Rafael Saraiva Campos

A Fast Database Correlation Algorithm for Localization of Wireless Network Mobile Nodes using Coverage Prediction and Round Trip Delay

In this work a fast database correlation algorithm is proposed for the localization of mobile stations in wireless metropolitan area networks. The algorithm is entirely based on built-in functionalities of mobile nodes. The set of measured parameters used for localization is called radio-frequency fingerprint. To provide a position estimate, it is correlated with a database built from field measurements or propagation modeling. The latter alternative was selected, as it allows a less expensive and faster database update. A novel use of the round trip delay to reduce the correlation space and improve positioning accuracy is presented. Vehicular field tests and Monte Carlo simulations were conducted to evaluate the algorithm’s accuracy in GSM and WCDMA cellular networks, respectively.

Location methods for legacy GSM handsets using coverage prediction

In this work several methods for the location of legacy GSM handsets are compared. These methods use data available in the mobile station measurement report - communication channel server identity, received power levels from the serving and neighbor cells, received signal quality - and time alignment with the server. It is discussed how these parameters can be used for mobile station positioning. A new approach for the cell identity plus time alignment method, using propagation modeling, is proposed. The efficiency of radio-frequency fingerprint correlation using coverage prediction maps of different resolutions is also analyzed. Field tests were conducted to evaluate the methods in real scenarios.

Search space reduction in DCM positioning using unsupervised clustering

Database correlation methods (DCM) are used to locate mobile stations (MSs) in wireless networks. A target radio-frequency (RF) fingerprint - measured by the target mobile station - is compared with georeferenced RF fingerprints, previously stored in a correlation database (CDB). In this paper, two unsupervised clustering techniques (K-medians and Kohonen Layer) were applied to reduce the search space inside the CDB. The clustering effects on the computational cost of the positioning method and on the positioning accuracy were experimentally evaluated using 46200 target fingerprints and a CDB with 924 reference fingerprints, containing Received Signal Strength (RSS) values of 136 WiFi 802.11b/g networks in a 12-floor building. A reduction of 81% in the average time to produce a position fix was observed, as well as a 38% decrease in the DCM average positioning error and a 6% improvement in the floor identification accuracy.

Finite element method framework for RF-based through-the-wall mapping

Radiofrequency (RF) Through-the-Wall Mapping (TWM) employs techniques originally applied in X-Ray Computerized Tomographic Imaging to map obstacles behind walls. It aims to provide valuable information for rescuing efforts in damaged buildings, as well as for military operations in urban scenarios. This work defines a Finite Element Method (FEM) based framework to allow fast and accurate simulations of the reconstruction of floors blueprints, using Ultra High-Frequency (UHF) signals at three different frequencies (500 MHz, 1 GHz and 2 GHz). To the best of our knowledge, this is the first use of FEM in a TWM scenario. This framework allows quick evaluation of different algorithms without the need to assemble a full test setup, which might not be available due to budgetary and time constraints. Using this, the present work evaluates a collection of reconstruction methods (Filtered Backprojection Reconstruction, Direct Fourier Reconstruction, Algebraic Reconstruction and Simultaneous Iterative Reconstruction) under a parallel-beam acquisition geometry for different spatial sampling rates, number of projections, antenna gains and operational frequencies. The use of multiple frequencies assesses the trade-off between higher resolution at shorter wavelengths and lower through-the-wall penetration. Considering all the drawbacks associated with such a complex problem, a robust and reliable computational setup based on a flexible method such as FEM can be very useful.

A priori selection of high accuracy mobile station position estimates

A priori identification and selection of high accuracy position estimates, i.e., with error below 100 meters, is particularly relevant for critical location-based applications, like vehicle tracking and, specially, emergency call positioning. This work presents a backpropagation artificial neural network classifier used to predict the accuracy of mobile station position estimates produced by a network-based radio-frequency fingerprinting method, RF-FING+RTD-PRED (Predicted Radio-frequency Fingerprint with Round Trip Delay), previously formulated by the authors. The classifier employs the same radio-frequency parameters used by the aforementioned method plus some additional network data. In field tests carried out in GSM (Global System for Mobile Communications) networks in urban and suburban areas, where 6600 measurement reports have been collected, a 89% precision in the identification of high accuracy position estimates has been achieved. The presented method is promptly extensible to 3G cellular networks.

Genetic algorithm optimized DCM positioning

Database Correlation Methods estimate the mobile station location by comparing a measured radio frequency fingerprint with a set of previously collected or generated reference fingerprints. This set is referred to as the search or correlation space. Genetic algorithms can be used to optimize both the location accuracy and the time required to produce a position fix, reducing the size of the search space. This paper proposes an innovation in such application of genetic algorithms, restricting the first generation population to the predicted best server area of the serving sector measured by the mobile station. In field tests in a GSM cellular network in a dense urban environment, this approach achieved reductions of 20% and 15% in the 50-th and 90-th percentile location errors, respectively, in comparison to the original formulation, where the initial population is randomly distributed throughout the whole service area. An average reduction of 91% in the time to produce a position fix was also observed.