Adaptive Vector Delay Lock Tracking Loops GPS Signal over Multipath Fading Channels

This paper evaluates the potential capacity of Receiver Autonomous Integrity Monitoring (RAIM) algorithms for Global Positioning System (GPS) and GPS Aided GEO Augmented Navigation (GAGAN) to support approach and landing phases of flight with vertical guidance. As the traditional RAIM does not support vertical guidance, Advanced RAIM (ARAIM) with potential to achieve worldwide coverage of vertical guidance, is used. These algorithms have focused on the Horizontal Protection Level (HPL) and Vertical Protection Level (VPL). User position is computed by detecting and excluding faults within the navigation solution and position accuracies are also provided. It is found that using RAIM, Position Dilution of Precision (PDOP) mean was good (1.5674) and Position Error (PE) was estimated to be 9.4042m with Fault Detection and Exclusion (FDE) implementation. While on the other hand, to increase horizontal and vertical accuracies, Advanced RAIM algorithm is implemented using GPS and GAGAN data. In case of GPS, Horizontal Protection Error (HPE) mean is 32.8541m and Vertical Protection Error (VPE) mean is 19.9901m. In case of GAGAN, HPE mean is 2.7190m and VPE mean is 3.3010m, thus achieving 95 percent accuracy requirement.

On the enhanced detectability of GPS anomalous behavior with relative entropy

: Software GPS receiver development has been undertaken. We are particularly interested in improving the GPS signal-to-noise/interference ratio using beam forming techniques. The phase relationship among the antenna array elements requires careful calibration. In this study, we will report a phase calibration technique for a 2 by 2 GPS antenna array using both simulated and real GPS signals. This technique is based on the GPS signal-processing algorithm developed for the software GPS receiver. A four-channel digital data collecting system was used in the experiment. For a simulated GPS signal, the experiment was conducted in an anechoic chamber in which a GPS simulation system was facilitated. For real GPS signals, we conducted the experiment on a rooftop to receive the signal from GPS satellites. The calibration verified the coherent nature of the signals among the elements. The results also allowed the source's direction to be determined.

A smart city’s main purpose is to provide intelligent responses to different problems of the rapid urban population growth. For instance, integrating fleet management solutions into intelligent transportation systems (ITS) can efficiently resolve transportation problems relying on each vehicle information. Usually, the position estimate is ensured by the integration of the Global Positioning System (GPS) and Inertial Navigation Systems (INS). For multisensor data fusion, the Extended Kalman Filter (EKF) is generally applied using the sensor’s measures and the GPS position as a helper. However, the INS are expensive and require more complex computing which induces restrictions on their implementation. Furthermore, the EKF performance depends on the vehicle dynamic variations and may quickly diverge because of environmental changes. In this paper, we present a robust low cost approach using EKF and Support Vector Machines (SVM) to reliably estimate the vehicle position by limiting the EKF drawbacks. The sensors used are a GPS augmented by a low cost wireless sensor network. When GPS signals are available, we train SVM on different dynamics and outage times to learn the position errors so we can correct the future EKF predictions during GPS signal outages. We obtain empirically an improvement of up to 94% over the simple EKF predictions in case of GPS failures.

This paper presents two space detected Global Positioning System (GPS)signals reflected off sea ice and compares the returned power profiles with independent estimates of ice concentration provided by the Advanced Microwave Scanning Radiometer (AMSR-E) and sea ice charts from the National Ice Center. The results of the analysis show significantly different signals received for each of the GPS reflections. For the first collection,comparisons with ice concentration estimates from AMSR-E and the National Ice Centers reveal a very strong GPS signal return off high concentration sea ice. The second GPS data collection occurs over a region of changing sea ice concentration, and the GPS signal level responds at roughly the same point that the AMSR-E data and National Ice Center charts indicate a change in ice concentration. However, the very strong signal of the first GPS collection is not consistent in magnitude with similar ice concentrations during the secondGPS data collection. This demonstration shows the potential and the difficulties of this new technique as a valuable low-cost compliment to existing sea ice monitoring instruments. Additionally, a general method for calculating the location of the specular reflection point on the Earth’s surface and the received Doppler frequencies and code phase delays is presented as part of an on-board open-loop signal tracking technique.

The car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

In this article, the problem of detecting and excluding faulty global navigation satellite systems (GNSSs) measurements at the receiver end is formulated as a parameterized quadratic programming (PQP) problem. Compared to the existing fault detection and exclusion (FDE) methods, which mostly rely on exhaustive search, the PQP method is computationally efficient for finding the outliers even when the number of outliers is moderate or large. In the context of multiconstellation GNSS where the probability of multiple simultaneous faults is increased, the PQP method is ideal for the task of fault exclusion since the computation time does not increase with the number of fault hypotheses. It is noted that this article addresses the computational load due to the exclusion function only. The integrity risk bound and continuity risk bound are of fundamental importance to assess the performance of FDE algorithms for safety-critical applications. With the aim to benefit safety-critical applications, the PQP method is integrated with the integrity risk bound and the continuity risk bound derived for FDE using Chi-squared receiver autonomous integrity monitoring (RAIM). It is emphasized that the integration of the PQP method and the integrity and continuity risk bounds do not make the PQP method a practical integrity monitoring method. This is because the computation of the integrity risk bound is still combinatorial and the resulting integrity risk bound is rather conversative. Instead, the integration allows for the opportunity to refine the PQP method so that it can be considered a practical integrity monitoring method. In particular, improvements to reduce the computational load for the Chi-squared RAIM FDE integrity risk bound calculation can readily be applied to the integrity risk bound calculation for the PQP method. Also, further research on PQP parameter tuning can be pursued to tighten the integrity risk bound.

The global positioning system (GPS) signal that is transmitted from satellite to receiver will be disturbed and degraded in the atmosphere layers due to many factors affecting the signal quality, especially in the ionosphere layer which is an ionised layer in upper atmosphere. Ionospheric scintillation and Total Electron Content (TEC) depletions are the most famous factors that can cause serious effects on GPS signal. This research investigates and analyses the measurement of the ionospheric amplitude scintillation and TEC depletions at quiet and disturbed day. The other parameters involved on this study are the carrier-to-noise (C/No) ratio, and rate of change of TEC (ROT). Data from GISTM receiver at UKM station for minimum solar cycle event were analysed on 5 April 2010 (quiet day) and 7 January 2010 (disturbed day); whereas, data from GISTM for maximum solar cycle event were analysed on 7 October 2015 (quiet day) and 2 June 2015 (disturbed day). The solar cycle event affected on the GPS signal especially when this event increased, so the analysis of ionospheric scintillation during maximum solar cycle event was compared with minimum solar cycle event to investigate the differences between these solar cycle events. The ionospheric scintillation and TEC variations are affected by solar cycle. Results showed that the ionospheric scintillation and TEC variations increased during maximum solar cycle event.

Autonomous driving is essential for the automation of agricultural systems, and current research primarily relies on using global positioning system (GPS) signals. However, in environments where GPS signals are unstable or denied, such as canopies and smart farms, GPS-based autonomous driving systems face limitations. Therefore, this study proposes an unmanned aerial vehicle autonomous driving system using three-dimensional light detection and ranging simultaneous localization and mapping (3D LiDAR SLAM). The proposed algorithm follows three steps: (1) A map is generated using 3D LiDAR. Because it targets an environment with height restrictions, lightweight and ground-optimized LiDAR odometry and mapping (LeGO-LOAM), which has the characteristic of extracting the ground, is used as a 3D LiDAR SLAM algorithm. (2) Noise is removed through post-processing. (3) A path is generated using the A* algorithm. To evaluate the performance of the proposed system, field evaluations were conducted in an outdoor canopy orchard using GPS and real time kinematic (RTK) GPS data as references. The experimental results verified the limitations of existing GPS- and RTK GPS-based methods and demonstrated the good performance and effectiveness of autonomous driving using 3D LiDAR SLAM. These findings suggest the feasibility of automated operations in environments where GPS is unstable or denied, and efficient agricultural automation may be achieved in conjunction with future harvesting and pollination operations.

Global Positioning System (GPS) signals adopt spreading spectrum technology, which could effectively reduce the consumption of signal transmitting power. Nevertheless, the GPS signals may be blocked unexpectedly in complicated environments. In this case, the stronger signals transmitted from Low-Earth-Orbital (LEO) communication satellite systems could be considered to aid in GPS service provision. However, the dynamic of an LEO satellite is relatively high, which causes difficulties with its integration into the general GPS receiver design. Doppler positioning algorithms can benefit from this high dynamic characteristic. In this paper, considering Doppler shifts as measurements, a Doppler Positioning algorithm is introduced. Since no navigation message can be decoded from the signals, satellite orbit prediction using the two-line orbital element set (TLE) will be involved. One commercial LEO communication satellite system is selected as well as the configurable frontend Universal Software Radio Peripheral (USRP) for the LEO signal collection and processing. A static experiment in an open field is conducted in this research. The experimental results for the open field test indicate that the positioning performance might not gain improvement from the use of an LEO signal because of orbit prediction errors or the quality of its measurement is not as good as that of GPS; but the important thing is that the LEO signal can extend positioning service in situations where the number of GPS satellites in-view are less than four due to signal obstructions. This paper presents the results of the enhanced navigation service provided by GPS with the aid of the LEO satellite.

The application of unmanned vehicles in civilian and military fields is increasingly widespread. Traditionally, unmanned vehicles primarily rely on Global Positioning Systems (GPSs) for positioning; however, GPS signals can be limited or completely lost in conditions such as building obstructions, indoor environments, or electronic interference. In addition, countries are actively developing GPS jamming and deception technologies for military applications, making precise positioning and navigation of unmanned vehicles in GPS-denied or constrained environments a critical issue that needs to be addressed. In this work, authors propose a method based on Visual–Inertial Odometry (VIO), integrating the extended Kalman filter (EKF), an Inertial Measurement Unit (IMU), optical flow, and feature matching to achieve drone localization in GPS-denied environments. The proposed method uses the heading angle and acceleration data obtained from the IMU as the state prediction for the EKF, and estimates relative displacement using optical flow. It further corrects the optical flow calculation errors through IMU rotation compensation, enhancing the robustness of visual odometry. Additionally, when re-selecting feature points for optical flow, it combines a KAZE feature matching technique for global position correction, reducing drift errors caused by long-duration flight. The authors also employ an adaptive noise adjustment strategy that dynamically adjusts the internal state and measurement noise matrices of the EKF based on the rate of change in heading angle and feature matching reliability, allowing the drone to maintain stable positioning in various flight conditions. According to the simulation results, the proposed method is able to effectively estimate the flight trajectory of drones without GPS. Compared to results that rely solely on optical flow or feature matching, it significantly reduces cumulative errors. This makes it suitable for urban environments, forest areas, and military applications where GPS signals are limited, providing a reliable solution for autonomous navigation and positioning of drones.

In current state-of-the-art navigation systems combining a global positioning system (GPS) and an inertial navigation system (INS), the two subsystems are complimentary, and the combined system only works when GPS signals are available. Much research is being undertaken on methods to replace the role of the GPS in the GPS-INS systems for use in environments where the GPS signals are not available, including areas such as urban canyons, indoors, and areas where the GPS signals have been jammed. In this paper we present a method of replacing the GPS-aiding of the INS system, by using a ranging sensor such as a lidar or radar combining with an evidence grid methodology. The evidence grid is used to create three-dimensional maps based on the measurements of the ranging sensor and the inertial system. These maps are cross-correlated in time to determine how best to correct for navigation drift, with the corrections then fed back into the extended Kalman filter to aid the inertial navigation solution. Experimental results, based on lidar measurements, show that the method is capable of remarkable accuracy, able to correct errors of less than one meter over six minutes of travel.

Reliability testing, namely receiver autonomous integrity monitoring (RAIM), consists of statistical testing of least-squares residuals of observations, e.g., on an epoch-by-epoch basis aiming towards reliable navigation fault detection and exclusion (FDE). In this paper, classic RAIM and FDE methods are extended with testing of range-rate residuals to find inconsistent velocity solutions in order to contribute to the reliability of the system with special focus on degraded signal environments. Reliability enhancement efforts discussed include a Backward-FDE scheme based on statistical outlier detection and an iteratively reweighted robust estimation technique, a modified Danish method. In addition, measurement weighting assigned to code and Doppler observations is assessed in the paper in order to allow fitting a priori variance models to the estimation processes. The schemes discussed are also suitable in terms of computational convenience for a combined GPS/Galileo system. The objective of this paper is to assess position and velocity reliability testing and enhancement in urban and indoor conditions and to analyze the navigation accuracy conditions with high sensitivity GPS (HSGPS) tests. The results show the necessity of weighted estimation and FDE for reliability enhancement in degraded signal-environment navigation.

Multipath (MP) is the dominant error source in Global Positioning System (GPS) code-based position solutions requiring high accuracy. A technique is introduced here to mitigate error due to ground-reflected MP signals. The technique uses two orthogonal dipoles to capture the direct GPS signal and the ground-reflected GPS signal. Adjusting the amplitude and phase of the received voltage between the two dipoles can reduce the impact of MP error. Theoretical derivations of this technique are performed for a GPS signal upon reflection from dry soil, seawater, and fresh water. The theoretical results are verified with a real world experiment on the aforementioned surfaces. GPS pseudo-range (PR) and carrier-to-noise ratio (C/No) measurements for specific satellites are used to verify the predicted theoretical results.

Currently, global navigation satellite system (GNSS) receivers can provide accurate and reliable positioning service in open-field areas. However, their performance in the downtown areas of cities is still affected by the multipath and none-line-of-sight (NLOS) receptions. This paper proposes a new positioning method using 3D building models and the receiver autonomous integrity monitoring (RAIM) satellite selection method to achieve satisfactory positioning performance in urban area. The 3D building model uses a ray-tracing technique to simulate the line-of-sight (LOS) and NLOS signal travel distance, which is well-known as pseudorange, between the satellite and receiver. The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange. The measurement of the satellite will be excluded if the simulated and raw pseudoranges are inconsistent. Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal. According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.