Development of self navigation system for mobile vehicles without location information

While GPS (Global Positioning System) navigation and GPS based autonomous car driving techniques are mature, the security of GPS signal has not been a primary system concern. In fact, it has been shown that an ordinary GPS can be easily spoofed by a low-cost, open-source based software defined radio (SDR) system such as bladeRF and HackRF [3, 4] which can cause serious complications to the navigation system of the car especially for self-driving cars which are driven based on the information from sensors, cameras, and GPS. In this work, we design a new GPS authentication system called lightweight authentication GPS (AuthGPS) to authenticate GPS signal against GPS spoofing and LTE base station broadcast message spoofing. In the past, there have been many successful attempts on GPS spoofing which resulted in shifting the destination of the car to the spoofer's desired location. In a case where the car is connected to the Internet via LTE network, the navigation system can find out if the GPS is spoofed by acquiring the correct GPS satellite information from LTE base stations' location information. However, a skillful attacker can spoof the LTE base station signals [7] as well using another SDR which will produce spoofed LTE base station information. Hence giving wrong information about GPSinformation according to the will of the attacker. Currently, there are defense methods against the GPS spoofing [6]. One of them is signal-processing-based methods. By monitoring unusual or unreasonable signal changes at GPS receivers, GPS spoofing attack can be detected. Received Power Monitoring (RPM) looks at all the received amplitude and automatic gain control (AGC) setpoint [1]. The receiver will sense drastic power jump if a spoofing attack occurs. However, an overly powerful spoofing attack with noise is not detectable in the case of this mechanism. Another way to detect spoofing is the symmetric-key encryption mechanism of GPS signals. According to previous research [2], encrypted precision code for anti-spoofing referred to as P(Y) code, might not be able to be spoofed. A spoofing attack can be detected by calculating cross-correlation between spoofed coarse/acquisition code, referred to as C/A code, and P(Y) code. Unfortunately, this P(Y) code is only for military purpose while the C/A code can be publicly accessible. This method requires knowledge of specific key which is not revealed to the public to decrypt P(Y) code. Monitoring the direction of arrival of the signals can be one of the methods [6]. A GPS receiver measures the direction-of-arrival vector with more than 3 antennas. Even though this signal-geometrybased system with multiple antennas makes GPS robust, an attacker can spoof signals from multiple directions, which is more difficult to detect the spoofing. The idea here is to develop a system that can discriminate the spoofed signal without complex computation or heavy message exchange. An important component of AuthGPS is a verification of valid GPS satellite information via LTE base stations' location information. We propose 6-digit one-time password-based authentication system in this paper.

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.

Global Positioning System (GPS) information is stored in EXchangeable Image File Format (EXIF) in the image file. It is considered personal information; therefore, sharing it without the owner’s consent could lead to personal information disclosure. GPS information is embedded on the images uploaded by many people on social network services or stored in the Cloud. Images must be encrypted to prevent GPS information from being disclosed. However, there are no existing methods to encrypt them in EXIF. Using block cipher for encryption can exceed the tag value range of EXIF. In this paper, we propose a method to encrypt the GPS information of an image file using Format-Preserving Encryption, which preserves the length and format of the plaintext. We confirmed that GPS information can be secured by encrypting it in image files.

: 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.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

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.

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.

Navigation signals are the basis of satellite navigation systems, which determine the navigation systems' performance (such as tracking accuracy, anti-interference capability etc.), and are determined by some constraints (such as radio frequency resources, construction cost, technology level etc.). The aim of signal design is to select "proper" signals considering these above factors and make tradeoffs between performance and constraints. This paper will summarize the signal structure evolution of present satellite navigation and conclude some principles and issues which will be greatly valuable to our 2nd satellite navigation system. There's four main satellite positioning systems presently, the U.S. Global Positioning System (GPS), the Russian Global Navigation Satellite System (GLONASS), the Europe Galileo satellite navigation system and the Chinese BD-1 satellite positioning system. Among these systems, the signal characteristics of GPS and Galileo are good reference to our 2nd satellite navigation system. The improvement of GPS signals is a main aspect of GPS modernization. The aim of GPS modernization is mainly to modernize its signal structure so as to improve the whole systems' performance. The signal structure of Galileo has been revised for several times, and its present signal is the results of long time signal design. From GPS modernization and Galileo signals status, we can summarize some key specifications of navigation signals and some technologies to support these specifications. Based on this, we'll discuss some problems which should be attentive in our country's 2nd satellite navigation system and give some suggestions. This paper is organized into five sections. The first section will give a brief introduction of present satellite navigation system and their main features. Section two will review the GPS and Galileo signals structure evolution, including GPS modernization, Galileo signals status and the comparison of two systems' signals. In the third section, the relationship between signal parameters and system performance will be analyzed, including RF signal characteristics, modulation type, ranging code (such as code type, length and rate) and navigation data. In section four we will discuss some inspiration and technologies concerning our 2nd satellite navigation system, such as the use of multiple frequency signals (ionospheric delay estimation and carrier ambiguity resolution), pilot tones & dataless channel, binary offset carrier (BOC) modulation, and so on. The last section is conclusion, in which the whole paper will be retrospected and some useful conclusion will be given.

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.

The Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) are the prime land and autonomous vehicles navigation information source. However, urban canyons high rise buildings block the GPS signal. Therefore, the Inertial Navigation System (INS) or the Reduced Inertial Sensor System (RISS) are utilized as an alternative source of the navigation information. The RISS is able to produce a full navigation solution using less number of sensors and calculations. Dramatically, the RISS solution drifts over time as the INS system. The integration between the GPS and the RISS is mitigating each system drawbacks. However, during the GPS outage periods, the navigation system is only depending on the inertial measurements. The RISS is utilizing the odometer/speedometer to measure the vehicle's forward speed. Many types of error affect the odometer measurements. These errors are either due to the vehicle's specifications as the differences in wheel diameters, and/or inefficient wheelbase or the road nature as wheel slips, uneven road surfaces, and/or skidding. In this paper, a Radar-based RISS system is introduced to take place the traditional RISS or INS systems. The radar unit is an essential part of the adaptive cruise control (ACC) system. Furthermore, it is capable of measuring the relative velocity and distance between the carrying and the in front vehicle to reduce collisions and increase the safety of driving. In addition, the radar measurements are not affected by the error sources that affect the odometer. The idea is to invest the ground reflection and derive the forward speed of the onboard vehicle as the ACC keep a safe distance between the cars. A novel method based on extracting the ground reflection features is introduced to obtain the onboard vehicle's speed. The obtained speed is utilized with two accelerometers and one vertical gyroscope to produce the Radar-based RISS system. The proposed system has been integrated with the GPS producing a Radar-based RISS/GPS integrated navigation system. The system has been tested on a real road trajectory in a downtown area and involved several GPS signal outages. The results show the significant capabilities of the proposed system in keeping the navigation solution drift to a minimum especially when the GPS signal is in outage compared with the traditional RISS/GPS system.

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.

Global Positioning System (GPS) spoofing is a common mean to cheat an Unmanned Aerial Vehicle (UAV) which is guided by the INS/GPS integrated navigation system. Detecting the spurious GPS signal is the precondition of anti-spoofing for UAVs. A detection system of GPS spoofing is designed based on dynamics identification. The centroid motion model and attitude motion model were established. The measurement information from inertial measurement units (IMUs) and positioning information from GPS both are visualized responses to the dynamic laws of UAVs. These two types of measurement information were separately used to estimate the dynamics parameters of UAVs online using two Extended Kalman Filters (EKFs). The detection of spurious GPS signals was realized effectively based on the monitoring of relative estimation errors. Numerical simulations demonstrated that the dynamics parameters could be estimated online by the combined filters. And the precision and accuracy of filter #1, which was based on IMU information, were both higher than those of filter #2, which was based on GPS information. When the spurious GPS signal was just zero-order continuous with the true signal at the penetration point, a pulse relative error would occur at the penetration point; when the spurious GPS signal was first-order continuous with the true signal at the penetration point, a step relative error would occur. Furthermore, relative systematic errors matched with the period of spurious signals.

A Kalman filter updating method for the indoor moving object database

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.