Model-based Layer Estimation using a Hybrid Genetic/Gradient Search Optimization Algorithm

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A particle swarm optimization (PSO) algorithm is combined with a gradient search method in a model-based approach for extracting interface positions in a one-dimensional multilayer structure from acoustic or radar reflections. The basic approach is to predict the reflection measurement using a simulation of one-dimensional wave propagation in a multi-layer, evaluate the error between prediction and measurement, and then update the simulation parameters to minimize the error. Gradient search methods alone fail due to the number of local minima in the error surface close to the desired global minimum. The PSO approach avoids this problem by randomly sampling the region ... continued below

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7 p. (0.2 MB)

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Chambers, D; Lehman, S & Dowla, F May 17, 2007.

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A particle swarm optimization (PSO) algorithm is combined with a gradient search method in a model-based approach for extracting interface positions in a one-dimensional multilayer structure from acoustic or radar reflections. The basic approach is to predict the reflection measurement using a simulation of one-dimensional wave propagation in a multi-layer, evaluate the error between prediction and measurement, and then update the simulation parameters to minimize the error. Gradient search methods alone fail due to the number of local minima in the error surface close to the desired global minimum. The PSO approach avoids this problem by randomly sampling the region of the error surface around the global minimum, but at the cost of a large number of evaluations of the simulator. The hybrid approach uses the PSO at the beginning to locate the general area around the global minimum then switches to the gradient search method to zero in on it. Examples of the algorithm applied to the detection of interior walls of a building from reflected ultra-wideband radar signals are shown. Other possible applications are optical inspection of coatings and ultrasonic measurement of multilayer structures.

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7 p. (0.2 MB)

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PDF-file: 7 pages; size: 0.2 Mbytes

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  • Presented at: Adaptive Sensor Array Processing Workshop, Boston, MA, United States, Jun 05 - Jun 06, 2007

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  • Report No.: UCRL-CONF-231266
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 919230
  • Archival Resource Key: ark:/67531/metadc890255

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Office of Scientific & Technical Information Technical Reports

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  • May 17, 2007

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  • Sept. 22, 2016, 2:13 a.m.

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  • April 17, 2017, 12:53 p.m.

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Chambers, D; Lehman, S & Dowla, F. Model-based Layer Estimation using a Hybrid Genetic/Gradient Search Optimization Algorithm, article, May 17, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc890255/: accessed June 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.