GPS Outages Research Articles

Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage

When INS/GNSS (inertial navigation system/global navigation satellite system) integrated system is applied, it will be affected by the insufficient number of visible satellites, and even the satellite signal will be lost completely. At this time, the positioning error of INS accumulates with time, and the navigation accuracy decreases rapidly. Therefore, in order to improve the performance of INS/GNSS integration during the satellite signals interruption, a novel learning algorithm for neural network has been presented and used for intelligence integrated system in this article. First of all, determine the input and output of neural network for intelligent integrated system and a nonlinear model for weighs updating during neural network learning has been established. Then, the neural network learning based on strong tracking and square root UKF (unscented Kalman filter) is proposed for iterations of the nonlinear model. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical UKF to avoid the filter divergence caused by the negative definite state covariance matrix. Meanwhile, the strong tracking coefficient is introduced to adjust the filter gain in real-time and improve the tracking capability to mutation state. Finally, an improved calculation method of strong tracking coefficient is presented to reduce the computational complexity in this algorithm. The results of the simulation test and the field-positioning data show that the proposed learning algorithm could improve the calculation stability and robustness of neural network. Therefore, the error accumulation of INS/GNSS integration is effectively compensated, and then the positioning accuracy of INS/GNSS intelligence integrated system has been improved.

A Novel Multidimensional Hybrid Position Compensation Method for INS/GPS Integrated Navigation Systems During GPS Outages

A Novel Multidimensional Hybrid Position Compensation Method for INS/GPS Integrated Navigation Systems During GPS Outages

Promoting navigation system efficiency during GPS outage via cascaded neural networks: A novel AI based approach

Promoting navigation system efficiency during GPS outage via cascaded neural networks: A novel AI based approach

AI-ACCIDENT DETECTION SYSTEM FOR FAST RESCUE ON FREEWAYS USING ON-BOARD CAR MOTION SENSORS FUSION

Continuous tracking and detecting position systems are significant services that can using in many applications such as detecting accident cars on Freeways. This paper introduces Artificial intelligent (AI) method to quick discover the accident and determine the position of car on Freeways that will reduce the rate of fatalities. Most tracking and detecting position systems are based on Global Navigation Satellite System (GNSS). But the GNSS signal in certain areas such as buildings and tunnel is unavailable which make a big problem for these systems. To solve this problem, most traditional methods are based on integrated GNSS with other navigation sensors such as accelerometers, gyroscopes, odometers and so on. But these integrated methods still high cost and need to long time processing. Today, most modern Vehicles are equipping with several On-board Car Motion Sensors (OVMS) such as Acceleration Sensors (AS), Wheel Velocity Sensor (WVS), Gravity Sensor (GS), Yaw Rate Sensor (YRS) and additional to GNSS that can improve the safety and also use to determine and detect the position of accident vehicle. This paper develops an approach AI-accident detection system based on integrating GNSS with On-board Vehicle Motion Sensors (OVMS) using Extended Kalman Filter (EKF) algorithm. During GPS outages, the three Acceleration Sensors (3-ASs) are processing to determine velocities and then position of vehicle on three x, y and z axes. While the YRS and GS sensors are using to determine velocities angles on three x, y and z axes, respectively. In same time the three WVS sensors are using to correct velocities errors of three acceleration sensors (3-ASs) until GNSS signal is return again. The proposed AI-accident detection system is tested in different scenarios during GPS outage signal. The results of proposed method can actually detect accident and determine the position of vehicle with high efficiency.

An Improved GPS/INS Integration Based on EKF and AI During GPS Outages

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at [Formula: see text] and [Formula: see text]. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method.

Trajectory Planning for Multiple UAVs and Hierarchical Collision Avoidance Based on Nonlinear Kalman Filters

Fully autonomous trajectory planning for multiple unmanned aerial vehicles (UAVs) is significant for building the next generation of the logistics industry without human control. This paper presents a method to enable multiple UAVs to fly in the same trajectory without collision. It benefits several applications, such as smart cities and transfer goods, during the COVID-19 pandemic. Different types of nonlinear state estimation are deployed to test the position estimation of drones by treating the information from AirSim as offline dynamic data. The obtained global positioning system sensor data and magnetometer sensor data are determined as the measurement model. The experiment in the simulation is separated into (1) the localization state, (2) the rendezvous state, in which the proposed rendezvous strategy is presented by using the relation between velocity and displacement through the setting area, and (3) the full mission state, which combines both the localization and rendezvous states. The localization state results show the best RMSE in the case of full GPS available at 0.21477 m and 0.25842 m in the case of a GPS outage during a period of time by implementing the ensemble Kalman filter. Similarly, the ensemble Kalman filter performs well with an RMSE of 0.5112414 m in the rendezvous state and demonstrates exceptional performance in the full mission state. Moreover, the experiment is implemented in a real-world situation with some basic drone kits as proof that the proposed rendezvous strategy can truly operate.

Efficient Route Planning Using Temporal Reliance of Link Quality for Highway IoV Traffic Environment

Intermittently connected vehicular networks, terrain of the highway, and high mobility of the vehicles are the main critical constraints of highway IoV (Internet of Vehicles) traffic environment. These cause GPS outage problem and the existence of short-lived wireless mobile links that reduce the performance of designed routing approaches. Nevertheless, geographic routing has attracted a lot of attention from researchers as a potential means of accurate and efficient information delivery. Various distance-based routing protocols have been proposed in the literature, with an emphasis on restricting the forwarding area to the next forwarding vehicle. Many of these protocols have issues with significant one-hop link disconnection, long end-to-end delays, and low throughput even at normal vehicle speeds in high-vehicular-density environments due to frequently interrupted wireless links. In this paper, an efficient geocast routing (EGR) approach for highway IoV–traffic environment considering the shadowing fading condition is proposed. In EGR, a geometrical localization for GPS outage problem and a temporal link quality estimation model considering underlying vehicular movement have been proposed. Geocast routing to select a next forwarding vehicle from forward region by utilizing temporal link quality is proposed for four different scenarios. To evaluate the effectiveness and scalability of EGR, a comparative performance evaluation based on simulations has been performed. It is clear from the analysis of the results that EGR performs better than state-of-the-art approaches in highway traffic environment in terms of handling the problem of wireless communication link breakage and throughput, as well as ensuring the faster delivery of the messages.

A hybrid optimization algorithm for GWO Fine-tuning GRU-Aided AKF during GPS outage

In order to improve the positioning accuracy of the combined navigation system when GPS outages, a hybrid model using Grey Wolf Optimizer to fine-tune Gated Recurrent Unit (GWO-GRU) aided adaptive Kalman filter (AKF) is proposed. Firstly, it is verified that the introduction of GWO plays a crucial role in the prediction performance of GRU, and effectively solves the problem of difficult selection of hyperparameters in GRU networks. Secondly, the trajectory curves with different complexity are predicted by three simulated GPS outage experiments, which verifies that the proposed model has strong error suppression ability and solves the problem that complex trajectory is difficult to predict accurately. In general, this paper demonstrates the prediction ability of GWO-GRU + AKF model when GPS is outang for a long time and complex trajectory.

An adaptive method of attitude and position estimation during GPS outages

Aiming to improve the accuracy of navigation systems during GPS outages, this paper presents an adaptive-gain complementary filter for attitude estimation. With the introduction of the acceleration vector as the observation, system dynamic information is considered to handle the high-frequency interference caused by external acceleration. Meanwhile, this paper presents a position prediction algorithm based on fuzzy neural networks with velocity and GPS position increment as the desired outputs. A hybrid method of the Least Mean Square (LMS) and conjugate gradient method is utilized to tune the parameters. With a 160 s non-overlapping sliding window, a flight test has been done using the proposed methods during 240 s GPS outages. The results indicate that the attitude estimation algorithm (RMSE of 0.89° and 0.45° for roll and pitch angles) performed better than the Mahony algorithm, and position prediction errors are 0.93 m and 1.12 m for latitude and longitude respectively.

Continuous Positioning with Recurrent Auto-Regressive Neural Network for Unmanned Surface Vehicles in GPS Outages

Continuous Positioning with Recurrent Auto-Regressive Neural Network for Unmanned Surface Vehicles in GPS Outages

Detection And Tracking of Theft Vehicle

In this paper presents ‘Detection and tracking of theft vehicle’. The aim is to design a system that can able to capture the image of an unauthorized person and transmit it to a remote location. This project can be used for the recovery of the automobiles. And the owner can be able to stop the vehicle by sending one message to system. And we can be able to identify the face of the thief also. The microprocessor acts as the controlling head of the system. When the system goes in Theft mode the authorized user gets the alert message and user will Able to stop the vehicle engine immediately via sending SMS to system with ‘STOP’ Keyword. The system includes a GPS modem that vehicle location in the form of latitude and longitude. This location can be accessed via SMS that is being sent to the user. With the help latitude and longitude information SMS user will able to locate vehicle with Google map. This system proves very beneficial for transport and travel companies as they can now keep track of their vehicles. It will also scan the face through the camera and send the message to the owner. By using GPS and GSM technologies enable the vehicle owners to track and monitor the vehicle at anytime from anywhere. During GPS outage environment, Inertial Navigation Sensor is used that contains 3- axis magnetometer and accelerometer which inform the vehicle positions to the owner.

SINS/GNSS-Integrated Navigation of Surface Vessels Based on Various Nonlinear Kalman Filters and Large Ship Dynamics

Generally, studies investigating the strapdown inertial navigation system/global navigation satellite system (SINS/GNSS)-integrated navigation of surface vessels focus on adaptive strong nonlinear algorithms to handle the system and measurement noise induced by the complex environment and motion state. However, these studies rarely consider the suitability of strong nonlinear adaptive optimization in all conditions or the existence of any restriction. The application conditions for these standard nonlinear filters with respect to the surface vessel SINS/GNSS require investigation. In this study, the estimation accuracy of the motion state obtained using the extended Kalman filter, unscented Kalman filter, and cubature Kalman filter in case of different vessel motions is compared based on the simulated ship sensor data obtained using a dynamic large surface vessel model under various marine conditions. Compared with previous studies, the data generator in this study simulates the actual ship movements under various conditions, based on which considerably detailed and practical analysis and conclusion can be realized in case of the SINS/GNSS-integrated navigation obtained using various standard nonlinear filters. In particular, the situations often encountered by large surface vessels during integrated navigation attributed to environmental interference or instrument failure, including system noise amplification, initial alignment error, and GPS outage, are investigated.

An EL-SHAKF-Based Integration Scheme for Gyro Thermal-Magnetic Coupling Heading- Effect Drift Compensation in INS

Inertial navigation system (INS) is widely equipped in various vehicles for overcoming GPS-challenging environments. The thermal-magnetic coupling heading-effect drift (thermal-magnetic drift) of INS is a multi-physics coupling error related to carrier maneuver, which cannot be compensated well by traditional methods. In this paper, the source of gyro thermal-magnetic drift are analyzed and an Ensemble Learning (EL) and Sage-Husa adaptive Kalman filter (SHAKF) based integration scheme for the thermal-magnetic drift compensation in high-grade INS is established. In the integrated system, the geomagnetic field strength calculated by International Geomagnetic Reference Field (IGRF) model and temperature information measured by sensors are selected as input of the thermal-magnetic drift prediction model. The gyro biases estimated by exponential fading SHAKF are taken as target in training phase. The EL algorithms, LSBoost and Bagging, are used for training when GPS data is available. The gyro thermal-magnetic drift is predicted and compensated by the trained model during GPS outages. Land and sea tests show its effectiveness on compensate the gyro thermal-magnetic drift of the proposed method.

Privacy‐preserving cooperative localization in vehicular edge computing infrastructure

SummaryAdvancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in‐efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un‐encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy‐preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real‐time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state‐of‐the‐art methods.

TrajData: On Vehicle Trajectory Collection With Commodity Plug-and-Play OBU Devices

For years, vehicle trajectory data have increasingly been important for a wide range of applications, from driver behavior investigation/classification, travel time/distance estimation, and routing in vehicular networks, to vehicle energy/emission evaluation. This article presents TrajData, the first systematic solution to reliable vehicle trajectory data collection, with only reliance on commercial-off-the-shelf (COTS) onboard unit (OBU) devices that utilize lightweight GPS modules and low-cost onboard diagnostics (OBD) readers. In the practical use of trajectory collection, GPS outages inevitably occur in urban environments thereby leading to large trajectory errors as well as missing vehicle location data. To resolve this, we propose a novel data-fusion-enabled deep learning approach with the purpose of achieving reliable vehicle trajectory collection in various urban road conditions. Specifically, we leverage motion information retrieved from OBD readers in TrajData to help reconstruct the trajectory data during GPS outages. By investigating the changes of direction angle from the OBD readings, we can identify different types of road sections. Furthermore, we integrate the neural arithmetic logic units (NALUs) into our trajectory reconstruction model to tame the challenges when GPS outages take place in various road sections. Experimental results from realistic data have demonstrated the effectiveness and reliability of the proposed method. In the road test, TrajData achieves an average position error below 15-m around a 60-s GPS outage, even in complex road sections, i.e., continuous turns and driving with accelerations/decelerations resulting in frequent changes of direction and speed.

Fuzzy Adaptive Attitude Estimation for a Fixed-Wing UAV With a Virtual SSA Sensor During a GPS Outage

This paper proposes a novel robust attitude estimation algorithm for a small unmanned aerial vehicle (UAV) in the absence of GPS measurements. A synthetic sideslip angle (SSA) measurement formulated for use under the zeroangle assumption is newly proposed for a UAV without angle-of-attack (AOA)/SSA sensors to enhance the state estimation performance during a GPS outage. In addition, the nongravitational acceleration is estimated using the proposed Kalman filter and is then subtracted from the raw acceleration to yield a reliable gravity estimate. Then, a fuzzy-logic-aided adaptive measurement covariance matching algorithm is devised to adaptively reduce the weight given to disturbed acceleration and magnetic field measurements in the attitude estimation, yielding the fuzzy adaptive error-state Kalman filter (FAESKF) algorithm. Experimental flight results demonstrate that the proposed FAESKF algorithm achieves a remarkable improvement in attitude estimation compared to the conventional algorithm.

Deep Gaussian Process Regression for Performance Improvement of POS During GPS Outages

Position and orientation system (POS) is a high-precision inertial navigation systems/Global navigation satellite system (INS/GNSS) integrated system that can continuously provide time-spatial reference for airborne remote sensing, mobile mapping and vehicle localization using Kalman Filter (KF) during the availability of GPS measurements. However, the POS suffers from the GPS outages in some especial environment, whose accuracy of motion parameters degrades with the time accumulation. In order to suppress the performance degradation caused by GPS outages, this work proposes a hybrid predictor based on data-driven Deep Gaussian Process Regression (DGPR), which uses multi-layer Gaussian Process Regression to deal with highly complex data relationships and predictive uncertainty. Once GPS outages happen, the proposed approach starts to predict the observation measurement, and then feeds it to KF as a virtual update to estimate all the INS errors. The proposed approach is validated by the real flight test, and the experimental results show that significant performance improvement has been achieved during various GPS outages.

Novel Hybrid Algorithm of Improved CKF and GRU for GPS/INS

In order to ensure that Inertial Navigation System/Global Positioning System integrated navigation system (INS/GPS) can still provide high precision positioning results when GPS outages, a novel hybrid algorithm based on Gated Recurrent Unit (GRU) and interacting multiple model adaptive robust cubature Kalman filter (IMM-ARCKF) is proposed. Firstly, the IMM-ARCKF algorithm is proposed to solve the uncertainty of system model and measurement noise statics in the application of INS/GPS on the road. Then, GRU neural network is introduced into INS/GPS system which includes two modes of training and prediction. When GPS signal can be received, the GRU neural network works in the training mode. When GPS outages, the GRU neural network predicts the GPS position increment. Finally, the effectiveness of the algorithm is evaluated by the experiment and analysis. From the data of the experiment, the proposed algorithm can improve the positioning accuracy during GPS outages.

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

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.

Prediction of SINS/GPS Navigation Information by ELM Algorithm during GPS outages

SINS/GPS integrated navigation has received great attention recently due to its impressive navigation accuracy on missile-borne platforms. However, it remains an ongoing challenge to address the problem of GPS signal loss. Furthermore, the limited space and power consumption of a projectile have made it challenging to carry out time-consuming iterative calculations. In this paper, ELM is proposed for the first time to predict the navigation information on the missile-borne platform for its simpler neural network structure and no iterative calculation. Comparing it with the algorithms of BP and GABP, the simulation results reveal that the ELM algorithm is more time-saving with higher prediction accuracy than the others. More importantly, the number of parameters needed to be set is much less than those of the other two algorithms, which is beneficial for its practical application.