FILTERING TECHNIQUES APPLIED ON RAW CARRIER PHASE FOR GPS DETECTING SMALL DYNAMIC DISPLACEMENTS

This work is part of a research under construction since 2000, in which the main objective is to measure small dynamic displacements by using L1 GPS receivers. A very sensible way to detect millimetric periodic displacements is based on the Phase Residual Method (PRM). This method is based on the frequency domain analysis of the phase residuals resulted from the L1 double difference static data processing of two satellites in almost orthogonal elevation angle. In this article, it is proposed to obtain the phase residuals directly from the raw phase observable collected in a short baseline during a limited time span, in lieu of obtaining the residual data file from regular GPS processing programs which not always allow the choice of the aimed satellites. In order to improve the ability to detect millimetric oscillations, two filtering techniques are introduced. One is auto-correlation which reduces the phase noise with random time behavior. The other is the running mean to separate low frequency from the high frequency phase sources. Two trials have been carried out to verify the proposed method and filtering techniques. One simulates a 2.5 millimeter vertical antenna displacement and the second uses the GPS data collected during a bridge load test. The results have shown a good consistency to detect millimetric oscillations.

Measuring Dynamic Oscillations of a Small Span Cable-Stayed Footbridge: Case Study Using L1 GPS Receivers

Highly redundant or statically undetermined structures, such as a cable-stayed bridge, have been of particular concern to the engineering community nowadays because of the complex parameters that must be taken into account for healthy monitoring. The purpose of this study was to verify the reliability and practicability of using GPS to characterize dynamic oscillations of small span bridges. The test was carried out on a cable-stayed wood footbridge at Escola de Engenharia de Sao Carlos-Universidade de Sao Paulo, Brazil. Initially a static load trial was carried out to get an idea of the deck amplitude and oscillation frequency. After that, a calibration trial was carried out by applying a well known oscillation on the rover antenna to check the environment detectable limits for the method used. Finally, a dynamic load trial was carried out by using GPS and a displacement transducer to measure the deck oscillation. The displacement transducer was used just to confirm the results obtained by the GPS. The results have shown that the frequencies and amplitude displacements obtained by the GPS are in good agreement with the displacement transducer responses. GPS can be used as a reliable tool to characterize the dynamic behavior of large structures such as cable-stayed footbridges undergoing dynamic loads.

A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms

In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen`s Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology. (C) 2010 Elsevier B.V. All rights reserved.