To launch profitable commercial LBS the LBS value chain is awaiting the commercial availability of precise location technologies, that coupled with devices capable of displaying maps in color, can deliver
real value to the end user. Currently the CDMA world, with commercially available A-GPS technologies is facing a unique window of opportunity, while the GSM/GPRS world is awaiting to advance from its present Cell/sector ID precision level. Shared utilities to manage privacy, personalization, GIS and billing are key to profitable deployment of LBS.
Location-based services have been standardized for incorporation into 3rd generation wireless communications as a result of the Federal Communications Commission’s (FCC) mandate on wireless carriers to provide automatic location information (ALI) during emergency911 calls. This mandate has driven the wireless carriers to explore
terrestrial, satellite, and hybrid based location technology solutions. This paper presents a communications model that investigates the position accuracyof a Global Standard Mobile (GSM) phone employing the enhanced observed time difference (EOTD) location technology. The EOTD positioning technique requires the mobile
station (MS) to detect signals from at least three base stations (BS). This studyassumes the three BSs are synchronized in time. For a given BS geometry with respect to the MS, a Monte Carlo simulation was performed to assess the two-dimensional position accuracyof the MS in Rayleigh and Ricean fading channels. In each channel, a Monte Carlo simulation was performed for a good and a bad BS-to-MS geometry. The paper concludes with a list of pros/cons of implementing EOTD as a location technologyenabler in telematics applications.
Finding the position of a mobile user has become important for many wireless applications. There are many methods, which fulfill this task but require extensions of the network or the terminals. The technique presented here is based on the received signal powers obtained from standard measurements. It uses a restricted search area and a time series of measurements. The search area is determined by the intersection of the maximum area covered by the serving cell and a ring determined from round-trip time measurements. A Viterbi-like algorithm is used to compare the time series of reception power levels measured by the handset with predicted values from the network planning process. It returns a weighted average of possible positions. Since only standard measurements are used, no changes of the network or the handsets are required, making the method inexpensive. Furthermore, the method can be used for traffic localization. The method has actually been implemented by Siemens for GSM. Extensive field trials in an operating network in Germany showed an accuracy of 130 meters in urban environments and about 300 meters in suburban and rural areas. Since similar measurements exist in UMTS networks, the method is applicable in UTRAN as well.
In wireless location, TOA and AOA observations are sometimes corrupted by Non-Line-of-Sight errors due to multipath propagation, which results in poor location accuracy. In this paper, a channel estimation based wireless location solution, is proposed. It can mitigate NLOS errors contained in TOA and AOA observations and improve the final location accuracy by extracting the earliest multipath signal.
Angles of arrival (AOAs) of a signal transmitted by a mobile station (MS) are estimated at two or more base stations (BSs) by employing directive antennas or antenna arrays. In traditional location algorithms, the MS position is determined by solving for the intersecting points of at least two lines of position given by these angles and the known positions of the BSs or using a Taylor series (TS) based algorithm to get a least squares (LS) solution. Obstruction of the direct path leading to non-line-of-sight (NLOS) propagation and the presence of multipaths due to scattering objects near and around the MS and BSs lead to errors in the measured AOAs
that cause these algorithms to perform poorly. In this paper, we propose an algorithm that makes use of AOA measurements at only 3 BSs including the serving BS. The algorithm mitigates the angular error by computing normalized scale factors or weights that adjust the corrupted angle measurements to near their true values. Utilizing the constraints imposed by the geometry of the cell layout and bounds obtained using the multipath angles, the scale factor estimation is formulated as a constrained optimization problem. Bounds on the scale
factors are obtained by making use of the known maximum angular spread at the NLOS BSs and the objective function to be minimized is the angle error norm at the serving BS. Our proposed algorithm has the advantage of not being limited to any particular network and can be adopted universally. Simulations show that the proposed
algorithm performs significantly better than the traditional algorithms especially when multipath information is incorporated.
Within the existing GSM standard, several measurements are available that can be used for estimating the position of a cellular phone. First, the timing advance (TA) gives an estimate for the distance to the serving base station. Second, the signal strengths (RXLEV) of neighbouring base stations can also be interpreted as distance information. Both TA and RXLEV are subject to measurement errors caused for example by shadowing, reflections, and fast fading. Thus, a nonlinear set-theoretic estimation technique based on pseudo ellipsoids is applied. The uncertainty regions in the original space defined by the measurements are transformed into a hyperspace of higher dimension and described by pseudo ellipsoids. An approximation of the set intersection of the pseudo ellipsoids can be calculated recursively by a linear set-theoretic filter. The resulting pseudo ellipsoid is transformed back into the original space, and the position estimate is calculated as center of gravity of the resulting uncertainty region. The algorithm is evaluated based on the data of an extensive field trial in a rural area. Compared to pure cell ID, the accuracy is significantly increased by using TA and RXLEV, reducing the mean error by half.
This paper describes an enhancement of the Enhanced-Observed Time-Difference (E-OTD) method of locating mobile phones in unsynchronized networks (e.g. GSM) which does away with the need to deploy additional network monitoring equipment. It is therefore a method of locating mobile telephones that does not require an external source of synchronization. The timing measurements from many mobile phones made within a few minutes of each other are combined to estimate simultaneously both the time offsets between the base stations and the positions of all the mobile phones contributing measurements. Experimental and theoretical results show that the method performs as well as standard E-OTD, but without the need for Location Measurement Units (LMUs).
In this paper, nonlinear Bayesian filtering techniques are applied to the localization of mobile radio communication devices. The application of this approach is demonstrated for the localization of DECT mobile telephones in a scenario with several base stations and a mobile handset. The received signal power, measured by the mobile
handsets, is related to their position by nonlinear measurement equations. These consist of a deterministic part, modeling the received signal power as a function of the position, and a stochastic part, describing model errors and measurement noise. Additionally, user models are considered, which express knowledge about the motion
of the user of the handset. The new Prior Density Splitting Mixture Estimator (PDSME), a Gaussian mixture filtering algorithm, significantly improves the localization quality compared to standard filtering techniques as the Extended Kalman Filter (EKF).
This paper proposes a fusion framework to locate trains travelling on track routes. The input data are gained from two independent sensor devices, namely, a Global Navigation Satellite System and an eddy current sensor device. The sensor data are fused by an Extended Kalman Filter gaining precise information about the train location. This positioning system combines the sensor devices with the fusion approach to perform a robust location even in the case of noise or when a sensor fault occurs. Additionally, some future fusion strategies to extend the existing location system are presented.
The Preprocessing GPS - SMS Communication Unit (PCU) is a mobile tracking device used within AVL tracking systems for determining the location of vehicles. It was designed primarily to utilize the SMS service of the GSM network for communicating. The use of SMS messages is part of an effort aimed at providing a cost effective alternative for tracking the location of vehicles.
Its primary function is to send information about user location across a GSM network to a Central Base Station (CBS) from which assets are being tracked. Though SMS is the main bearer, the unit is also capable of using Circuit Switch Data Service (CSD) to send and receive data from the Base Station (BS).
The PCU was developed as a small hardware unit based on the Microchip microcontroller, with a multiplexer switching two RS 232 serial inputs. One input is dedicated to the GPS receiver and the second one to the wireless modem.
During a pre-programmed course to a particular destination, an autonomous vehicle may potentially encounter environments that are unknown at the time of operation. Some regions may contain objects or vehicles that were not anticipated during the mission-planning phase. Often user-intervention is not possible or desirable under these circumstances. Thus it is required for the onboard navigation system to automatically make short-term adjustments to the flight plan and to apply the necessary course corrections. A suitable path is visually navigated through the environment to reliably avoid obstacles without significant deviations from the original course.
This paper describes a general low-cost stereo-vision sensor framework, for passively estimating the range-map between a forward-looking autonomous vehicle and its environment. Typical vehicles may be either unmanned ground or airborne vehicles. The range-map image describes a relative distance from the vehicle to the observed environment and contains information that could be used to compute a navigable flight plan, and also visual and geometric detail about the environment for other onboard processes or future missions.
Aspects relating to information flow through the framework are discussed, along with issues such as robustness, implementation and other advantages and disadvantages of the framework. An outline of the physical structure of the system is presented and an overview of the algorithms and applications of the framework are given.
Scene navigability is a character of local scene area, and the character embodies the ability of position-correction of some scene area for navigation system. Such ability provides position-correction for Inertia Navigation System of aircraft, which adopts Digit Scene Matching Area Correlator(DSMAC) techniques.
When analyzing the navigability of scene matching system for some scene area, the information content of scene is the most important factor. Generally speaking, the scene navigability can be determined by acquisition probability or matching fix precision of the navigation system.
The paper broadens the hypothesis of the reference map and additional noise. The preprocessed reference map is supposed as discrete fractional Brownian random field with zero mean, and additional noise is supposed as independent random field with zero mean.
The optimal expression of the measurement of DSMAC is deduced. Analyzing fractal and statistics characteristic of reference image derives an estimation model of acquisition probability on Cross Correlation Scene Matching Systems.
Many experiments with satellite images demonstrate the validity of this proposed acquisition probability model. The relation between acquisition probability and fractal feature is set up, and it provides a theoretical and practical basis for design of self-guided systems using scene matching.
Time-transfer is the process of using a GPS receiver to derive accurate time relative to GPS system time. The GPS receiver clock error establishes the relationship between the receiver’s time and the GPS system time. The main problem here is to estimate the desired range equivalent of receiver clock error. Kalman filter is one of the methods used to estimate the various error components separately using observations made over time. This paper proposes the use of neural networks for the estimation of these error components based on a revised time-transfer error model.
Ranger is a local area radio frequency ranging system implemented in the 2.4 GHz Industrial, Scientific andMedical band. Roundtrip time-of-flight measurements are made in a two-stage process: a coarse measurement provides ± 3.4 m accuracy, followed by a fine measurement correction that reduces error to below 20 cm. This
innovative approach can be implemented in other parts of the radio frequency spectrum; the 2.4 GHz band was chosen both for regulatory acceptance and availability of commercial components.
The Ranger system consists of fixed and mobile radios. Fixed location radios provide the reference from which distances are measured to the mobile radio. A single distance measurement requires approximately 50
ms, therefore with a network of four fixed radios, the position of the mobile can be updated five times per second. Additionally, the digital communications link that is used for distance measurement is also used for concurrent high-bandwidth data communication.
This paper describes the application of the Extended Kalman Filter to the estimation of range and bearing biases in marine radars using data from hydrographic charts and radar scans. By defining at least two correspondence points from radar video and electronic charts, the technique provides a rapid and accurate calibration in range and bearing, giving also estimates for the own ship speed, heading and geographical position (latitude and longitude). The method is tested with real data from radar scan images and electronic charts displayed on a navigation console installed on a patrol boat. The technique does not require GPS nor speed information from the ship log unit, however it is shown that their inclusion can improve the estimation.