Showing papers on "GPS/INS published in 2019"

Covert Spoofing Algorithm of UAV Based on GPS/INS-Integrated Navigation

Abstract: A covert spoofing algorithm of an unmanned aerial vehicle (UAV) based on GPS/inertial-navigation-system-integrated navigation is analyzed in this paper. The proposed algorithm theoretically proves the fact that when the acceleration component of the counterfeit GPS signal is the difference between the UAV's current acceleration and the spoofing control input, the UAV can be covert spoofed. To avoid adverse consequences (detection or crash) caused by frequent changes in UAV flight during the GPS spoofing attacks, the proposed algorithm requires that the deception trajectory planned by the GPS spoofer is slowly changing relative to the reference trajectory, which is pre-set by the UAV. Simulation results have verified the correctness of the proposed covert spoofing algorithm of UAV. Moreover, the spoofing effect is more distinct when the deception trajectory planning is considered.

Security of GPS/INS Based On-road Location Tracking Systems

Abstract: Location information is critical to a wide variety of navigation and tracking applications. GPS, today's de-facto outdoor localization system has been shown to be vulnerable to signal spoofing attacks. Inertial Navigation Systems (INS) are emerging as a popular complementary system, especially in road transportation systems as they enable improved navigation and tracking as well as offer resilience to wireless signals spoofing and jamming attacks. In this paper, we evaluate the security guarantees of INS-aided GPS tracking and navigation for road transportation systems. We consider an adversary required to travel from a source location to a destination and monitored by an INS-aided GPS system. The goal of the adversary is to travel to alternate locations without being detected. We develop and evaluate algorithms that achieve this goal, providing the adversary significant latitude. Our algorithms build a graph model for a given road network and enable us to derive potential destinations an attacker can reach without raising alarms even with the INS-aided GPS tracking and navigation system. The algorithms render the gyroscope and accelerometer sensors useless as they generate road trajectories indistinguishable from plausible paths (both in terms of turn angles and roads curvature). We also design, build and demonstrate that the magnetometer can be actively spoofed using a combination of carefully controlled coils. To experimentally demonstrate and evaluate the feasibility of the attack in real-world, we implement a first real-time integrated GPS/INS spoofer that accounts for traffic fluidity, congestion, lights, and dynamically generates corresponding spoofing signals. Furthermore, we evaluate our attack on ten different cities using driving traces and publicly available city plans. Our evaluations show that it is possible for an attacker to reach destinations that are as far as 30 km away from the actual destination without being detected. We also show that it is possible for the adversary to reach almost 60--80% of possible points within the target region in some cities. Such results are only a lower-bound, as an adversary can adjust our parameters to spend more resources (e.g., time) on the target source/destination than we did for our performance evaluations of thousands of paths. We propose countermeasures that limit an attacker's ability, without the need for any hardware modifications. Our system can be used as the foundation for countering such attacks, both detecting and recommending paths that are difficult to spoof.

Robust GPS/INS/DVL Navigation and Positioning Method Using Adaptive Federated Strong Tracking Filter Based on Weighted Least Square Principle

Abstract: Multi-sensor integrated positioning technique that combines complementary features of the global positioning system (GPS) and inertial navigation system (INS) for navigation in challenging urban environments has been a hot research area. A variety of algorithms have been proposed over the past two decades for this well-studied field. However, with the increasing demands of seamless positioning, traditional GPS/INS integrated technique faces rigorous challenges, especially in GPS-denied environment, where traditional techniques cannot be applied directly. To improve the precision and robustness of the navigation system, a novel hybrid GPS/INS/Doppler velocity log (DVL) positioning method is proposed, which introduces DVL as the reference information to assist the GPS module to correct the divergence error of INS. A new robust adaptive federated strong tracking Kalman filter (RAFSTKF) algorithm is also presented for data fusion, which has the advantage of robustness with respect to the uncertainty of the system model. Meanwhile, we introduce the least square principle and adaptively adjust information sharing factors to obtain the optimal estimation, which can improve the reliability of the overall system. The theoretical analysis and simulation results demonstrate the effectiveness of the proposed hybrid GPS/INS/DVL positioning method based on RAFSTKF. In addition, the tracking performance of the proposed method outperforms that of traditional federated Kalman filter.

Combination of iterated cubature Kalman filter and neural networks for GPS/INS during GPS outages.

Abstract: To improve the performance of the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation system, current research studies merely combine neural networks with nonlinear filter methods. Few studies focus on how to optimize the parameters of the neural network and how to further improve the small error accumulated into the next filter step due to the imprecise design of the filter when setting the initial parameters in the GPS/INS integrated system. In this article, a dual optimization method consisting of an iterated cubature Kalman filter-Feedforward Neural Network (ICKF-FNN) and a radial basis function-cubature Kalman filter (RBF-CKF) is proposed to compensate the position and velocity errors of the integrated system during GPS outages. The prominent advantages of the proposed method include the following. (i) The ICKF is designed to optimize the parameters of the introduced FNN adaptively and obtain an appropriate internal structure when GPS is available, which improves the accuracy of the training model. (ii) The RBF establishes the relationship between filter parameters and the optimal estimation errors, reducing the errors caused by inaccurate predicted observation during GPS outages. (iii) The proposed dual optimization method takes advantages over other combination algorithms under different moving conditions or even during long period of GPS outages, which shows its great stability. Experimental results show that the root mean squared error of the east position is reduced by 85.79% to 3.2187 m using the proposed strategy during turning movement and the east velocity error accumulation rate decreases by 92.69% during the long straight movement of 250 s. These results are from offline processing.

Performance Improvement of Time-Differenced Carrier Phase Measurement-Based Integrated GPS/INS Considering Noise Correlation.

Abstract: In this study, we combined a time-differenced carrier phase (TDCP)-based global positioning system (GPS) with an inertial navigation system (INS) to form an integrated system that appropriately considers noise correlation. The TDCP-based navigation system can determine positions precisely based on high-quality carrier phase measurements without difficulty resolving integer ambiguity. Because the TDCP system contains current and previous information that violate the format of the conventional Kalman filter, a delayed state filter that considers the correlation between process and measurement noise is utilized to improve the accuracy and reliability of the TDCP-based GPS/INS. The results of a dynamic simulation and an experiment conducted to verify the efficacy of the proposed system indicate that it can achieve performance improvements of up to 70% and 60%, respectively, compared to the conventional algorithm.

Improving GPS/INS Integration Using FIKF‐Filtered Innovation Kalman Filter

Abstract: The Kalman Filter (KF) is the most popular approach in sensor fusion and navigation applications. Improving the accuracy of estimation may increase the navigation accuracy. KF is the best ...

A state-constrained tracking approach for Kalman filter-based ultra-tightly coupled GPS/INS integration

Abstract: The traditional design of tracking loop in global positioning system (GPS), known as the combination of phase-locked loop and delay-locked loop, is fragile under complex environments. With the increasing requirements for tracking performance under more harsh applications, several implementations have emerged in recent years, among which Kalman filter (KF)-based tracking loop is widely used due to its adaptive nature and robust feature, and it could achieve a higher dynamics performance with the aid of inertial navigation system (INS). However, even more critical conditions, such as severe fading, abrupt phase changes, and signal interference coexisting with high user dynamics, are now challenging the traditional carrier tracking architectures, thus calling for the enhancement of robust carrier tracking techniques. A state-constrained Kalman filter-based (SC-KF) approach is proposed to restrict the errors of the tracking loop and to enhance the robustness of the tracking process in high dynamics and signal attenuation environments. In the SC-KF, the system model of INS-aided KF-based tracking loop is built from a perspective of control theory. Based on the ultra-tight GPS/INS integrated scheme, a Doppler-constrained method and moving horizon estimation architecture are introduced to correct the Doppler state and the code, carrier phase states in KF-based tracking loop, respectively. Software and hardware simulations indicate that the proposed architecture has a better performance in tracking and navigation domains comparing with the conventional INS-aided KF-based tracking loop under severe environments.

Auto Takeoff and Precision Terminal-Phase Landing using an Experimental Optical Flow Model for GPS/INS Enhancement

Abstract: The high estimated position error in current commercial-off-the-shelf (GPS/INS) impedes achieving precise autonomous takeoff and landing (TOL) flight operations. To overcome this problem, in this p...

GPS/INS integration based on adaptive interacting multiple model

Abstract: The extended Kalman filter (EKF) had widely been used in the inertial navigation system (INS) and global positioning system (GPS) integrated navigation system. Nevertheless, the EKF contains instability caused by linearisation and numerous calculations of the Jacobian matrix. To solve the problem, the adaptive interacting multiple model (AIMM) filter algorithm is proposed in order to achieve a better navigation solution. The soft-switching feature, which is offered by interacting multiple model, allows conversion of process noise between lower and upper limits, meanwhile the measurement covariance is adjusted online by Sage adaptive filter. Considering the need to update the pseudo-range and Doppler data, the updating strategy of classification measurement is proposed. The results of the GPS/INS integrated navigation are estimated in the form of data of real ship, and experimental results indicate that the higher position accuracy can be obtained in AIMM filter.

Two-Stage Kalman Filter for Estimation of Wind Speed and UAV Flight Parameters based on GPS/INS and Pitot Tube Measurements

Abstract: Two-stage Kalman filter based estimation algorithm was developed for wind speed and UAV motion parameters. In the first stage., wind speed estimation algorithm is used based on GPS measurements and dynamic pressure measurements. The wind speed is estimated by Kalman filter using GPS and Air Data System (ADS)measurements. For this purpose., Extended Kalman Filter (EKF)was designed., and as state variables., the wind velocity components and ADS pitot scale factor are considered. In the second stage., the state parameters of the UAV dynamic model are estimated using GPS and IMU measurements. The second stage filter uses GPS position measurements., IMU orientation angles and speed measurements as well as the wind speed value estimated by the first stage filter.

Geometric and statistical interpretation of correlation between fault tests in integrated GPS/INS systems

Abstract: Abstract Fault detection and identification (FDI) in either a stand-alone GPS or in integrated GPS/INS systems is essential for improving the quality of positioning, navigation, and many other applications. The assumption that the observations include a single fault has been considered intensively in literature. However, this assumption may not necessarily be valid due to the fact that multiple faults may exist simultaneously. In this study, separability of multiple faults in GPS/INS integration systems has been analysed geometrically and statistically. This has been achieved through testing how large correlation coefficient between any pair of fault tests statistics increases the probability of faults misidentification. In addition, a new calculation procedure of correlation coefficient when four faults appear in the observations has been developed. This procedure considers calculation the correlation between a single and a punch of measurements combined together. The results show that there is a strong relationship between the value of correlation coefficient and the probability of misidentification. Furthermore, a significant relationship between the correlation and the fault test values can be found when splitting the measurements combinations into groups based on the combination similarity. Nevertheless, this relationship can be defined without splitting the measurements into groups when using a new correlation procedure for four faults case. The geometric representation shows that large correlation coefficient reflects small angle between the correlation and the x-axis; whereas the angle between the fault-test vectors and the x-axis becomes wider when a tiny correlation exist.

LS-SVM-based self-adaptive fault-tolerant GPS/INS integrated navigation method

Abstract: The invention provides an LS-SVM-based self-adaptive fault-tolerant GPS/INS integrated navigation method. The method is briefly characterized in that if an LS/SVM considers that a certain current sub-navigation system breaks down, the weight of the sub-navigation system in information fusion is lowered, the weight for other sub-navigation systems is increased, and therefore fault isolation is achieved. When the LS-SVM detects that fault information is repaired, the weight of the corresponding navigation sub-system is recovered to be normal, and therefore it is ensured that all normally-workingsub-navigation systems take part in information fusion, and the purpose of improving the precision of the integrated navigation system is achieved. Compared with the prior art, the navigation methodhas the advantages that when a fault exists, fault detection and isolation can be effectively achieved, and it is ensured that the navigation precision is not affected by fault information; when not fault exists, optimal information fusion of all the normally-working sub-navigation systems can be effectively achieved, and the navigation precision of the integrated navigation system is improved tothe maximum extent.

GPS/INS integrated navigation method based on self-learning cubature Kalman filter

Abstract: The invention discloses a GPS/INS integrated navigation method based on self-learning cubature Kalman filter. Running stages of a GPS/INS integrated navigation system are divided into a training stagand an error compensation stage. At the training stage being a GPS signal effective stage, a self-learning Kalman filter device formed by two cyclic filtering subsystems carries out optimal estimationon a speed error and a position error of an INS by using the speed difference and position difference of the INS and the GPS as observed quantities, thereby realizing the self-learning function. At the error compensation stage being a GPS signal out-of-lock stage, the Kalman filter device has the function of predicting the observation quantities through self-learning, the prediction result of theLSTM network can be trusted fully, seamless navigation under the out-of-lock condition of the GPS signal is realized, and the optimal estimation error value of the Kalman filter device is compensated, thereby improving the positioning accuracy of intelligent vehicle navigation in complex environments. The GPS/INS integrated navigation method can be applied occasions like vehicle navigation positioning in a complex urban environment; and the autonomy of navigation positioning is improved effectively and thus the intelligence degree of the vehicle is increased.

Calibration plate for multi-line laser radar and GPS\INS calibration and method thereof

Abstract: The invention discloses a calibration plate for multi-line laser radar and GPS\INS calibration, and a method thereof. A GPS measurement antenna is embedded in the front center of the calibration plate, and a positioning module and a data transmission module are installed on the back of the calibration plate. The method comprises the following steps that the system remains stationary and acquires a set of time-synchronized radar coordinates {Pi} and geodetic coordinates {Pj}; the RANSAC algorithm is used to obtain a rotation matrix Rs and a translation matrix Ts; {Rs, Ts} is used combined with GPS\INS to obtain a rotation matrix R0 and translation matrix T0; the system keeps moving, acquires a set of time-synchronized radar coordinates {Pn} and geodetic coordinates {Pm}, and simultaneously records the attitude and geodetic coordinates of the station center coordinate system; the coordinates {Pn}' of {Pn} in the geodetic coordinate system are calculated according to the above coordinates combined with the initial value {R0, T0}, an objective function is constructed; and the optimized rotation matrix R and translation matrix T are obtained by solving the over-determined equation by SVD. The invention has the advantage of providing a more accurate calibration result for multi-line laser radar and GPS\INS compared to the prior art in an outdoor environment.

GPS/INS integrated navigation method and device, storage medium and electronic equipment

Abstract: The disclosure, which relates to the technical field of navigation and mapping, provides a GPS/INS integrated navigation method and device, a storage medium and electronic equipment. The method comprises: acquiring a first type of position and a first type of velocity of a carrier by using a GPS system and acquiring a second type of position and a second type of velocity of the carrier by using anINS system; according to a difference between the first type of position and the second type of position as well as a difference between the first type of velocity and the second type of velocity, carrying out Kalman filtering to obtain state characteristics of the carrier; determining a time synchronization error between the GPS system and the INS system based on the state characteristics and carrying out compensation on the first type of position and the first type of velocity or on the second type of position and the second type of velocity based on the time synchronization error; and thencarrying out fusion on the first type of position, the first type of velocity, the second type of position and the second type of velocity after compensation to navigate the carrier. Therefore, the impact of time asynchronization on the integrated navigation system can be reduced; the navigation accuracy can be improved; and the requirements on hardware resources can be reduced.

Implementation of Path-Following Algorithm for an Unmanned Surface Vehicle Using Viam-Navi GPS/INS Module

Abstract: With the understanding and development of Unmanned Surface Vehicles (USV), will be beneficial to not only the environmental monitoring, military, but also in many commercial applications. The vehicle adopts a catamaran design making it easy to assemble, transport and integrated sensors for the desired applications. Sensor data is collected to estimate, improve the quality of the sensors and display real-time monitor environmental parameters. This paper describes the control system which provides path following, under external forces: wind, waves and currents. The guidance and control system are developed to guide the vehicle to follow waypoints using the Line of Sight (LOS) algorithm based on the input of the sensors. These waypoints are automatically received from ground station and stored on an on-board computer. The navigation system is one of the most important USV subsystems. To improve the ability of navigation for vehicles, Viam-Navi GPS/INS Module: integration of Inertial Navigation System (INS) and Global Positioning System (GPS) is developed with low-cost, highly accurate and stable navigation system. Beside, modelling and simulation are presented for developing an appropriate guidance and control systems, the results of which is applied to the designed platform. Finally, experiment of these navigation, controllers and guidance laws show that a low deviation between the vehicle and path.

Feature Coordinate Estimation by Multi-Baseline Method utilizing Monocular Vision/GPS/INS

Abstract: 3D map generation methodologies have been actively studied for many years. They are usually based on images acquired by aerial or land vehicles. These methods essentially depend on location information where images are captured. To allocate positions of landmarks in the global frame, pixels corresponding to them are manually identified in the images. In general, conventional photogrammetric operation methods rely on Ground Control Points (GCPs) for reliable geo-referencing information. However, it requires excessive cost and time to survey GCPs beforehand. On the other hand, the Direct-Georeferencing (DG) methods can reduce the time-consuming procedures considerably with high-quality Global Positioning System (GPS) and Inertial Navigation System (INS). However, it is difficult to meet the accuracy requirement for many mapping applications with low-cost Micro Electro Mechanical Systems (MEMS) Inertial Measurement Unit (IMU) and GPS. Due to its insufficient accuracy, it is necessary to compensate their navigation solutions for the accurate 3D information of point clouds. In this paper, we propose an efficient method to estimate the location of image-based point clouds by the same target features identified in the two consecutive images. In the proposed method, GPS/INS Kalman enables considering time-differenced multi-baselines. Image-based point clouds are compensated and converted to absolute coordinates directly. A simulator was built to evaluate the performance of the proposed method systematically. The simulation result confirms the feasibility of the proposed method.

Self-position estimation device

Abstract: To provide a self-position estimation device capable of stabilizing self-position estimation accuracy even when a vehicle travels in a section where the number of lanes changes.SOLUTION: A self-position estimation device mounted on a vehicle comprises an on-vehicle camera 110, a self-vehicle state quantity sensor unit 120, a satellite positioning unit 130, a map data storage unit 140 and a position estimation unit 150 for estimating a self-position of the vehicle on a map on the basis of an image, state quantity and map data. The position estimation unit includes a number-of-lanes increase/decrease determination unit 151 for recognizing an increase/decrease section of the number of lanes on a road on the basis of sensing by the on-vehicle camera and a position correction unit 152 for setting weighting of an estimated position based on the map data of the position estimation unit to be smaller than that for the case where the increase/decrease section of the number of lanes cannot be recognized in order to correct the estimated position based on the map data.SELECTED DRAWING: Figure 3

Feature coordinate estimation by time differenced vision/GPS/INS

Abstract: This paper proposes a multi-baseline method to estimate absolute coordinates of point clouds and the camera attitude parameter utilizing feature points in successive images. Conventionally, 3D map generation methodologies have been based on images acquired by aerial or land vehicles. Pixels corresponding to known landmarks are manually identified at first. Next, the coordinates are directly geo-referenced and automatically allocated to pixels with high-quality Global Positioning System (GPS) and Inertial Navigation System (INS). However, it is difficult to obtain accurate coordinates by the conventional methodology with low-cost GPS and INS. With camera positions and attitude parameters, image-based point clouds can be compensated accurately. A simulation was carried out to evaluate the performance of the proposed method.

Accuracy Improvement of GPS/INS Navigation System Using Extended Kalman Filter

Abstract: Inertial navigation is a method for determining position and orientation of a vehicle, which operates according to Newton’s laws of motion. Due to continual increase of output error because of measurement noise, bias, misalignment and so on, one may need one or more additional navigation systems to improve accuracy in long-term navigation. In this paper, the error compensation based on GPS/INS data fusion algorithm is studied. Then, by designing a DSP processor-based hardware, GPS and INS data are recorded and GPS/INS data fusion algorithm is implemented. Results indicate that the accuracy of the positioning is improved and position, velocity, and orientation errors are confined to a limited range.

Issues of Improving the Noise Immunity of GPS/INS Aircraft Navigation Systems

Abstract: The paper is devoted to questions on increasing of noise immunity of GPS/INS navigation systems containing GPS and inertial navigation systems. Analysis of effectual methods for digital processing of noisy signals of GPS/INS navigation systems is actual task. At the same time there is another direction of development of GPS navigation systems, based on modification of aerial tract and filtration devices. Different adaptive methods of multi-aerial receipt can be stressed out as an example. As regards the filtration method, there are some additional possibilities for filtration of noisy GPS signals upon presence of a priory or a posteriori data on energy-frequency characteristics of noise signal. The comparative analysis of methods for increasing of noise immunity of integrated GPS/INS navigation systems is carried out. The possibility for optimization of averaged integrated value of modified indicator, named as effective ratio of power of carrier signal on noise power regarding the case of utilization of noise signals pertaining to broad class of noise signals characterized by presence of integrated limitation condition imposed on amplitude-frequency spectrum. The optimization task on increase of averaged integrated parameter indicating the effective value of ratio of power of carrier signal on power of noise signal is formulated. Solution of formulated optimization task make it possible to formulate recommendations for increasing the noise immunity of GPS/INS based navigation systems for some limited class of utilized jamming noise. The methodic to increase noise immunity of GPS targeting systems in condition of jamming noise is suggested.

A Robust EMD-Based RVFL Network Fusion Algorithm for Low-Cost GPS/INS Integrated System

Abstract: Global positioning system (GPS) and inertial navigation system (INS) are commonly combined to overcome disadvantages of each and constitute an integrated system that realizes long-term precision. However, the performance of the integrated system deteriorates on which GPS is unavailable. Especially when low-cost inertial sensors based on the microelectromechanical system (MEMS) are used, performance of the integrated system degrades severely over time. In this study, in order to minimize the adverse impact of high-level stochastic noise from low-cost MEMS sensors, denoising technology based on empirical mode decomposition (EMD) is employed to improve signal quality before navigation solution by which significant improvement of removing noise is achieved. Moreover, a random vector functional link (RVFL) network-based fusion algorithm is presented to estimate and compensate position error during GPS outage such that error accumulation is suppressed quickly when INS is working standalone. Performance of the proposed approach is evaluated by experimental results. It is indicated from comparison that the proposed algorithm takes advantages such as better accuracy and lower complexity and is more robust than the commonly reported methods and is more appropriate for real-time and low-cost application.