A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> Fundamentals of Satellite Navigation. GPS Systems Segments. GPS Satellite Signal Characteristics and Message Formats. Satellite Signal Acquisitions and Tracking. Effects of RF Interference on GPS Satellite Signal Receiver Tracking. Performance of Standalone GPS. Differential GPS. Integration of GPS with other Sensors. Galileo. The Russian GLONASS, Chinese Bediou, and Japanese QZSS Systems. GNSS Markets and Applications. <s> BIB001 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> From the Publisher: ::: "Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics." "The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems." "Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area."--BOOK JACKET. <s> BIB002 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> In this paper, techniques and algorithms developed in the framework of Statistical Learning Theory are applied to the problem of determining the location of a wireless device by measuring the signal strength values from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no special-purpose hardware is required.The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in scientific literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques. <s> BIB003 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> This work presents a location determination technique which is based on wireless local area networks (WLANs), in particular the IEEE 802.11b standard, in conjunction with GPS (Global positioning system). Since each of the positioning devices has its shortcomings, this paper proposes to use the data obtained from the GPS system together with the WLAN positioning data to estimate the mobile user's position in a network nearest to its true position. Some of the theoretical work that substantiate the proposed method and initial simulation results are shown as well. <s> BIB004 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> In this work we quantify the usefulness of particle filters applied to the problem of mobile device tracking in Wi-Fi networks, under the assumption of log-normal fading. Our principal aim was to determine if a real-time deployment of a particle filter was possible while still providing factor two gains in the prediction performance relative to a stand-alone optimal Wi-Fi positioning algorithm. We conclude that the required gains are achieved in our adopted filter algorithm when the particle number is set to the relatively small number of 300, meaning that a real-time deployment is possible. In addition, we quantify the performance gain of the particle filter when intermittent GPS information is available to the mobile device. We propose the fusion of the GPS information be implemented as a renormalization of the particle cloud. Finally, we probe the limits of the filter performance under biased-error distributions. Our simulations show that tracking of people, vehicles and robotic devices in an outdoor Wi-Fi network, where non-linear and non-Gaussian conditions exist, can be significantly enhanced by the pragmatic real-time particle filter algorithm presented here <s> BIB005 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> Location tracking in wireless networks has many applications, including enhanced network performance. In this work we investigate the experimental use of "particle filter" techniques as applied to the mobile device tracking problem in WiFi networks. Particle filters are well suited to the non-Gaussian and biased error conditions characteristic of signals found in most WiFi networks, as well as allowing for seamless data fusion with intermittent GPS availability and prior map information. Our experimental results show that particle filter tracking can indeed deliver significant performance gains. In some physical regions of our test area, factors of three improvement in location accuracy - relative to optimal unfiltered WiFi positioning - is found. We show how these gains are achieved at a relatively small particle number of 300, meaning real-time implementation of the particle filter can be easily achieved. <s> BIB006 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> This paper presents a method to integrate Wi-Fi networks with Global Positioning System (GPS) and Dead Reckoning (DR) system for tracking a vehicle in the urban areas by using the federated form of Unscented Kalman filer (UKF) and Kalman filer (KF). Due to non-line-of-sight conditions and multipath propagation environments, the estimation of the GPS/DR integrated positioning system lacks acceptable accuracy for demanding applications, such as continuous vehicle tracking. Comparing with GPS, a fingerprinting positioning method based on Wi-Fi signal strength observations could continuously log location information without visibility to the sky. Numerical results show the improvement of the algorithm proposed in the paper. <s> BIB007 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> GPS is now widely accepted as a reliable, available and accurate source of positioning, able to operate across the globe. However, it generally requires at least four satellites to be in view of the receiver, in order to resolve the three co-ordinates of the receiver, and the local receiver time offset. In indoor environments or urban canyons, this requirement can often not be achieved, as walls or tall buildings block the signals emitted by the satellites. Developing alternative positioning systems in such environments has attracted a lot of research in the past few years. In this context, Wi-Fi based positioning systems have emerged as being accurate enough for most users and most importantly because they don’t require any additional hardware or infrastructure. However, such systems fail to reach the same level of accuracy as GPS. The ultimate goal of ubiquitous positioning would be to combine these two technologies so that an accurate position can be delivered, even in poor GPS environments. This paper will examine a method to combine a Wi-Fi obtained position with the output of a two satellite positioning algorithm developed at the University of New South Wales. It will first explore the performances that can be expected from both systems, and then show that better results are obtained when combining both. Tests were conducted using data collected from the CBD area of Sydney and a rooftop mounted GPS antenna in UNSW. <s> BIB008 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> Global positioning systems, widely known as GPSs, provide timing, navigation, and positioning signals to military and civilian users. When a GPS receiver receives satellite signals, the distance between the satellite and the user is first calculated, and then the user's current position is located using the triangulation method. However, there is a possibility that atmospheric conditions, multipath fading, and shadowing effects could have an impact on the satellite signal. As wireless technology has progressed, different user locations and locating systems have been invented, such as the WiFi locating system, which mainly uses received signal strength indication (RSSI) to set up radio maps that provide locations, and RF models to estimate the locations of a moving user. The Zigbee locating system tracks the position of blind nodes, using RF signal strength calculation and known coordinates to estimate the distance between reference points. Using the triangulation method, Zigbee searches for blind node coordinates. This research proposes an adaptive-weighting locating mechanism within wireless heterogeneous networks, which enhances GPS's ability to receive signals in buildings and precision in estimating locations. <s> BIB009 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> This paper contains selected test results of an indoor positioning prototype system making use of GPS/Galileo signals on L1/E1 and L5/E5a in addition to WiFi data, ZigBee data and a MEMS-based dead reckoning algorithm called pedestrian navigation system (PNS). The system uses a partially coherent ultra-tightly coupled integration scheme. The GNSS signal processing (acquisition and tracking) supports coherent integration times up to several seconds, which reduces the squaring loss and mitigates multipath by the high Doppler selectivity of a few hertz. The system works in post processing and is intended to be an indoor analysis system allowing to get a better understanding of the indoor positioning problem and to act as a reference system. GNSS signal acquisition is demonstrated at ∼ 3 dBHz and tracking well below 0 dBHz. <s> BIB010 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> The goal of this paper is to present the architecture and first performance results of the DINGPOS platform. The DINGPOS platform (Demonstrator for INdoor GNSS POSitioning) is a project funded by the ESA that covers the design, development and integration of an experimental indoor positioning system based on the fusion of three different technologies: High Sensitivity GNSS (GPS and the future Galileo), MEMS-based Pedestrian Navigation System and WIFI. This paper introduces the different architectural trade-offs of the final platform and presents the first results of its performance and capabilities with real data. <s> BIB011 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> We describe a hybridized receiver architecture tightly coupled with different sensors, namely MEMS accelerometers and gyroscopes, Wi-Fi engine and a barometer, allowing significant increase of positioning performance for indoor GNSS signal reception. This is possible due to the increase of integration times, use of higher sensitivity GNSS receivers and by resorting to external aiding sensors tightly coupled with the receiver. Expected overall navigation performance increase is described and the benefits of using such external sensors are quantified. Indeed, hybridization of GNSS receivers with other sensors is expected to be a key technology that will enable new services and applications for the use of GNSS for navigation in indoor and densely urbanized environments. <s> BIB012 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> We present a hybrid indoor positioning solution combining angle-based localization, pedestrian dead reckoning, and map filtering. The angle-based localization system provides absolute location estimates, whereas pedestrian dead reckoning (PDR) results in accurate shape and length of the traversed route without absolute location and heading information. PDR movement and angle-based location estimates are fused together with a building vector map in a fusion filter, which in this study is implemented through a particle filter. When the fusion filter has been initialized with absolute position data and has converged, it can run also on PDR and map filtering, independently of the angle-based localization system, in areas that are not illuminated by multi-antenna arrays. In principle the same fusion design could accommodate location estimates also from other positioning sources, for example WiFi, or GPS in outdoor-to-indoor transitions. By combining information from various positioning modalities, different types of indoor environments can be provided with seamless positioning coverage with reasonable accuracy and infrastructural investments. <s> BIB013 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> This paper presents a measurement fusion approach for seamless outdoor/indoor positioning utilizing multiple sensor and network measurements in addition to computer-vision based aiding. A Kalman filter is implemented for fusing WLAN, Bluetooth, and high-sensitivity GPS positioning results with accelerometer based motion information as well as digital compass and camera derived direction. The fusion filter provides improved positioning accuracy to a single-technology system and offers higher positioning availability in time. In the experiment conducted inside the third floor of a typical office building, the proposed fused solution achieves horizontal accuracy of around 7 m with a 1-Hz update rate. Aiding the fusion filter with a heading rate measurement derived from visual processing improves the horizontal position accuracy further to around 6 m. <s> BIB014 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> It is well known that a global positioning system (GPS) receiver needs to ‘see’ at least four satellites to provide a three-dimensional fix solution. But in difficult environments such as an urban canyon, the number of ‘visible’ satellites is often not enough. Wireless fidelity (WiFi) signals have been utilised for positioning mainly based on the fingerprinting technology. However, the accuracy of WiFi positioning outdoors is from several tens of metres to more than one hundred metres. This study proposes a new methodology to integrate WiFi positioning technology and GPS to improve positioning accuracy in urban canyons. When only two GPS satellites are visible, the pseudorange observations can be used to generate a time difference of arrival (TDOA) measurement. The TDOA generates a hyperboloid surface which can be intersected with the surface of the Earth and shows the possible location of the user on that line of position. Integrating this method with the WiFi fingerprinting technology can increase the positioning performance significantly. The test results show that the positioning accuracy can be improved by more than 50% if the new method can be applied. <s> BIB015 </s> A survey of hybridisation methods of GNSS and wireless LAN based positioning system <s> Weighting of GNSS and WPS positions <s> This paper describes the development of a scooter supporting the mobility of older people. The scooter is equipped with a drive assistance system and a special scooter navigation system. The drive assistance system consists of a velocity controller, a steering controller, and a collision avoidance system. In this paper it is demonstrated how the challenging control and steering tasks are modified to increase safety for older people. A special scooter navigation system is presented, to support elderly people in navigating on a safe route through the city using sidewalks, pedestrian lights and crosswalks. For extended positioning requirements a hybrid positioning system was developed combining GPS, WLAN, and inertial sensor data. By combination of these technical improvements it is demonstrated how older people are able to preserve their self-determined and independent life. Usability research was done with focus groups in order to become familiar with global user demands and expectations towards a mobility assistance system. Results show that the system components are expected to assist the user in navigation, steering and speed control rather than to take complete control on the driving situation. <s> BIB016
As in the section before, the hybridisation methods presented here derive WPS and GNSS positions independently, but only one position is provided to the sys-tem. Features are combined instead that just a decision between two positions is made. The algorithms which will be described, weight positions derived from different sensors, to provide a common position, according to application specific criteria. In the data fusion process additional information is taken into account, to combine several positions to one, possibly more accurate and robust, position. To accomplish the fusion, some of the following approaches use either simple weighting strategies or adaptive filters. Data fusion methods based on adaptive filtering can usually mathematically described by the recursive Bayesian framework. This framework is a general, probabilistic description of a state estimator, providing an estimate of an internal state obtained from noisy measurements. It involves a process model, describing the physics of the system evolving in time, and an observation model, describing the measurement process. The Kalman filter (KF) was a long time the standard method to fuse different measurements, especially applied for target tracking. The KF assumes linear processes with Gaussian noise and hence is able to approximate the involved probability distributions with their first and second order moment. (The KF performs a linear combination (related to weighted mean), where the weights are reasoned from the measurement covariances.) The Kalman filter applies for linear, Gaussian models since under these conditions the distributions remain Gaussian. If these conditions are not met, modified versions of the Kalman filter apply. The extended Kalman filter (EKF) linearises about an estimate of the current first and second order moment, but is only valid for Gaussian noise. For highly nonlinear processes the unscented Kalman filter (UKF) is often the filter of choice. Instead of approximating the non-linearities, the UKF approximates the probability distributions of the Bayesian recursion with samples, which capture the first two moments of the distributions. This approximation is accurate up to the third moment for Gaussian distributions and accurate up to the second moment for non-Gaussian distributions. In a likewise fashion the cubature Kalman filter (CKF) approximates the probability distributions functions, but the sample points are chosen corresponding to a cubature rule. The CKF will not be further explained, since it found no application in the reviewed publications. A filter used widely in localisation and tracking applications is the particle filter (PF). The PF approximates the unknown probability distributions with a set of samples and associated weights, allowing the approximation of arbitrary probability distributions. Due to this compelling property, it is kind of all-purpose. This advantage comes on much higher computational costs compared with the KF variants. Algorithms based on rather simple weighting strategies will be presented first: In  , a weighting scheme was investigated combining outcomes of a WLAN and GNSS location fingerprinting algorithm. In the on-line phase GNSS positions (longitude, latitude) and WLAN RSS are stored in data bases. To increase robustness of the fingerprinting methods, for each GPS fingerprint, the GPS coordinates are recorded at three different weather conditions; for each WLAN fingerprint, the WLAN RSS are recorded in the four cardinal directions, which are then averaged. The position estimate for both sources is obtained by standard fingerprinting algorithm based on the Euclidean distance. The weighting of the two positions is achieved by the linear combination p = (1 − β)p WPS + βp GPS dependent on the weather conditions. The weight was found empirically: For sunny β = 1, when cloudy β = 0.9, and β = 0.7 for rainy conditions. To estimate the proper user position the algorithm needs the input from the user about the weather condition. These developments have been extended with a Zigbee position fingerprinting system and a different weighting algorithm BIB009 . In this approach, three data bases for three different positioning systems must be recorded in the off-line phase, and three different positions are estimated in the online phase. These three position estimates (if available) are finally linearly combined via p = αp WPS + βp GPS + γp Zigbee . The weights are derived according to α = 1/σ GPS 1/σ GPS +1/σ WPS +1/σ Zigbee as example for GPS. The standard deviations are deduced from sample statistics. β and γ are derived by exchanging the numerator for 1/σ WPS and 1/σ Zigbee , respectively. They further suggest to update the variances during the on-line phase to improve their method. The algorithm described in BIB004 , retrieves positions and additional information from a conventional GPS receiver and a WPS. Fingerprinting, with an algorithmic addition accounting for WLAN signal multipath effects, is applied to obtain a WPS position. Important to mention is that 3 different radio maps, one for indoor, one for outdoor and one for both (overall), are generated. This way undesired position jumps between indoor and outdoor can be avoided, if the presence of the user in the indoor or outdoor area is correctly detected. A linear weighting scheme is presented in that work, relying mainly on the availability of the GPS. The GPS and the WPS positions are combined by p = (1− β)p WPS + βp GPS . The parameter β is set according to the following cases: a) no satellite in view, β = 0, and a WPS position is derived from the indoor radio map; b) the number of satellites is between one and four, β = 0, and a WPS position from the overall radio map is derived; c) more than 4 satellites are available but one of the GNSS signals has very low signal to noise ratio, again β = 0, and a WPS position from the outdoor radio map is computed; d) number of satellites is larger 4 and all GPS signal to noise ratio conditions are fulfilled, the WPS position is derived from the outdoor radio map, and is linearly combined with the GPS position. In this case the geometrical dilution of precision (GDOP) decides if β equals 0.25, 0.5 or 1.0. Algorithms relying upon adaptive filtering will be presented in the following: A multisensor data fusion method integrating GPS and WPS by means of a Kalman filtering was proposed in BIB014 . The focus of this work lies on the integration of the multiple sensors, and in particular on camera image processing to stabilise the heading estimation. Besides inertial sensors, a compass and a camera are used for a dead reckoning (DR) BIB002 system, and Bluetooth, WLAN and GPS serve with an absolute location estimate to the system. The positions from the GPS and the WPS contribute to the final position estimate via the KF equations. Their weights are influenced by the predicted state and the measurement noise covariances trough the Kalman gain. [12] presents a position fusion method, based upon Kalman filtering and an interacting multiple model (IMM) estimator. The used positioning systems are GPS and awiloc ®5 . The IMM usually employs multiple Kalman filters with different process models, each accounting for a different motion the mobile device may perform. It combines the output of the filter adaptively by computing a weighted sum. The filters run in parallel and conditional probabilities are computed, which mix (weight) the outcomes of the two filters in an optimal manner. The weights are mode probabilities deduced from the fact how well fits the data propagated through the process models to the last measurements BIB002 . In BIB016 the IMM is used differently. Instead of a weighting according to the performed motion, the process model is the same for both filters and the IMM BIB002 Dead reckoning is about estimating the current location based on motions made since the last known position. The motion is in general approximated by velocity and course estimates. BIB003 A commercial WPS employing RSS fingerprinting [1]. algorithm is used to weight according to the quality of information (QoI) of the GNSS and WPS position estimates. In the case of GNSS this quality measure are reasoned from the GDOP and awiloc ® delivers its proprietary error measure. An approach to fuse GNSS and WPS incorporated into a DR system for vehicle localisation was proposed in BIB007 . The system design consists of a federated KF. The DR system, consisting of a gyroscope and an odometer, estimates within an UKF the state of the vehicle. To bound the error grow of the DR system, absolute positioning sources are loosely coupled with the DR system. In parallel to the UKF a KF is set up to fuse GPS and WPS positions. The integration of the UKF and KF outputs happens in a federated form. A master KF combines the states of both subfilters and feeds the final estimates back to the two subfilters. The WPS takes the role of the GPS in GPS denied environments. It helps to limit the error accumulation of the DR and provides an absolute initial position. As well    BIB010 BIB011 use a WPS to back up the GNSS component. In these publications, a high sensitivity GNSS receiver is investigated, to overcome the localisation problems in high rise urban areas and eventually indoors. Remind the CDMA scheme for GNSS signals, with signal power levels around the noise floor. Obstacles such as buildings and so forth impair this situation further, which then makes a signal acquisition and tracking with a conventional GNSS receiver unfeasible. To still acquire and track GNSS signals, the suggestions of the previously cited references, are to increase the integration time and to decrease the tracking bandwidth. On that account, nonetheless further sensors are necessary for a HS-GNSS receiver design. Inertial sensors are employed, to deliver information about the dynamics of the mobile device. In addition, inertial sensors provide opposed benefits and drawbacks compared to GNSS. When combining the two systems, the GNSS bounds the errors of the inertial navigation system (INS) and calibrates the inertial sensors; whereas the INS bridges GNSS outages and smooths the GNSS solution. The fusion architecture in    BIB010 BIB011 is a deeply integrated INS/GNSS with aid of further sensors. Since the application aims at mobile devices, only micro-electromechanical system (MEMS) inertial sensors are qualified. INS/GNSS yields a power gain, because not the whole absolute dynamic of the antenna (mobile device) must be tracked, but only the INS errors. Thus, a lower tracking bandwidth is required, which increases the noise resistance; due to enabling a longer integration time. The inertial sensors data helps during highly dynamic motion to keep the tracking loops in lock. Furthermore, a deep coupling enables the INS/GNSS for short periods to work with less than four satellites. The INS is vitally to a HS-GNSS, but without corrections from GNSS it can not support the system for a long amount of time (dependent on error growth). WPS comes into play when nevertheless the GNSS looses its position fix. A WPS position is then used to provide a position to limit the errors of the INS. Among other sensors, WPS is fused into the system as a KF position measurement. In the work of BIB012 as well a HS-GNSS receiver is developed, following a very similar strategy: a HS-GNSS receiver based on a INS/GNSS tight integration scheme with a WPS among other sensors. Again, the WPS supplies an independent, absolute position to the EKF, and in particular to the INS. Only with an additional position input the INS error can be kept in reasonable limits, during GNSS outages. For further details on HS-GNSS and INS/GNSS and the different integration algorithms, it is referred to BIB001 64] . Also a method using a particle filter to combine the two positioning systems has been published. The following two approaches BIB005 BIB006 describe primarily a WLAN RSS fingerprinting particle filter. Their contribution to data fusion of GNSS and WPS is to insert sporadic occurring GPS fixes in their positioning solution. In this approach the GPS position fixes are assumed to be of better accuracy than the WPS position estimates. When a GPS fix is available the particle cloud becomes re-centered at the GPS location and the weights become normalised. The already briefly mentioned hybrid indoor positioning approach proposed in BIB013 , makes use of a particle filter too. Even though GNSS was not used in their experiments, they already had in mind to accommodate the system for outdoors and therefore integrate a GNSS receiver. Their method integrates angle measurements from WLAN signals and pedestrian DR, by employing a particle filter and map filtering. A geometric approach, improving the WPS accuracy by solely two GNSS satellite signals, is proposed in BIB008 BIB015 . The key idea is to use the pseudo range difference of two satellites to eliminate the GNSS receiver clock error, and so to narrow down the possible area of the receiver location. The difference of two ranges give one hyperboloid surface, which intersects in a line with the earth's surface. One point of this line is the possible receiver location. To find the receiver location on that line a WPS position, or an other external position provider can be exploited. By simply deriving the shortest distance from the WPS position to the line of candidate positions, the receiver location can be obtained. Notice, pseudo ranges are observed, which contain several errors. Hence the intersection line is actual a intersection stripe, making the result less reliable. Relying upon lateration, the accuracy of the intersection line depends heavily on the satellite geometry. The larger the angle between the two satellites, the more accurate the intersection line of the TDOA hyperboloid and the spheroid approximating the earth.