Current Issue : July - September Volume : 2012 Issue Number : 3 Articles : 5 Articles
Multipath remains a dominant source of ranging errors in Global Navigation Satellite Systems (GNSS), such as the Global\r\nPositioning System (GPS) or the future European satellite navigation system Galileo.Multipath is generally considered undesirable\r\nin the context of GNSS, since the reception of multipath can make significant distortion to the shape of the correlation function\r\nused for time delay estimation. However, some wireless communications techniques exploit multipath in order to provide signal\r\ndiversity though in GNSS, the major challenge is to effectively mitigate the multipath, since we are interested only in the satellitereceiver\r\ntransit time offset of the Line-Of-Sight (LOS) signal for the receiver�s position estimate. Therefore, the multipath problem\r\nhas been approached from several directions in order to mitigate the impact of multipath on navigation receivers, including the\r\ndevelopment of novel signal processing techniques. In this paper, we propose a maximum likelihood-based technique, namely,\r\nthe Reduced Search Space Maximum Likelihood (RSSML) delay estimator, which is capable of mitigating the multipath effects\r\nreasonably well at the expense of increased complexity. The proposed RSSML attempts to compensate the multipath error\r\ncontribution by performing a nonlinear curve fit on the input correlation function, which finds a perfect match from a set of ideal\r\nreference correlation functions with certain amplitude(s), phase(s), and delay(s) of the multipath signal. It also incorporates a\r\nthreshold-based peak detection method, which eventually reduces the code-delay search space significantly. However, the downfall\r\nof RSSML is the memory requirement which it uses to store the reference correlation functions. The multipath performance of\r\nother delay-tracking methods previously studied for Binary Phase Shift Keying-(BPSK-) and Sine BinaryOffset Carrier- (SinBOC-)\r\nmodulated signals is also analyzed in closed loop model with the new Composite BOC (CBOC) modulation chosen for Galileo E1\r\nsignal. The simulation results show that the RSSML achieves the bestmultipath mitigation performance in a uniformly distributed\r\ntwo-to-four paths Rayleigh fading channel model for all three modulated signals....
The aim of this study is to analyze the urban land use changes occurred in the central part of Ulaanbaatar, the capital city of\r\nMongolia, from 1930 to 2008 with a 10-year interval using geographical information system (GIS) and very high-resolution remote\r\nsensing (RS) data sets. As data sources, a large-scale topographic map, panchromatic and multispectral Quickbird images, and\r\nTerraSAR synthetic aperture radar (SAR) data are used. The primary urban land use database is developed using the topographic\r\nmap of the study area and historical data about buildings. To extract updated land use information from the RS images, Quickbird\r\nand TerraSAR images are fused. For the fusion, ordinary and special image fusion techniques are used and the results are compared.\r\nFor the final land use change analysis and RS image processing, ArcGIS and Erdas imagine systems installed in a PC environment\r\nare used. Overall, the study demonstrates that within the last few decades the central part of Ulaanbaatar city is urbanized very\r\nrapidly and became very dense....
Satellite navigation technology is becoming essential for civil application. The high-accuracy navigation service is demanded.\r\nHowever, the satellite signal may be exposed to the signal from other systems, which are sharing the same frequency band. This is\r\na potential threat for the performance of navigation devices. The aim of this paper is to present an interference impact assessment\r\nin the context of global navigation based on the new modulation Composite Binary Offset Carrier (CBOC) that will be used\r\nfor Galileo E1 civil signal. The focus is on the analysis of the Galileo CBOC-modulated signal robustness against narrowband\r\ninterference....
The Global Positioning System (GPS) has become one of the state-of-the-art location systems that offers reliable mobile terminal\r\n(MT) location estimates. However, there exist situations where GPS is not available, for example, when the MT is used indoors\r\nor when the MT is located close to high buildings. In these scenarios, a promising approach is to combine the GPS-measured\r\nvalues with measured values from the Global System for Mobile Communication (GSM), which is known as hybrid localization\r\nmethod. In this paper, three nonlinear filters, namely, an extended Kalman filter, a Rao-Blackwellized unscented Kalman filter,\r\nand a modified version of the recently proposed cubature Kalman filter, are proposed that combine pseudoranges from GPS with\r\ntiming advance and received signal strengths from GSM. The three filters are compared with each other in terms of performance\r\nand computational complexity. Posterior Cram�´er-Rao lower bounds are evaluated in order to assess the theoretical performance.\r\nFurthermore, it is investigated how additional GPS reference time information available from GSM influences the performance\r\nof the hybrid localization method. Simulation and experimental results show that the proposed hybrid method outperforms the\r\nGSM method....
Present land vehicle navigation relies mostly on the Global Positioning System (GPS) that may be interrupted or deteriorated in\r\nurban areas. In order to obtain continuous positioning services in all environments, GPS can be integrated with inertial sensors and\r\nvehicle odometer using Kalman filtering (KF). For car navigation, low-cost positioning solutions based on MEMS-based inertial\r\nsensors are utilized. To further reduce the cost, a reduced inertial sensor system (RISS) consisting of only one gyroscope and speed\r\nmeasurement (obtained from the car odometer) is integrated with GPS. The MEMS-based gyroscope measurement deteriorates\r\nover time due to different errors like the bias drift. These errors may lead to large azimuth errors and mitigating the azimuth errors\r\nrequires robust modeling of both linear and nonlinear effects. Therefore, this paper presents a solution based on Parallel Cascade\r\nIdentification (PCI) module that models the azimuth errors and is augmented to KF. The proposed augmented KF-PCI method\r\ncan handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear and\r\nresidual parts of the azimuth errors are modeled by PCI. The performance of this method is examined using road test experiments\r\nin a land vehicle....
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