Synthesis of Wideband Imaging Beamformers Using Evolutionary Programming.

This paper presents spatial arrays of antenna elements deployed over a plane region are often used to image sources of reflected power in radar applications. The array of antennas samples the reflected field at a fixed set of locations. The received data are then processed using signal processing techniques such as beamforming to produce an image characterizing the distribution of the objects in the field of view of the active array imaging system. For some applications, as in through-wall microwave imaging, the scene to be imaged is often illuminated with wideband signals to achieve good range resolution. For such imaging problems, the concept of coarrays may be used to facilitate array signal processing issues in a simplified context [1, 2], resulting in a Point Spread Function, PSF(thetas, phi), which is not only a function of weights at the coarray elements but also a function of the wideband signal spectrum, S(omega). For high resolution imaging, it is desirable to synthesize the weights and S(omega), to achieve a desired PSF distribution, for example one with certain beam shape and/or with tapered side lobe levels. This could be potentially a challenging non-linear optimization problem, consisting of a very large number of optimization parameters, and is different from the standard narrow-band fixed antenna array problem, which maybe solved by traditional array synthesis techniques. Evolutionary algorithms such as genetic algorithms (GAs), evolutionary programming (EP) and particle swarm optimization (PSO) are well suited for this class of problems.

Main Author: Hoorfar, Ahmad.
Other Authors: Ahmad, Fauzia., Thajudeen, Christopher.
Format: Villanova Faculty Authorship
Language: English
Published: 2008
Online Access: http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:177055
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dc_source_str_mv Proceedings of 2008 IEEE International Symposium on Antennas and Propagation, 2008, 1- 4.
author Hoorfar, Ahmad.
author_facet_str_mv Hoorfar, Ahmad.
Ahmad, Fauzia.
Thajudeen, Christopher.
author_or_contributor_facet_str_mv Hoorfar, Ahmad.
Ahmad, Fauzia.
Thajudeen, Christopher.
author_s Hoorfar, Ahmad.
spellingShingle Hoorfar, Ahmad.
Synthesis of Wideband Imaging Beamformers Using Evolutionary Programming.
author-letter Hoorfar, Ahmad.
author_sort_str Hoorfar, Ahmad.
author2 Ahmad, Fauzia.
Thajudeen, Christopher.
author2Str Ahmad, Fauzia.
Thajudeen, Christopher.
dc_title_str Synthesis of Wideband Imaging Beamformers Using Evolutionary Programming.
title Synthesis of Wideband Imaging Beamformers Using Evolutionary Programming.
title_short Synthesis of Wideband Imaging Beamformers Using Evolutionary Programming.
title_full Synthesis of Wideband Imaging Beamformers Using Evolutionary Programming.
title_fullStr Synthesis of Wideband Imaging Beamformers Using Evolutionary Programming.
title_full_unstemmed Synthesis of Wideband Imaging Beamformers Using Evolutionary Programming.
collection_title_sort_str synthesis of wideband imaging beamformers using evolutionary programming.
title_sort synthesis of wideband imaging beamformers using evolutionary programming.
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description This paper presents spatial arrays of antenna elements deployed over a plane region are often used to image sources of reflected power in radar applications. The array of antennas samples the reflected field at a fixed set of locations. The received data are then processed using signal processing techniques such as beamforming to produce an image characterizing the distribution of the objects in the field of view of the active array imaging system. For some applications, as in through-wall microwave imaging, the scene to be imaged is often illuminated with wideband signals to achieve good range resolution. For such imaging problems, the concept of coarrays may be used to facilitate array signal processing issues in a simplified context [1, 2], resulting in a Point Spread Function, PSF(thetas, phi), which is not only a function of weights at the coarray elements but also a function of the wideband signal spectrum, S(omega). For high resolution imaging, it is desirable to synthesize the weights and S(omega), to achieve a desired PSF distribution, for example one with certain beam shape and/or with tapered side lobe levels. This could be potentially a challenging non-linear optimization problem, consisting of a very large number of optimization parameters, and is different from the standard narrow-band fixed antenna array problem, which maybe solved by traditional array synthesis techniques. Evolutionary algorithms such as genetic algorithms (GAs), evolutionary programming (EP) and particle swarm optimization (PSO) are well suited for this class of problems.
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dc.title Synthesis of Wideband Imaging Beamformers Using Evolutionary Programming.
dc.creator Hoorfar, Ahmad.
Ahmad, Fauzia.
Thajudeen, Christopher.
dc.description This paper presents spatial arrays of antenna elements deployed over a plane region are often used to image sources of reflected power in radar applications. The array of antennas samples the reflected field at a fixed set of locations. The received data are then processed using signal processing techniques such as beamforming to produce an image characterizing the distribution of the objects in the field of view of the active array imaging system. For some applications, as in through-wall microwave imaging, the scene to be imaged is often illuminated with wideband signals to achieve good range resolution. For such imaging problems, the concept of coarrays may be used to facilitate array signal processing issues in a simplified context [1, 2], resulting in a Point Spread Function, PSF(thetas, phi), which is not only a function of weights at the coarray elements but also a function of the wideband signal spectrum, S(omega). For high resolution imaging, it is desirable to synthesize the weights and S(omega), to achieve a desired PSF distribution, for example one with certain beam shape and/or with tapered side lobe levels. This could be potentially a challenging non-linear optimization problem, consisting of a very large number of optimization parameters, and is different from the standard narrow-band fixed antenna array problem, which maybe solved by traditional array synthesis techniques. Evolutionary algorithms such as genetic algorithms (GAs), evolutionary programming (EP) and particle swarm optimization (PSO) are well suited for this class of problems.
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