Emitter Location Finding using Particle Swarm Optimization

Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error

Mixing and combining with AOA and TOA for the enhanced accuracy of mobile location

[[abstract]]Position location has become a hot issue over the past few years in wireless communication. Providing the accurate location information of the mobile station (MS) is necessitated by the Emergent 911 call in United States. The angle of arrival (AOA), time of arrival (TOA), and time difference of arrival (TDOA) techniques have been proposed for providing location services in wireless networks. We present a method for the enhanced accuracy of mobile location. This method is mixing and combining with AOA and TOA in wireless networks and picks out mobile location with large deviation to enhance the accuracy of location estimation. Numerical results demonstrate that the proposed location scheme gives much higher location accuracy than the method that only used TOA and AOA location technique.[[notice]]需補地點及國別[[conferencetype]]國際[[conferencedate]]20030422~2003042

Advanced array processing techniques and systems

Research and development on smart antennas, which are recognized as a promising technique to improve the performance of mobile communications, have been extensive in the recent years. Smart antennas combine multiple antenna elements with a signal processing capability in both space and time to optimize its radiation and reception pattern automatically in response to the signal environment. This paper concentrates on the signal processing aspects of smart antenna systems. Smart antennas are often classified as either switched-beam or adaptive-array systems, for which a variety of algorithms have been developed to enhance the signal of interest and reject the interference. The antenna systems need to differentiate the desired signal from the interference, and normally requires either a priori knowledge or the signal direction to achieve its goal. There exists a variety of methods for direction of arrival (DOA) estimation with conflicting demands of accuracy and computation. Similarly, there are many algorithms to compute array weights to direct the maximum radiation of the array pattern toward the signal and place nulls toward the interference, each with its convergence property and computational complexity. This paper discusses some of the typical algorithms for DOA estimation and beamforming. The concept and details of each algorithm are provided. Smart antennas can significantly help in improving the performance of communication systems by increasing channel capacity and spectrum efficiency, extending range coverage, multiplexing channels with spatial division multiple access (SDMA), and compensating electronically for aperture distortion. They also reduce delay spread, multipath fading, co-channel interference, system complexity, bit error rates, and outage probability. In addition, smart antennas can locate mobile units or assist the location determination through DOA and range estimation. This capability can support and benefit many location-based services including emergency assistance, tracking services, safety services, billing services, and information services such as navigation, weather, traffic, and directory assistance

Data fusion for ground target tracking in GSM networks

Positioning in mobile cellular networks is an exciting research area. The Global System for Mobile communications (GSM) network, as a widely used mobile communication standard around the world, has shown the potential to provide position information. Ground target tracking is a significant application of finding the position of a mobile station (MS). However, a GSM positioning system based on current specifications faces many difficulties to yield an accurate position estimate. Since the signals are designed by communication needs rather than positioning, the resolution of the measurements in GSM networks for positioning is coarse. The ambiguities of the position estimate arise when there are not a sufficient number of measurements available. Moreover, due to the restriction of terrain, road and traffic, the ground target often maneuvers. Therefore, data fusion approaches, which integrate redundant information from different sources, are applied in this work to obtain improved position estimation accuracy. This work focuses on the state estimation problem of the MS\u27s position given the measurements from the GSM networks and a priori road information. A data fusion solution, which integrates time of arrival (TOA) and received signal strength (RSS) measurements using an extended Kalman filter (EKF), is proposed to provide an improved position estimate. The theoretical best achievable performance, posterior Cramer-Rao lower bound (PCRLB), is derived for the data fusion approach. The PCRLB is used to demonstrate the benefits of the fusion approach and applied as a benchmark to compare different estimators. The road constraint is incorporated into the estimation process as a pseudomeasurement. Simulations of the linear and nonlinear road segments prove the advantages of the road-constrained approach. Moreover, the motion mode uncertainty problem is considered and solved by a multiple model (MM) approach. In particular, an adaptive road-constrained interacting MM (ARC-IMM) estimator, which incorporates the road information into a variable structure MM mechanism, is proposed and demonstrated to be effective and robust to provide a significantly improved position estimate.Die Ortung in Mobilfunknetzen ist ein faszinierendes Forschungsgebiet. Der in großem Umfang genutzte Mobilfunkstandard für digitale Netze Global System for Mobile communications (GSM) kann auch zur Positionsbestimmung erfolgreich eingesetzt werden. Eine der bedeutenden Anwendungen bezüglich der Ortung von Mobilstationen (MS), d.h. von Mobilfunkend-geräten, ist das sogenannte Ground-Target-Tracking, also die Zielverfolgung derselben. Im Falle eines GSM-basierten Ortungssystems, das auf den aktuellen GSM-Spezifikationen basiert, müssen viele Schwierigkeiten überwunden werden, um die Position genau schätzen zu können. Zum einen liegen - da die Signale im Wesentlichen unter Berücksichtigung der Anforderungen hinsichtlich der Kommunikation (und nicht der Ortung) entworfen wurden - die Ergebnisse der Ortung in GSM-Netzwerken nur in einer groben Auflösung vor, und im Falle einer nicht ausreichend hohen Anzahl von verfügbaren Messwerten treten Mehrdeutigkeiten bei der Positionsschätzung auf. Zum anderen führt das Ziel entsprechend dem Gelände, dem Straßenverlauf und dem Verkehr oft Bewegungsänderungen durch. In dieser Arbeit werden deshalb Datenfusionsansätze verfolgt, die redundante Messwerte aus verschiedenen Quellen berücksichtigen, um eine verbesserte Genauigkeit der Positionsschätzung zu erzielen. Im Mittelpunkt der Arbeit steht die Zustandsschätzung unter Berücksichtigung der Messwerte aus dem GSM-Netzwerk und von a priori Information zum Straßenverlauf. Es wird ein Datenfusionsansatz eingeführt, mit dem die Fusion der Messwerte aus den Verfahren Time-of-Arrival (TOA) und Received-Signal-Strength (RSS) möglich wird, um einen verbesserten Positionsschätzwert zu erhalten. Es wird dabei ein Extended-Kalman-Filter (EKF) eingesetzt. Die theoretisch beste erzielbare Genauigkeit mit dem Datenfusionsansatz wird in Form der posterior Cramér-Rao lower bound (PCRLB) abgeleitet. Die PCRLB wird herangezogen um die Vorteile des Datenfusionsansatzes zu zeigen und dient als Benchmark für den Vergleich verschiedener Verfahren. Die Information über den Straßenverlauf wird in den Schätzprozess in Form einer Pseudomessung integriert. Simulationen sowohl in linearen als auch in nichtlinearen Fällen zeigen die Vorteile dieses Ansatzes, der die Randbedingungen durch den Straßenverlauf einbezieht. Weiterhin wird das Problem der Unsicherheit bei der Auswahl der Bewegungsart im Multiple-Model (MM) - Ansatz betrachtet und gelöst. Insbesondere wird ein sogenannter Adaptive-Road-Constraint-Interacting-Multiple-Model (ARC-IMM) - Schätzer, der die Straßen-information in einen MM-Ansatz mit variabler Struktur integriert, vorgeschlagen. Es wird gezeigt, dass dieser Schätzer effizient und robust ist, und eine wesentlich verbesserte Positionsschätzung liefert