Global Positioning System Signals Research Articles

Impact of sand and dust storms on tropospheric parameter estimation by GPS

The transport of dust from the Middle East and African deserts affects European and Asian countries at certain times of the year, especially in spring. Turkey is one of these countries, and many dust storm events have occurred in the first half of 2022, which have affected especially the southeastern part of Anatolia. Apart from threatening human health, dust and sand particles, which are described as particulate matter, may possibly affect Global Positioning System (GPS) signals. The purpose of this research is to look into the effects of particulate matter less than 10μm (PM10) on GPS-estimated precipitable water vapor (PWV). Hourly PM10 and PWV data between April 1, 2022, and June 10, 2022, were utilized. Four different extreme dust concentration events and a benchmark period were investigated separately. Hourly data results showed that correlation coefficients vary according to events, wind directions, and the distance between GPS stations and air quality monitoring stations. Also, other meteorological parameters that affect PWV, such as temperature, relative humidity, and pressure, were investigated and found to have no anomalies that could affect PWV. Hourly and daily correlation coefficients in the benchmark period were significantly lower compared to dusty days, which indicates that there is no real correlation between PM10 and PWV concentrations in clear air conditions. Only with the increase of PM10 to extreme levels does the relationship show itself. Therefore, this study suggests that for all GPS applications, such as positioning or PWV estimation, PM10 concentrations should be considered.

Coarse-to-fine visual autonomous unmanned aerial vehicle landing on a moving platform

Autonomous unmanned aerial vehicle (UAV) landing is a challenging task, especially on a moving platform in an unstructured environment. Under such a scenario, successful UAV landing is mainly affected by poor UAV localization performance. To solve this problem, we propose a coarse-to-fine visual autonomous UAV landing system based on an enhanced visual positioning approach. The landing platform is marked with a specially designed QR code marker, which is developed to improve the landing accuracy when the UAV approaches the landing site. Besides, we employ the you only look once framework to enhance the visual positioning accuracy, thereby promoting the landing platform detection when the UAV is flying far away. The framework recognizes the QR code and decodes the position of a UAV by the corner points of the QR code. Further, we use the Kalman filter to fuse the position data decoded from the QR code with those from the inertia measurement unit sensor. Then, the position data are used for UAV landing with a developed hierarchical landing strategy. To verify the effectiveness of the proposed system, we performed experiments in different environments under various light conditions. The experimental results demonstrate that the proposed system can achieve UAV landing with high accuracy, strong adaptability, and robustness. In addition, it can achieve accurate landing in different operating environments without external real-time kinematic global positioning system (RTK-GPS) signals, and the average landing error is 11.5 cm, which is similar to the landing error when using RTK-GPS signals as the ground truth.

Movements of Semi-captive Elephants during Skidding Season in Myanmar

Semi-captive Asian elephants (Elephas maximus) engaged in forestry activities in Myanmar account for 20% of all captive and semi-captive Asian elephants in the world, and are important for both forestry and the conservation of Asian elephant populations. Understanding moving behavior of the semi-captive elephants is required to sustain them. Our specific goals are 1) to determine the moving range during free time, and 2) to determine the hourly moving distance during skidding and when off duty. Three elephants were fitted with handheld global navigation satellite systems with the signals of global positioning system to collect data on their movements. The elephants were generally located between 0.534 and 0.875 km from the camp with temporary housing of the elephant handler when not skidding (i.e., free time) and between 1.365 and 1.372 km when skidding (i.e., work time). The hourly moving distance during free time (0.622–0.655 km) and work time (1.522 and 1.629 km) did not differ greatly from the hourly moving distance of wild Asian elephants (0.010–1.500 km). The elephants remained within 0.875 km of the camp of the elephant handler, and some variation in movements among individuals was observed during free time. Thus, the conservation of forest in areas near the camp is important for the well-being of these elephants.

GPS Spoofing Attacks in FANETs: A Systematic Literature Review

Flying Ad-Hoc Networks (FANETs) are groups of UAVs connected in an Ad-Hoc manner to accomplish a common mission. The widespread acceptance of UAVs due to their low cost and high efficiency has attracted malicious security attacks against them. These attacks cannot be easily prevented due to UAVs’ limited computational power, short battery life, and inability to execute complex algorithms. FANETs rely on the Global Positioning System (GPS) for localization. GPS Spoofing, an easy-to-launch attack, is one of the main challenges in FANETs. In which, the legitimate and not-encrypted civilian GPS signals are overridden by counterfeit signals to deceive the UAVs to collide or to be hijacked. Researchers proposed various countermeasures to address GPS Spoofing attacks in FANETs. To further assist future research, this paper provides a systematic literature review on GPS Spoofing attacks in FANETs and their defense mechanisms. After formulating eight research questions and applying well-defined inclusion/exclusion criteria and quality assessment tools, 70 research articles were extracted. The existing defense mechanisms were classified based on their objectives (i.e., detection, mitigation, and/or prevention) and according to their basis (i.e., relying on readings from various drones’ devices/sensors, processing the signals received by various sensors, employing machine learning algorithms, relying on game theory, or leveraging cryptographic techniques to authenticate and protect the confidentiality of GPS signals). The defense mechanisms were also analyzed to identify the attacker models, impacts of the attack, and detection performance. This study found that most of the proposed methods are detection approaches, rather than mitigation or prevention. Also, almost all papers used simulation experiments rather than a proof-of-concept implementation, which does not demonstrate a convincing performance under realistic mobility and propagation models. Moreover, most solutions addressed GPS Spoofing for a single UAV. Only eight articles addressed multiple UAV scenarios and none of them provided a proof-of-concept evaluation.

A Non-Iterative Fault Location Algorithm for Transposed/Untransposed Transmission Line Immune to Unsynchronized Measurements

This paper presents an impedance-based non-iterative fault location algorithm for a two-terminal line considering unsynchronized measurements to account for the loss of Global Positioning System (GPS) signal. Based on the availability of pre-fault signals two algorithms are proposed, one using pre-fault data and the other without using pre-fault data. The algorithms are formulated by using fundamental frequency phasor based decoupled modal components of the measured signals at both ends of the line while considering distributed line model. Application of decoupled modal components with distributed model enhances the accuracy of the presented algorithms in both transposed and untransposed configuration of the line. The proposed algorithms have been tested and analyzed with different fault conditions simulated in EMTP-RV platform. Comparative analysis with existing methods is also presented to establish prominent features of the proposed methods. The algorithms have also been tested with practical data of Power Grid Corporation of India Limited (PGCIL) and the test result validates the accuracy of the developed fault location algorithms.

Updates on CYGNSS Ocean Surface Wind Validation in the Tropics

Abstract Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s−1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.

Passive IR-UWB Localization System for UAV-Based Electric Facility Inspection During GPS Outage

A practical localization system is proposed for a substation inspection unmanned aerial vehicle (UAV) performing its mission under global positioning system (GPS) signal blockage. The sensor hardware consists of a single transmitter and a cruciform receiver array, which produces the range difference (RD) information used for UAV positioning. The use of impulse-radio ultra wide-band (IR-UWB) devices secures the robustness against RF interference often caused by an exogenous electromagnetic field. With this hardware configuration, the UAV localization can be formulated as the state estimation problem for an uncertain linear state space model and be solved by using the computationally efficient robust weighted least squares (RWLS) estimator. Since the standard RWLS estimator could be sensitive to the imperfectness of prior knowledge on the noise statistic, a geometric constraint expressed in terms of a UAV position is exploited to secure the reliable localization performance in practice. Through experimental results, it is verified that the proposed solution provides reliable UAV positioning performance despite using the imperfect measurement noise statistics for the localization system.

Autonomous Lunar L1 Halo Orbit Navigation Using Optical Measurements to a Lunar Landmark

<h3>Abstract</h3> Autonomous cislunar spacecraft navigation is critical to mission success as communication to ground stations and access to global positioning system (GPS) signals could be lost. However, if the satellite has a camera of sufficient quality, geometric line-of-sight (unit vector) measurements can be made to known lunar landmarks (e.g., Tycho Crater) to provide observations that enable autonomous estimation of the position and velocity of the spacecraft. In this study, an improved batch gaussian initial orbit determination (IOD) differential corrector (DC) algorithm, based on the approximated values of the two-body f and g series, is applied to initialize a (non-conic based) circular restricted three body problem (CR3BP) extended Kalman Filter (EKF) navigator. This navigator collects geometric line-of-sight unit vector (angle only) measurements to a known location on the Moon to sequentially estimate the position and velocity of an observer spacecraft flying on an approximate southern L1 Halo orbit. In this study, it was found that the best approach is to initialize the CR3BP EKF (navigator) using the solution from the batch DC filter with at least 10 measurements taken against the perceived centroid of Tycho Crater. Thereafter, it is best to continue the navigator with subsequent measurements taken against the same center coordinates of the Tycho Crater, where these coordinates are now expressed in the CR3BP rotating frame. For successful conic-based batch filter initialization and long-term CR3BP EKF convergence, it was found that the cadence for all optical measurements should be taken at 10 minutes for a simulated measurement noise of 0.1° one sigma uncertainty about the line-of-sight measurement unit vector.

Statistical validation of ionospheric electron density profiles retrievals from GOES geosynchronous satellites

In this paper, we discuss a novel retrieval of ionospheric electron density profiles using the Radio Occultation (RO) technique applied to measurements captured by the Global Positioning System (GPS) receivers onboard two Geostationary Operational Environmental Satellites (GOES). The GOES satellites operate at ~35,800 km altitude and are primarily weather satellites that operationally contribute continuous remote-sensing data for real-time weather forecasting, as well as near-Earth environment monitoring and Sun observations. The GPS receivers onboard GOES-16 and GOES-17 satellites can track GPS signals propagated through the Earth’s atmosphere, and although the receivers are primarily designed for navigation and station-keeping maneuvers, these GPS measurements that traverse the Earth’s atmosphere can be used to retrieve the ionospheric electron density profiles. This process poses a range of technical challenges. GOES RO links are different from the traditional low Earth orbit (LEO) RO geometry since the receiver is located in an orbit that is higher in altitude than the GPS constellation of transmitters. Additionally, the GPS receivers onboard GOES satellites provide only single-frequency GPS L1 observations and have clocks much less stable than those typically used for RO measurements. The geographical distribution of the retrieved GEO-based RO profiles was found to be uniquely constrained and repeatable based on the relative geostationary fixed positions in the Earth-Centered Earth Fixed reference frame with respect to the GPS constellation orbiting at lower altitude, and significantly different from the coverage patterns of LEO-based RO missions. We demonstrate the successful application of the proposed RO profiling technique with a statistically significant set of GPS observations from GOES-16 and GOES-17 satellites over several years of data collection. This enabled us to retrieve more than 10,000 ionospheric electron density profiles with a maximum altitude of up to 1000–2000 km, much higher than any existing LEO-based RO mission. We demonstrate good performance of GEO-based RO measurements for properly specifying the vertical distribution of ionospheric plasma density by comparing the profiles dataset from the GOES RO experiment with independent reference observations – ground-based ionosondes and LEO-based RO missions, as well as model simulation results provided by the empirical International Reference Ionosphere model. Over multiple years of observations, statistical analysis of discrepancies between the ionospheric F2 layer peak parameters (peak density and height) derived from geosynchronous GOES observations and reference measurements was conducted. This analysis reveals a very good agreement between GOES RO electron density profiles and independent types of measurements in both the F2 peak and the profile shape.

A Vehicle-Carried INS Positioning Accuracy Improvement Method by Using Lateral Constraint in GPS-Denied Environment

When the vehicle passes through the tunnel or bridge, Global Positioning System (GPS) will fail due to signal occlusion or interference. In order to improve the positioning accuracy of vehicle-carried inertial navigation system (INS) in GPS rejection environment, the lateral constraint method based on vehicle-carried INS is studied in this article. According to the characteristics of lateral constraint, in the area with good GPS signal, installation errors between the INS body coordinate frame and the vehicle coordinate frame are estimated at the same time of integrated navigation. In the GPS-denied area, the estimated installation errors are compensated to calculate the reference velocity at first. After compensation, the difference between INS velocity and reference velocity is taken as the measurement. Finally, the Kalman filter model is established to correct the inertial navigation position. In addition, the simulation and experimental verification of the proposed lateral constraint method are carried out. The results show that the INS positioning accuracy can be improved above 50% by using the lateral constraint method in GPS-denied environment, which verifies the effectiveness of the proposed lateral constraint method.

Machine Learning Modeling of GPS Features with Applications to UAV Location Spoofing Detection and Classification

In this paper, machine learning (ML) modeling is proposed for the detection and classification of global positioning system (GPS) spoofing in unmanned aerial vehicles (UAVs). Three testing scenarios are implemented in an outdoor yet controlled setup to investigate static and dynamic attacks. In these scenarios, authentic sets of GPS signal features are collected, followed by other sets obtained while the UAV is under spoofing attacks launched with a software-defined radio (SDR) transceiver module. All sets are standardized, analyzed for correlation, and reduced according to feature importance prior to their exploitation in training, validating, and testing different multiclass ML classifiers. The resulting performance evaluation of these classifiers shows a detection rate (DR), misdetection rate (MDR), and false alarm rate (FAR) better than 92%, 13%, and 4%, respectively, together with a sub-millisecond detection time. Hence, the proposed modeling facilitates accurate real-time GPS spoofing detection and classification for UAV applications.

Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs

Unmanned aerial vehicles (UAVs) are an emerging technology in the 5G-and-beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the opensource simulator can use commercial software-defined radio tools to generate fake global positioning system (GPS) signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations (BSs) at the same time. In this article, we propose a novel deep-ensemble-learning-based, mobile-network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between BSs and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multilayer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.

Landmark-Based Scale Estimation and Correction of Visual Inertial Odometry for VTOL UAVs in a GPS-Denied Environment.

With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and are applied in many areas. However, there are some environments where the Global Positioning System (GPS) is unavailable or has the problem of GPS signal outages, such as indoor and bridge inspections. Visual inertial odometry (VIO) is a popular research solution for non-GPS navigation. However, VIO has problems of scale errors and long-term drift. This study proposes a method to correct the position errors of VIO without the help of GPS information for vertical takeoff and landing (VTOL) UAVs. In the initial process, artificial landmarks are utilized to improve the positioning results of VIO by the known landmark information. The position of the UAV is estimated by VIO. Then, the accurate position is estimated by the extended Kalman filter (EKF) with the known landmark, which is used to obtain the scale correction using the least squares method. The Inertial Measurement Unit (IMU) data are used for integration in the time-update process. The EKF can be updated with two measurements. One is the visual odometry (VO) estimated directly by a landmark. The other is the VIO with scale correction. When the landmark is detected during takeoff phase, or the UAV is returning to the takeoff location during landing phase, the trajectory estimated by the landmark is used to update the scale correction. At the beginning of the experiments, preliminary verification was conducted on the ground. A self-developed UAV equipped with a visual-inertial sensor to collect data and a high-precision real time kinematic (RTK) to verify trajectory are applied to flight tests. The experimental results show that the method proposed in this research effectively solves the problems of scale and the long-term drift of VIO.

GPS Spoofing Detection Method for Small UAVs Using 1D Convolution Neural Network

The navigation of small unmanned aerial vehicles (UAVs), such as quadcopters, significantly relies on the global positioning system (GPS); however, UAVs are vulnerable to GPS spoofing attacks. GPS spoofing is an attempt to manipulate a GPS receiver by broadcasting manipulated signals. A commercial GPS simulator can cause a GPS-guided drone to deviate from its intended course by transmitting counterfeit GPS signals. Therefore, an anti-spoofing technique is essential to ensure the operational safety of UAVs. Various methods have been introduced to detect GPS spoofing; however, most methods require additional hardware. This may not be appropriate for small UAVs with limited capacity. This study proposes a deep learning-based anti-spoofing method equipped with 1D convolutional neural network. The proposed method is lightweight and power-efficient, enabling real-time detection on mobile platforms. Furthermore, the performance of our approach can be enhanced by increasing training data and adjusting the network architecture. We evaluated our algorithm on the embedded board of a drone in terms of power consumption and inference time. Compared to the support vector machine, the proposed method showed better performance in terms of precision, recall, and F-1 score. Flight test demonstrated our algorithm could successfully detect GPS spoofing attacks.

Variational Bayesian Based Localization for Intelligent Vehicle Using Lidar and GPS Data Fusion: Algorithm and Experiments

Accurate localization is crucial for safe operation of intelligent vehicle (IV). In practice, global positioning system (GPS) signals sometimes may be contaminated or lost, resulting in inaccurate positions of IV. In this article, the distance of IV’s position between previous frame and current frame is derived from Lidar point cloud registration. A novel slide window variational Bayesian (VB) based localization method is proposed for IV with multiple dynamics by fusing GPS and Lidar. In the proposed method, the state of IV, the motion model identity of IV, the measurement loss identity, and the loss probability of measurement are jointly estimated by the VB technique. The effectiveness of the proposed localization method is validated by simulations and field experiments.

Possible seismo-ionospheric anomalies associated with the 2016 Mw 6.5 Indonesia earthquake from GPS TEC and Swarm satellites

Ionospheric anomalies through satellites can provide useful information about forthcoming earthquakes (EQs) over the epicentral regions. In this paper, we investigated seismo-ionospheric anomalies associated with the Mw 6.5 Sumatra earthquake that occurred in Indonesia on 06 December 2016 at 22:03 UT. We analyzed the total electron content (TEC) from Global Positioning System (GPS) signals received at the nearby stations around the epicenter. Furthermore, we also studied the TEC in local daytime and nighttime from Swarm satellites to confirm the EQ-induced ionospheric perturbations. The TEC showed significant perturbation within 5–10 days before the main shock in the form of positive anomalies beyond the upper bound. Similarly, Swarm satellites also validated the anomalies observed in the GPS TEC from nearby operating stations within 5–10 days over the epicentral region. The geomagnetic indices were quiet for the observed TEC anomalies within 5–10 days before the main shock, having Dst ≤ −40 nt and Kp ≤ 3. This study reveals the legitimate anomalies mainly associated with the EQ and suggests using the TEC from GPS and other satellites to look for possible future precursors with a more equipped satellite cluster.

A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages

To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extreme learning machine with a dynamic forgetting factor (DOS-ELM) algorithm is used to train the mapping model between the SINS’ acceleration, specific force, speed/position increments outputs, and the GPS’ speed/position increments. When a GPS signal is unavailable, GPS speed/velocity measurements are replaced with prediction output of the well-trained DOS-ELM module’s prediction output, and information fusion with the SINS reduces the degree of system error divergence. A land vehicle field experiment’s actual sensor data were collected online, and the DOS-ELM-aided methodology for the SINS/GPS integrated navigation systems was applied. The simulation results indicate that the proposed methodology can reduce the degree of system error divergence and then obtain accurate and reliable navigation information during GPS outages.

Research on Location Estimation for Coal Tunnel Vehicle Based on Ultra-Wide Band Equipment

Because the road surfaces of the underground roadways in coal mines are slippery, uneven, with dust and water mist, and the noise and light illumination effects are significant, global positioning system (GPS) signals cannot be received, which seriously affects the ability of the odometer, optical camera and ultrasonic camera to collect data. Therefore, the underground positioning of coal mines is a difficult issue that restricts the intellectualization of underground transportation, especially for automatic robots and automatic driving vehicles. Ultra-wide band (UWB) positioning technology has low power consumption, high performance and good positioning effects in non-visual environments. It is widely used in coal mine underground equipment positioning and information transmission. In view of the above problems, this research uses the WLR-5A mining unmanned wheeled chassis experimental platform; uses two UWB receivers to infer the position and yaw information of the vehicle in the underground roadway through the method of differential mapping; and tests the vehicle on the double shift line and quarter turn line in the GAZEBO simulation environment and on the ground simulation roadway to simulate the vehicle meeting conditions and quarter turning conditions in the underground roadway. The positioning ability of the method in these two cases is tested. The simulation and test results show that the vehicle position and attitude information deduced by two UWB receivers through the differential mapping method can basically meet the requirements of underground environments when the vehicle is traveling at low speeds.

Designing and developing a mobile application for indoor real-time positioning and navigation in healthcare facilities.

Navigation portable applications have largely grown during the last years. However, the majority of them works just for outdoor positioning and routing, due to their architecture based upon Global Positioning System signals. Real-Time Positioning System intended to provide position estimation inside buildings is known as Indoor Positioning System (IPS). This paper presents an IPS implemented as a mobile application that can guide patients and visitors throughout a healthcare premise. The proposed system exploits the geolocation capabilities offered by existing navigation frameworks for determining and displaying the user's position. A hybrid mobile application architecture has been adopted because it allows to deploy the code to multiple platforms, simplifying maintenance and upgrading. The developed application features two different working modes for on-site and off-site navigation, which offer both the possibility of actual navigation within the hospital, or planning a route from a list of available starting points to the desired target, without being within the navigable area. Tests have been conducted to evaluate the performance and the accuracy of the system. The proposed application aims to overcome the limitations of Global Navigation Satellite System by using magnetic fingerprinting in combination with sensor fusion simultaneously. This prevents to rely on a single technology, reducing possible system failures and increasing the scalability.

INS/GPS Integrated Navigation for Unmanned Ships Based on EEMD Noise Reduction and SSA-ELM

The primary problem faced by the integrated navigation system based on the inertial navigation system (INS) and global positioning system (GPS) is providing reliable navigation and positioning solutions during GPS failure. Thus, this study proposes an innovative integrated navigation algorithm to address the limitation of precise positioning when GPS fails. First, for the limitation of noise interference in INS, noise reduction technology based on ensemble empirical mode decomposition (EEMD) is proposed to improve the quality of the INS signal and enhance the noise reduction effect. Second, an INS/GPS integrated framework based on the sparrow search algorithm (SSA) and extreme learning machine (ELM) is proposed. During normal GPS conditions, SSA-ELM is used to develop a high-precision prediction model to estimate differences between INS and GPS. When the GPS signal is interrupted, the difference predicted by SSA-ELM is used as the measurement input and the INS is corrected. To confirm the effectiveness of this method, a real ship experiment is conducted with other commonly used methods. The experimental results demonstrate that the proposed method can improve positioning accuracy and reliability when GPS is interrupted.