| Budget Amount *help |
¥28,860,000 (Direct Cost: ¥22,200,000、Indirect Cost: ¥6,660,000)
Fiscal Year 2005: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2004: ¥8,840,000 (Direct Cost: ¥6,800,000、Indirect Cost: ¥2,040,000)
Fiscal Year 2003: ¥15,340,000 (Direct Cost: ¥11,800,000、Indirect Cost: ¥3,540,000)
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| Research Abstract |
This study was carried out with the purpose of developing an antipersonnel plastic landmine visualization radar system that deals with spatial-and frequency-domain interferometric texture adaptively in a brainlike manner based on the newly proposed theory, i.e., complex-valued neural networks. The developed system consists of three parts : (1)densely integrated wideband antenna handset, (2)iAmage clustering processing module employing complex-valued self-organizing map (CSOM), and (3)landmine-class identifier based on complex-valued associative memory.
(l)Densely integrated wideband antenna handset :
We have developed a new antenna element, i.e., walled-LTSA, which has a smaller aperture and a wide bandwidth. This antenna array enables us to capture subsurface reflection two-dimensionally with a high spatial resolution in a phase-sensitive manner.
(2)Image clustering processing module employing complex-valued self-organizing map(CSOM) :
We have also developed an adaptive texture classifier
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based on the CSOM. The reflection has complex-amplitude information in space (two-dimension) and frequency (one-dimension) domains (three-dimension in total). The CSOM segment the three-dimensional image by taking into account the complex-amplitude texture in an adaptive manner. The module successfully distinguishes a plastic landmine area from other soil areas including, e.g., stones and metal fractions.
(3)Landmine-class identifier based on complex-valued associative memory :
We have to identify plastic landmine class(es) among the segmented areas in the CSOM module. This identifying module deals with the feature vectors on double stages. First, the module chooses a feature-vector sets, in the memorized teacher data, nearest to those in the test observation. Then, it determines the correspondence of respective feature vectors to those in the teacher data.
In the developed system, we found that in the laboratory experiment the visualization performance for plastic landmines is near to that of the metal detectors for metal landmines. However, we also find that the performance is dependent on the conditions of soil, humidity, and other environment in the observation. Therefore, in the next step, we are going to have a field test out of the laboratory, but instead, on the field almost identical to that of landmine field next. In our plan, we start the field test in the following academic year. Less
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