정밀 좌표변환 알고리즘을 이용한 인공위성 항로추적시스템에 관한 연구

Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

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.

Navigation systems for ships mainly include inertial navigation system (INS), “BeiDou” satellite navigation system, Global Positioning System (GPS), and Doppler navigation system and so on. The characteristics of each system are different. In order to improve the accuracy and fault-tolerant ability of naval-ship navigation system, an integrated navigation algorithm based on Measurement-based adaptive Kalman filtering algorithm (MAKF), improved Suge-Husa algorithm and non-reset federated Kalman filter structure is proposed in this paper. The algorithm uses the short-term high precision of the inertial navigation system and the prior velocity information of the ship to adaptively estimate the measurement noise characteristics of the satellite navigation system. When the satellite navigation system is unable to locate, the INS and the log are combined with the help of the prior velocity information. The simulation results show that the algorithm can effectively exploit the characteristics of each navigation system, and can output high-precision navigation results even when the GPS/BeiDou satellite navigation system fails.

Localization of a vehicle is a key component for driving assistance or autonomous navigation. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches, which rely on some predefined known landmarks or a priori information about the environment, no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3-D information of the scene from image sequences. Relative positions of the camera motion are then estimated by registering the 3-D feature points from two consecutive image frames. Localization of the vehicle is finally given by the reference to its initial position. I. INTRODUCTION Localization and tracking of a vehicle are major compo- nents for providing positions, directions and travel informa- tion to the driver. They also serve as key technologies for building autonomous navigation systems. Many approaches have been proposed for locating a mobile robot or a ve- hicle based on various techniques. The most commonly used methods include dead-reckoning techniques, navigation using active beacons, landmark-based navigation, map-based navigation, global positioning system (GPS), and vision- based positioning. Dead-reckoning is a procedure for determining the present location of a vehicle by advancing some previous position through known path and velocity information over a given period of time. Since an odometer and optical sensors for wheel direction detection are easily installed on a vehicle, dead-reckoning is usually a less expensive method for vehicle localization. However, the integration of incremental motion information over time will lead to accumulation of errors. In the case of long distance travel, accumulation of orientation error will make position errors diverge as the increase of driving time. Active beacons such as laser, sonar or radio can be used as media for vehicle or mobile robot navigation. These approaches use triangulation to measure the distance between a number of beacons and the mobile platform and then deter- mine the current location. The problems associated with this technique are the inaccuracy of the distance measurement caused by time-delay of the signals, and the installation and maintenance cost of a large number of beacons required for an area. Satellite based differential global positioning system (DGPS) is probably the most advanced and accurate method for identifying the position and orientation of an object. However, DGPS does not work well if the satellite signals are blocked. This situation commonly happens in the indoor environments, or urban areas with tall buildings, etc. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches which rely on some predefined known landmarks or ap rioriinfor- mation about the environment (e.g., 3-D models of buildings, objects, etc.), no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3- D information of the scene from image sequences. A robust feature tracking method based on simultaneously considering the inter-frame images from the same camera and the stereo image pair from different cameras at a fixed time instant is developed. The relative position of the camera motion is then estimated by registering the 3-D feature points from two consecutive image frames. Our system includes the following basic modules:

E-navigation Services for Non-SOLAS Ships

Multi-source fusion constitutes a research focus in the navigation domain. This article focuses on the online estimation of the mounting angles between the body frame and vehicle frame within low-cost embedded vehicle navigation modules and the lever arm between the global satellite navigation system (GNSS) antenna/odometer and the inertial measurement unit (IMU). An online mounting angle error estimation algorithm, using odometers and IMU speeds, has been developed to estimate the angle errors while vehicles are in motion. At the same time, an online estimation algorithm model for the GNSS antenna lever arm and odometer lever arm was constructed. These two types of lever arms are used as the estimated states, and then Kalman filters are used to estimate them. The algorithm can simultaneously estimate the IMU mounting angle error, GNSS antenna arm, and odometer arm online. The experimental outcomes demonstrate that the lever arm estimation algorithm presented herein is effective for tactical and MEMS-level inertial navigation, with an estimation error of less than 2 cm. Meanwhile, the proposed online estimation of the mounting angle algorithm has an accuracy comparable to that of the post-processing algorithm. After making up the mounting angle and lever arm, we found that the position and speed precision of the multi-source fusion navigation systems were significantly improved. The results indicate that the proposed online estimation of mounting angle error and lever arm algorithm are effective and may promote the practical and widespread application of integrated navigation systems in vehicles. It solves the shortcomings of traditional methods, including the cumbersome and inaccurate manual measurement of the lever arm. It provides a technical solution for developing a more accurate and convenient low-cost vehicle navigation module.

This work presents an improvement of an inertial-Doppler underwater navigation system for unmanned underwater vehicles (UUV) using a complementary range sonar. The inertial-Doppler navigation system is generally based on an inertial measurement unit (IMU) and a Doppler velocity log (DVL) accompanying a magnetic compass and a depth sensor. Although conventional navigation systems update bias errors of the inertial sensors, scale effects of the DVL, the estimated position slowly drifts as time passes. This paper proposes a measurement model with additional range sonar to improve the performance of the IMU-DVL navigation system for underwater vehicles. The proposed navigation model based on inertial navigation systems includes the scale effects, the bias errors of the DVL, and the error model of the range sonar, where the order of the system states is 21. An extended Kalman filter was adopted to propagate the error covariance, updated the measurement errors and correct the state equation when the external measurements are available. Simulation conducted with the 6-d.o.f. equations of motion of an AUV in lawn-mowing survey mode illustrates the effectiveness of the IMU-DVL navigation system assisted by the additional range measurement.

Summary: The driver sometimes is not satisfied by the route chosen by the Car Navigation System. Therefore, the function and the concept of the current Car Navigation System were examined. It can be said that the driver's subjectivity can be changed based on objective conditions. There are three main results. 1. Derive the objective element, factoring in predicted conditions. 2. Based on a conditional node, make predictions which will make a judgment about the subjective element. 3. Example all possible route and at the conditional node make a judgment about the subjective element based on the weight of all the conditions. The background of the research was obtained from a practical example of decision-making problems. It is a system rapidly spreading throughout Europe, America and Japan. The system can be described a functional part of the car, guiding the driver to the desired destination by the displaying road information. This is called a Car Navigation System in Japan. When this is used, the drivers depend on the map information shown on the display. Based on the input given by the driver, the Car Navigation System shows the most efficient route based on time and distance. Generally, drivers do not drive the most direct route to the destination point. Usually when driving, even if a person preplans the route, he will have a desire to make impulsive stops along the way. Depending on the driver, he might even stop for brief rest to enjoy the view or spend some time on a leisure activity. These types of stops enhances the driving pleasure and are determined subjectively based on the individual driver. The driver can input desired leisure spots into the Car Navigation System. This input causes the Car Navigation System to plan desired stops along the final destination point. However after driving, the driver sometimes is not satisfied by the route chosen by the Car Navigation System. The dissatisfaction is caused by the system's inability to anticipate the driver's immediate need. For example, based on the driver's pretend stop points, the system may direct the driver to a tennis court, but by the time of arrival, it could be raining or the road driven may be congested, slowly the drive. In these cases, the drive to the courts would have been time wasted. The driver's desired stop point is subjective input while the change in weather change or traffic patterns are objective elements. The destination decided is influenced by both of these factors. Even if these are many objective elements, they are transitional so events that may influence can be predicted. Therefore, the function and the concept of the current Car Navigation System were examined. Although the system's function can guide the driver to the desired destination points, but it cannot forecast changes in driving conditions. In the future, there is a prospect for a new function of Car Navigation System based on model designed using an AHP.

In recent years, navigation systems have been widely used in various amusement parks and sightseeing spots. Navigation systems, such as car navigation systems and navigation applications in smartphones, are also used in our daily life. By using a navigation system, users can not only find their location but also browse information on nearby attractions such as restaurants or department stores; furthermore, it can tell users the route to lead them to their destination. In these situations users will set their destinations by using the navigation system, but in tourism or for events, users have no idea about the current conditions. On the other hand, organizers will have a hard time trying to control the crowd flow as more and more tourists use their own mobile devices to search for the information they want. In this research, we propose a navigation system to control the crowd flow and lead users to specific destinations or areas naturally by presenting suitable information to them. In this paper, we will explain the experiments performed using the proposed system during an event held in Osaka called "Osaka Mizube Bar". The results of the experiment confirmed that the behavior of users can be affected by controlling the rank list that they check on their devices; furthermore, users can be led to a specific destination or area by taking advantage of this function.

It is possible for the visually impaired to walk their known area independently by using a white cane. However, in their unknown area, they cannot walk without help of others even if they can use a white cane very well. In such cases, not only a visually impaired person but he/she who assists them receives many stress in dairy activity. Therefore, many types of independent walking assist system for the visually impaired are developing. Our objective of this study is the development of an auto navigation system for the visually impaired which assists independent walking of them. This system measures the position of a visually impaired person (a user) by using GPS (Global Positioning System) and navigates him/her to the destination like as a car navigation system. In our system, a location data is stored in the map database and a position of user are analyzed and optimal route for a user to the destination is calculated. Our system guides a user to the destination along this route, and notifies the user of this route and some information for the safety walking by artificial voice. Three normal subjects were tested with our navigation system. All subjects were blind folded by an eye mask and equipped a navigation system. As a result, our system worked well and all subjects were able to walk to the destination following guidance voice. So, we are developing a new navigation system which is installed on a cellular phone. In Japan, there are some cellular phones that include GPS. And most of application programs for cellular phone are written by JAVA. Therefore, GPS included in a cellular phone will be used and every functions of the navigation system will be written by JAVA. In this paper, we also reports about this new navigation system.

Car navigation systems (CNS) have become an interesting research and industrial topic due to their wide deployment. However, updating and distribution of the road map database is a complex and high cost process. In this paper, we propose a real-time map generation and updating algorithm, which fulfills or corrects the unknown road area of the conventional road map database by using global positioning system (GPS) information and wireless communication technologies. To generate the road map of unknown roads, the proposed algorithm uses road images, which can be obtained when a driver travels unknown roads. When a driver travels unknown roads, which do not appear on the map of the car navigation system, the car navigation system extracts GPS and image information of the unknown roads. Then, the car navigation system generates the road map for the unknown roads by extracting location and road information, and updates the road map database in the car. After updating the local road map database, the car navigation system updates the master road map database, which is located at the network and is distributed to other car navigation systems. Thus, the road map database provider can update the road map database of other car navigation systems, which share the same master road map database. The proposed algorithm provides an efficient updating process of the road map database with a simplified process and reduced time for unknown road areas.

To improve the navigation accuracy of AUV (autonomous underwater vehicle), SINS (strapdown inertial navigation system), GPS (global positioning system), DVL (Doppler velocity log) and TAN (terrain aided navigation) are adopted in the AUV integrated navigation system. The mathematic model of the AUV integrated navigation system and the observation model of the chosen navigation sensors are built according to the system simulation experiments data. An improved filter based on RBF neural network for adjusting the information sharing factors is designed and implemented in the AUV integrated navigation system. Simulation experiments are carried out according to the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy is improved substantially with the multiple sensors and federated filter in case that colored noise is engaged. The novel integrated navigation system is effective in prohibiting the divergence of the filter and improving fault tolerance ability and it greatly raises the precision of the navigation accuracy for the AUV integrated navigation system.

The marine navigation system is the precondition for all kinds of military and civilian marine vehicles to reach the polar regions and sail safely. However, the special environment of the polar region reduces the performance of the navigation system, which even cannot be applied. In order to fundamentally understand the application of marine navigation systems in polar regions. On the basis of systematically expounding the development history of marine vehicle navigation systems applied to polar regions. We propose to comprehensively analyze the impact of the special environment of the polar region on the main navigation system of the marine vehicle in four aspects: earth kinematics, geophysical field, marine environment and geographic environment. And we provide a valuable reference for combing the research context and future development of marine navigation technology through the research status of the marine navigation system in the polar region and its prospects for future trends.

The autonomous vehicle navigation system is heavily dependent on the use of GPS sensors. The poor accuracy of GPS sensors can be circumvented by combining other sensors using a Kalman filter-based method. Combining GPS and IMU sensors or other sensors is an option because of the relatively low cost. The use of controllers to build an autonomous vehicle navigation system becomes an important thing, so the controller devices used must have excellent performance, and it is found in UA V technology with autopilot-based controllers. The sensor combination method is carried out by combining the IMU sensor on the pixhawk 2.1 controller module with the Here2 GPS/GNSS Module, which is expected to make autonomous vehicles' position and heading orientation more accurate. The initial test results show that the navigation system accuracy is very good with the orientation angle towards the vehicle as expected. However, there was a problem in sending data from the vehicle to the ground station via telemetry radio due to distance and buildings constraints.

To solve the localization failure problem of terrain-aided navigation (TAN) system of the autonomous underwater vehicle (AUV) caused by large area of underwater flat terrain in the Arctic, a navigation system with relocation part is constructed to enhance the robustness of localization. The system uses particle filter to estimate the AUV’s position and reduce the nonlinear noise disturbance, and the prior motion information is added to avoid the mismatching caused by the similar altitude of low-resolution map. Based on the estimate data and the measured altitude data, the normalized innovation square (NIS) is used to evaluate the differentiation of terrain sequence, and the differentiation is used as a judgment of whether the AUV is in the switch location. A simulation experiment is carried out on the 500 m resolution underwater map of the Arctic. The results show that adding the prior motion information can restrain the divergence of the estimator; NIS can accurately reflect the sharp change of terrain sequence. After the relocation process, the AUV can still maintain the positioning accuracy within 2 km after running 50 km in the area including flat and rough terrain. This research solves the problem of localization errors in the Arctic flat terrain in the system level and provides a solution for the application of underwater navigation in the Arctic.