Among various indoor positioning technologies, impulse-radio UWB is a promising technique to provide indoor positioning and tracking services with high precision. Because UWB regulations turned to imposing restrictions on UWB low band, UWB high band becomes attractive for enabling simple and low cost implementation. However, UWB high band endures much larger propagation loss than UWB low band. In this paper, we propose two separated methods to compensate the deficiency of high band in propagation. With the first method, we bundle several IR-UWB modules to increase the average transmission power, while an adaptive detection threshold is introduced at the receiver to raise receiving sensitivity with the second method. We respectively implement each of these two proposed methods and evaluate their performance through measurements in laboratory. The results show that each of them achieves about 7dB gains in signal power. Furthermore, positioning performance of these two proposed methods are evaluated and compared through field measurements in an indoor sports land.

With the increasing demand of Internet-of-Things applicability in various devices and location-based services (LBSs) with positioning capabilities, we proposed simple and effective post-processing techniques to reduce positioning error and provide more precise navigation to users in a pedestrian environment in this letter. The proposed positioning error reduction techniques (Technique 1-minimum range securement and bounce elimination, Technique 2-direction vector-based error correction) were studied considering low complexity and wide applicability to various types of positioning systems, e.g., global positioning system (GPS). Through the real field tests in urban areas, we have verified that an average positioning error of the proposed techniques is significantly decreased compared to that of a GPS-only environment.

3GPP (3rd Generation Partnership Project) has started to discuss D2D (Device-to-Device)-aided OTDOA (Observed Time Difference Of Arrival) as one of the mobile positioning enhancement techniques for LTE (Long Term Evolution) systems. It is a kind of multi-node based OTDOA which directly receives D2D signals from adjacent multiple UEs (User Equipment) to measure RSTD (Reference Signal's Time Difference). D2D signals provide valuable advantages in terms of OTDOA positioning because it can guarantee more reference nodes and high SNR (Signal-to-Noise Ratio) of PRS (Positioning Reference Signal). Two typical methods for multi-node based OTDOA can be applied to D2D-aided OTDOA. Multiple OTDOA positioning is one of the multi-node based methods that averages multiple results from OTDOA; however, it cannot always guarantee high accuracy due to the non-uniform geometry of UEs. OTDOA positioning based on TSE (Taylor Series Expansion) algorithm may be one of the solutions; however, it has the initial value problem and high computational complexity due to its iterative procedure. Therefore, in this paper, we propose a novel D2D-aided OTDOA positioning method which utilizes UEs not as reference node of OTDOA but as assisting node for RSTD error reduction. The proposed method can reduce RSTD error of eNB based hyperbola by using multiple hyperbola bands. The hyperbola band indicates the possible range in which a hyperbola can occur due to RSTD error. Then, by using principal axes of hyperbolas, we estimate a modified hyperbola from the overlap area of hyperbola bands, which has less RSTD error. We verify that the proposed method can effectively reduce RSTD error and improve positioning performance with lower computational complexity.

This paper presents the set of procedures to blend GNSS and V2V communication to improve the performance of the stand-alone on-board GNSS receiver and to assure mutual positioning with a bounded error. Particle filter algorithm is applied to enhance mutual positioning of vehicles, and it fuses the information provided by the GNSS receiver, wireless measurements in vehicular environments, odometer, and digital road map data including reachability and zone probabilities. Measurement-based statistical model of relative distance as a function of Time-of-Arrival is experimentally obtained. The number of collaborative vehicles to the mutual positioning procedure is investigated in terms of positioning accuracy and network performance through realistic simulation studies, and the proposed mutual positioning procedure is experimentally evaluated by a fleet of five IEEE 802.11p radio modem equipped vehicles. Collaboration in a VANET improves availability of position measurement and its accuracy up to 40% in comparison with respect to the stand-alone GNSS receiver.

In this paper, we explore the relationship between Geometric Dilution of Precision (GDOP) and Cramer-Rao Bound (CRB) by tracing back to the original motivations for deriving these two indexes. In addition, the GDOP is served as a sensor-target geometric uncertainty analysis tool whilst the CRB is served as a statistical performance evaluation tool based on the sensor observations originated from target. And CRB is the inverse matrix of Fisher information matrix (FIM). Based on the original derivations for a same positioning application, we interpret their difference in a mathematical view to show that.

A novel virtual sensors-based positioning method has been presented in this paper, which can make use of both direct paths and indirect paths. By integrating the virtual sensor idea and Bayesian state and observation framework, this method models the indirect paths corresponding to persistent virtual sensors as virtual direct paths and further reformulates the wireless positioning problem as the maximum likelihood estimation of both the mobile terminal's positions and the persistent virtual sensors' positions. Then the method adopts the EM (Expectation Maximization) and the particle filtering schemes to estimate the virtual sensors' positions and finally exploits not only the direct paths' measurements but also the indirect paths' measurements to realize the mobile terminal's positions estimation, thus achieving better positioning performance. Simulation results demonstrate the effectiveness of the proposed method.

In this letter, a novel channel impulse response (CIR)-based fingerprinting positioning method using kernel principal component analysis (KPCA) has been proposed. During the offline phase of the proposed method, a survey is performed to collect all CIRs from access points, and a fingerprint database is constructed, which has vectors including CIR and physical location. During the online phase, KPCA is first employed to solve the nonlinearity and complexity in the CIR-position dependencies and extract the principal nonlinear features in CIRs, and support vector regression is then used to adaptively learn the regress function between the KPCA components and physical locations. In addition, the iterative narrowing-scope step is further used to refine the estimation. The performance comparison shows that the proposed method outperforms the traditional received signal strength based positioning methods.

The limitation of the GPS in urban canyon has led to the rapid development of Wi-Fi positioning system (WPS). The fingerprint-based WPS could be divided into calibration and positioning stages. In calibration stage, several grid points (GPs) are selected, and their position tags and featured access points (APs) are collected to build fingerprint database. In positioning stage, real time measurement of APs are compared with the feature of each GP in the database. The k weighted nearest neighbors (KWNN) algorithm is used as pattern matching algorithm to estimate the final positioning result. However, the performance of outdoor fingerprint-based WPS is not good enough for pedestrian navigation. The main challenge is to build a robust fingerprint database. The received number of APs in outdoor environments has large variation. In addition, positioning result estimated by GPS receiver is used as position tag of each GP to automatically build the fingerprint database. This paper studies the lifecycle of fingerprint database in outdoor environment. We also shows that using long time collected data to build database could improve the positioning accuracy. Moreover, a new 3D-GNSS (3D building models aided GNSS) positioning method is used to provide accurate position tags. In this paper, the fingerprint-based WPS has been developed in an outdoor environment near the center of Tokyo city. The proposed WPS can achieve around 17 meters positioning accuracy in urban canyon.

In this work, the novel fingerprinting evaluation parameter, which is called the punishment cost, is proposed. This parameter can be calculated from the designed matrix, the punishment matrix, and the confusion matrix. The punishment cost can describe how well the result of positioning is in the designated grid or not, by which the conventional parameter, the accuracy, cannot describe. The experiment is done with real measured data on weekdays and weekends. The results are considered in terms of accuracy and the punishment cost. Three well-known machine learning algorithms, i.e. Decision Tree, k-Nearest Neighbors, and Artificial Neural Network, are verified in fingerprinting positioning. In experimental environment, Decision Tree can perform well on the data from weekends whereas the performance is underrated on the data from weekdays. The k-Nearest Neighbors has proper punishment costs, even though it has lower accuracy than that of Artificial Neural Network, which has moderate accuracies but lower punishment costs. Therefore, other criteria should be considered in order to select the algorithm for indoor positioning. In addition, punishment cost can facilitate the conversion spot positioning to floor positioning without data modification.

In this paper, we propose an infrastructure-free precise positioning system by utilizing a variation of received radio broadcast signal strength against vehicle travel as fingerprints of road segments. Use of broadcast wave is considered advantageous in deployment cost and sample density that affects measurement reliability, compared to communication medium such as 802.11p-based V2X radio used in our previous paper. We also present preliminary experimental results that indicate potential of positioning at 20cm accuracy by using reception information of two FM radio channels broadcast from a station about 20km away from the test track

This paper presents two new types of markers of M-CubITS (M-sequence Multimodal Markers for ITS; M-Cubed for ITS) that is a ground-based positioning system, in order to advance the WYSIWYAS (What You See Is What You Are Suggested) navigation environments providing intuitive guidance. One of the new markers uses warning blocks of textured paving blocks that are often at important points as for pedestrian navigation, for example, the top and bottom of stairs, branch points, and so on. The other uses interlocking blocks that are often at wide spaces, e.g., pavements of plazas, parks, sidewalks and so on. Furthermore, we construct the integrated pedestrian navigation system equipped with the automatic marker-type identification function of the three types of markers (the warning blocks, the interlocking blocks, and the conventional marker using guidance blocks of textured paving blocks) in order to enhance the spatial availability of the whole M-CubITS and the navigation system. Consequently, we show the possibility to advance the WYSIWYAS navigation environments through the performance evaluation and the operation confirmation of the integrated system.

Incoming GPS signals through windows can be often observed indoors. However, conventional indoor positioning systems do not use Global Positioning System (GPS) generally because the signals may come in NLOS (Non Line of Sight). In this paper, we propose a positioning method by fingerprinting based on the incoming GPS signals.

This paper presents a design methodology for positioning sub-platform from the viewpoint of positioning for smartphone-based location-based services (LBS). To achieve this, we analyze a mechanism of positioning error generation including principles of positioning sub-systems and structure of smartphones. Specifically, we carry out the experiments of smartphone positioning performance evaluation by the smartphone basic API (Application Programming Interface) and by the wireless LAN in various environments. Then, we describe the importance of considering three layers as follows: 1) the lower layer that caused by positioning sub-systems, e.g., GPS, wireless LAN, mobile base stations, and so on; 2) the middle layer that caused by functions provided from the platform such as Android and iOS; 3) the upper layer that caused by operation algorithm of applications on the platform.

The increasing demand for navigation and automation has led to the development of a number of accurate and precise navigation applications that make use of the Global Navigation Satellite System (GNSS) and additional sensors. One of the precise navigation techniques in GNSS, the real-time kinematic (RTK) technique, is well known. In this method, once the correct integer ambiguities are found in the carrier phase observation data, position can be determined to within 10cm. In particular, the advent of QZSS and BeiDou satellites can increase the availability of RTK-GNSS (relative to RTK using only GPS). It is understood that the increasing availability of RTK-GNSS will improve the performance of the integration of GNSS with additional sensors because the errors due to the inertial measurement unit (IMU) accumulate as time goes on. On the other hand, GNSS tends to suffer from multipath errors, especially in urban environments. To overcome this problem, a method was developed for improving RTK-GNSS using a low-cost IMU and conventional vehicle speed sensors. In this study, the quality of the complete observation data was assessed based on the carrier-to-noise ratio and satellite elevation angle, and the least-squares ambiguity decorrelation adjustment method and the ratio test were used to obtain fixed positions. We used speed information obtained from Doppler measurements as an alternative source of information; information from the IMU and vehicle speed sensor (integrated with the RTK-GNSS via a Kalman Filter) was used when there were no visible satellites. We also used the IMU and vehicle speed sensors to detect wrong fixes in the RTK-GNSS. A position and orientation system for land vehicles (Applanix) was used to estimate the reference positions. During GNSS outages, it is important to accurately determine the last heading of the car for precise navigation. In this study, it was found that GNSS Doppler-based direction data are required to obtain better direction information. The results of the experiment demonstrate that our proposed method is, to some extent, beneficial as an alternative to the conventional RTK-GPS in an urban environment.

Currently, IEEE802.11p and ARIB STD T-109 are available as the typical inter-vehicle communication (IVC) standards. Carrier sense multiple access/collision avoidance (CSMA/CA) and orthogonal frequency division multiplexing (OFDM) are used in these standards. However, the performance degrades when there are hidden terminals. In this paper, IVC system that using a direct sequence spread spectrum (DS/SS) modulation scheme is discussed because it has code division multiple access (CDMA) capability. In DS/SS-IVC scheme, it is possible to avoid hidden terminal problem. On the other hand, near-far problem (NFP), multiple access interference (MAI) and interference by equivalent pseudo noise (PN) codes occurs in DS/SS communication. These problems cause performance degradation. In this paper, interference cancellation scheme and slotted ALOHA scheme with code sensing are applied so as to mitigate the impact of MAI, NFP and interference by equivalent PN code. By applying interference cancellation scheme and slotted ALOHA scheme with code sensing, the performance of DS/SS-IVC is improved. In this paper, location oriented PN code allocation is focused on as a method of PN code assignment. However, DS/SS-IVC scheme based on location oriented PN code allocation has a problem. Since each vehicle obtain PN code based on the position that is estimated by GPS, performance degrades when GPS positioning error occurs. Therefore, the positioning system of DS/SS-IVC scheme is also discussed in this paper. Elimination of ranging data that has large ranging error is proposed in addition to interference cancellation scheme and slotted ALOHA scheme with code sensing in order to improve the performance of positioning. From the simulation results, the positioning error can be mitigated by applying these proposed techniques.

User context and user location in particular play an important role in location-based services (LBS). The location can be determined by various positioning methods. These are typically evaluated with average positioning error or percentile values, which are not the most suitable metrics for evaluation of how a positioning method functions in the semantic space. Therefore, we propose a new method for evaluation of positioning accuracy in the semantic space. We focus on two types of semantic user locations that are widely available in urban areas: the street address and the categories of the surrounding points of interest (POIs). We demonstrate its use on ten different positioning methods: a standalone satellite navigation device, GPS module on a smartphone, two versions of Foursquare positioning service, Google positioning service, a positioning service of the local mobile operator, and four other possible variants of mobile operator-based positioning methods. The evaluation suggests that approach with the street addresses is more promising approach due to either sparse or unevenly distributed POIs. Furthermore, some of the positioning methods that are less accurate in Euclidean space, such as a combination of the GPS data with the mobile operator-based method that relies on the propagation models, performed comparably well in the semantic space as the methods that are using more accurate technologies, such as Google and Foursquare.

To achieve accurate wireless-local-area-network (WLAN) positioning, the directivity and influence of multipath fading on the power absorption by the user are clarified experimentally. Based on the results, a general model of the power absorption by the user is devised. The parameters of the model are estimated using maximum-likelihood estimation (MLE) and the magnetic sensor built into modern smartphones. The proposed method compensates the power absorption and the influence of multipath fading. According to experimental evaluations, the root-mean-square error (RMSE) of the proposed method is 34% lower than that of the conventional one. Namely, RMSE of the proposed method is 1.94m in a room.

For estimating user's location, Global Navigation Satellite System (GNSS) is very useful. Especially, Global Positioning System (GPS) by USA is very popular. A GPS receiver needs multiple satellites (usually 4 and more satellites). Propagation to the satellites needs line-of-sight. However, in urban area, there are many buildings. Received signals tend to become bad quality. Such signals are often called as non-line-of-sight (NLOS) or multipath signals. The problem is that the receiver cannot get line-of-sight signals from adequate number of the satellites coinstantaneously. This case leads to degradation of estimation quality or impossibility of estimation. In this paper, we will introduce a novel estimation algorithm, which can estimate own position with as low number of satellites as possible. The proposal achieves the estimation by only two satellites. The proposal also uses a traveling distance sensor which is often equipped on vehicles. By recorded satellite data, we will confirm our effectiveness.

Pedestrian dead reckoning (PDR) is an effective positioning means that can be used in urban-canyon environments where the accuracy of GPS is significantly degraded. Magnetic disturbances caused by artificial objects affect the accuracy of positioning if the PDR system uses a magnetometer to estimate the heading direction. Conventional PDR systems consider magnetic disturbances as unpredictable error sources, but the error becomes predictable and removable if the amount of the deviation in the magnetic field can be calculated at any position. In this study, we propose a method to correct the heading direction by referring to a map of magnetic deviation. The experimental results show that our method reduced the error in the heading direction caused by magnetic disturbances. Our approach removed the error components that differ depending on the position, and consequently, the resultant trajectory represented better the shape of the true trajectory.

An accurate time-of-arrival (TOA) estimation method for isolated pulses positioning system is proposed in this paper. The method is based on a multi-level crossing timing (MCT) digitizer and least square (LS) criterion, namely LS-MCT method, in which TOA of the received signal is directly described as a parameterized combination of a set of MCT samples of the leading and trailing edges of the signal. The LS-MCT method performs a receiver training process, in which a GPS synchronized training pulse generator (TPG) is used to obtain training data and determine the parameters of the TOA combination. The LS method is then used to optimize the combination parameters with a minimization criterion. The proposed method is compared to the conventional TOA estimation methods such as leading edge level crossing discriminator (LCD), adaptive thresholding (ATH), and signal peak detection (PD) methods. Simulation results show that the proposed algorithm leads to lower sensitivity to signal-to-noise ratio (SNR) and attains better TOA estimation accuracy than available TOA methods.