evaluation of neural network TEC forecasting models over equatorial low-latitude Indian GNSS station

Global Positioning System(GPS)services could be improved through prediction of ionospheric delays for satellite-based radio signals.With respect to latitude,longitude,local time,season,solar cycle and geomagnetic activity the Total Electron Content(TEC)have significant variations in both time and space.These temporal and spatial TEC variations driven by interplanetary space weather conditions such as solar and geomagnetic activities can degrade the communication and navigation links of GPS.Hence,in this paper,performance of TEC forecasting models based on Neural Networks(NN)have been evaluated to forecast(1-h ahead)ionospheric TEC over equatorial low latitude Bengaluru e12:97+N;77:59+ET,Global Navigation Satellite System(GNSS)station,India.The VTEC data is collected for 2009 e2016(8 years)during current 24 th solar cycle.The input space for the NN models comprise the solar Extreme UV flux,F10.7 proxy,a geomagnetic planetary A index(AP)index,sunspot number(SSN),disturbance storm time(DST)index,solar wind speed(Vsw),solar wind proton density(Np),Interplanetary Magnetic Field(IMF Bz).The performance of NN based TEC forecast models and International Reference Ionosphere,IRI-2016 global TEC model has evaluated during testing period,2016.The NN based model driven by all the inputs,which is a NN unified model(NNunq)has shown better accuracy with Mean Absolute Error(MAE)of 3.15 TECU,Mean Square Deviation(MSD)of 16.8 and Mean Absolute Percentage Error(MAPE)of 19.8%and is 1 e25%more accurate than the other NN based TEC forecast models(NN1,NN2 and NN3)and IRI-2016 model.NNunq model has less Root Mean Square Error(RMSE)value 3.8 TECU and highest goodness-of-fit(R2)with 0.85.The experimental results imply that NNunq/NN1 model forecasts ionospheric TEC accurately across equatorial low-latitude GNSS station and IRI-2016 model performance is necessarily improved as its forecast accuracy is limited to 69 e70%.

REVISITING THE DOPPLER FILTER OF LEO SATELLITE GNSS RECEIVERS FOR PRECISE VELOCITY ESTIMATION

The theoretical aspects of the precise velocity determination of Low Earth Orbit (LEO) satellites'on board Global Navigation Satellite Systems (GNSS) receivers are derived. It shows that the receiver's Phase Lock Loop (PLL) is required to feature extremely small group delay within its low frequency band, which is in contrast to existing work that proposed wide band linear phase filters. Following this theory, a Finite Impulse Response (FIR) filter is proposed. To corroborate, the proposed FIR filter and an Infinite Impulse Response (IIR) filter lately proposed in literals are implemented in a LEO satellite onboard GNSS receiver. Tests are conducted using a third party commercial GPS signal generator. The results show that the GNSS receiver with the proposed FIR achieves 11 mm/s R.M.S precision, while the GNSS receiver with the IIR filter has a filter-caused velocity error that can not be ignored for space borne GNSS receivers.

Comparison of availability and reliability among different combined-GNSS/RNSS precise point positioning

With emergence of the BeiDou Navigation Satellite System(BDS), the Galileo Satellite Navigation System(Galileo), the Quasi-Zenith Satellite System(QZSS)and the restoration of the Global Navigation Satellite System(GLONASS), the single Global Positioning System(GPS) has been gradually expanded into multiple global and regional navigation satellite systems(multi-GNSS/RNSS). In view of differences in these 5 systems, a consolidated multi-GNSS/RNSS precise point positioning(PPP) observation model is deduced in this contribution. In addition, the performance evaluation of PPP for multi-GNSS/RNSS is conducted using a large number of the multi-GNSS experiment(MGEX) station datasets. Experimental results show that multi-GNSS/RNSS can guarantee plenty of visible satellites effectively. Compared with single-system GPS, PDOP, HDOP, and VDOP values of the multi-GNSS/RNSS are improved by 46.8%, 46.5% and 46.3%, respectively. As for convergence time, the static and kinematic PPP of multi-GNSS/RNSS are superior to that of the single-system GPS, whose reliability, availability, and stability drop sharply with the increasing elevation cutoff. At satellite elevation cutoff of 40 °, the single-system GPS fails to carry out continuous positioning because of the insufficient visible satellites, while the multi-GNSS/RNSS PPP can still get positioning solutions with relatively high accuracy, especially in the horizontal direction.

Airborne GNSS-Receiver Threat Detection in No-Fading Environments

Jamming and spoofing detection of global navigation satellite systems(GNSS)is of great importance.Civil and military aerial platforms use GNSS as main navigation systems and these systems are main target of threat attacks.In this paper a simple method based on different empirical probability density functions of successive received signal powers and goodness of fit tech-nique is proposed for airborne platforms such as unmanned aerial vehicle(UAV),in no fading envi-ronment.The two different paths between UAV-satellite and UAV-threat,experience different empirical probability density functions which can be used to distinguish between authentic and threat signals.Simulation results including detection and false alarm probabilities show good perfor-mance of proposed method as well as low computational burden.