Indoor Positioning Using WiFi Fingerprint

Object positioning is a trending topic currently. The global positioning system (GPS) is the most common positioning method. Cars rely on GPS to navigate and plan routes to destinations. Unmanned aerial vehicles employ the GPS to assist flight and achieve cruise functions of automatic altitude and fixed point flights. However, the GPS fails when positioning is conducted indoors because GPS signals cannot be received indoors. Therefore, an indoor positioning system (IPS) should be used for indoor positioning. This paper mainly discusses the localization error of an ultra-wideband (UWB) indoor positioning system. A two-triangle positioning method was used to locate a target point separately through each triangle. Finally, values obtained using the two triangles were calculated using an algorithm to obtain a more accurate position.

Global positioning system (GPS) based indoor positioning techniques generally utilize repeaters to amplify and retransmit the original GPS signals to indoor receivers. In this paper, a GPS repeater based indoor positioning approach is proposed. Unlike most repeater-based indoor positioning techniques in current literature, repeaters are not required to connect to an outdoor antenna with fixed length cables. Neither do repeaters need to be synchronized. Each repeater retransmits the GPS signals continuously to the indoor receivers without sequential switching. Assume more than one satellite is visible to four or more repeaters. At a measuring time instance, the range from a repeater to the user receiver is identical for any travelling satellite signal, which is repeated by this repeater. Incorporating this feature and comparing the ranges measured by means of repeated signals of different satellites, the receiver is able to identify the repeater, which repeats a given satellite signal. With the known coordinates of the identified repeaters and the ranges from repeaters to the receiver, then the receiver position can be found. To compromise the impact of the repeater signal processing time difference on positioning accuracy, this work proposes two methods to calculate the receiver position and fuses the results by Kalman filter. Furthermore, considering the general indoor positioning environments, altitudes of floors of a building are utilized to assist in improving the positioning performance. Simulation results with true repeater coordinate data are provided to verify the proposed approach.

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.

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.

: 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.

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 Positioning System (GPS) is widely applied for outdoor positioning. However, GPS signals may degrade in an indoor environment. A nursing cart with a stable and accurate indoor positioning system inside is highly demanded in the hospital. The cart can be used for tracking the employees, or positioning movable medical devices. In this paper, we propose a Pedestrian Dead Reckoning (PDR) -based indoor positioning system with iBeacon calibrations. The proposed system can be implemented in resource- limited embedded device. To evaluate the property of the proposed system, real experiments under indoor environments have been conducted. The experimental results showed the effectiveness of the proposed approach. We also compared the impact of the number of iBeacons on positioning accuracy.

To ensure reliable land vehicle positioning, the Global Positioning System (GPS) is one of the best techniques commonly used in present-day positioning systems. However, GPS signals are not available if line of sight with the satellites is lost. In this respect, the addition of the Dead Reckoning (DR) method to complement the GPS unit would enhance the reliability of land vehicle positioning system. For implementing the DR method, the magnetic sensor and accelerometer are used for obtaining heading and velocity or distance information. Calibration of two sensors’ dynamic model and DR algorithm are also introduced in this paper. The test result utilizing the GPS and DR methods is analyzed in this paper. It utilizes positioning information from GPS receiver when GPS signal is available, otherwise DR is started to compensate GPS outage with the GPS output as the original point. The results showed that the error in east and north produced by the DR algorithm for a short time interval can be limited within an acceptable range.

A Global Positioning System (GPS) is widely used as a method of outdoor localization. However, indoor localization system is required because the GPS signals are disturbed indoors. Although the fingerprinting method is a simple and inexpensive method of indoor localization, it is affected by environmental factors such as multipath effects. This paper proposes a Convolutional Neural Networks (CNN)-based localization model that consists of pooling layers for Wi-Fi fingerprinting indoor localization. These pooling layers of CNN can extract features of RSSI that contribute to the localization. Experimental results show that the proposed model reduces MAE about 16%. Furthermore, the coefficient of determination improves by 0.5 compared to the fingerprinting method.

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.

For numerous hydrological applications, information on snow water equivalent (SWE) and snow liquid water content (LWC) are fundamental. In situ data are much needed for the validation of model and remote sensing products; however, they are often scarce, invasive, expensive, or labor‐intense. We developed a novel nondestructive approach based on Global Positioning System (GPS) signals to derive SWE, snow height (HS), and LWC simultaneously using one sensor setup only. We installed two low‐cost GPS sensors at the high‐alpine site Weissfluhjoch (Switzerland) and processed data for three entire winter seasons between October 2015 and July 2018. One antenna was mounted on a pole, being permanently snow‐free; the other one was placed on the ground and hence seasonally covered by snow. While SWE can be derived by exploiting GPS carrier phases for dry‐snow conditions, the GPS signals are increasingly delayed and attenuated under wet snow. Therefore, we combined carrier phase and signal strength information, dielectric models, and simple snow densification approaches to jointly derive SWE, HS, and LWC. The agreement with the validation measurements was very good, even for large values of SWE (>1,000 mm) and HS (> 3 m). Regarding SWE, the agreement (root‐mean‐square error (RMSE); coefficient of determination (R2)) for dry snow (41 mm; 0.99) was very high and slightly better than for wet snow (73 mm; 0.93). Regarding HS, the agreement was even better and almost equally good for dry (0.13 m; 0.98) and wet snow (0.14 m; 0.95). The approach presented is suited to establish sensor networks that may improve the spatial and temporal resolution of snow data in remote areas.

A technique for a novel application of Global Positioning System (GPS) signals to ocean altimetry is described. The entire Earth surface is divided into a triangular grid of points nearly-uniformly spaced. The sea surface heights at the grid points are determined using the GPS signals reflected from the surrounding area. Practical grid size and data sampling rate are determined. An efficient estimation scheme is devised to solve for the thousands of parameters. A simulation analysis shows that global ocean altimetric information can be recovered to better than 10 cm with only 1 day of reflected GPS signals.

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

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.