基于最小細(xì)節(jié)熵準(zhǔn)則的雷達(dá)網(wǎng)信號增強(qiáng)
Netted Radar Data Enhancement Based on Detail Entropy Minimization
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摘要: 提出了細(xì)節(jié)熵的概念，它能更真實(shí)地反映圖像的清晰程度。采用最小細(xì)節(jié)熵準(zhǔn)則對雷達(dá)網(wǎng)的數(shù)據(jù)進(jìn)行融合。對于復(fù)雜目標(biāo)采用迭代方法計(jì)算了最小細(xì)節(jié)熵準(zhǔn)則所需的累加權(quán)值。為滿足實(shí)際工作中實(shí)時(shí)性要求，采用神經(jīng)網(wǎng)絡(luò)來獲取融合時(shí)的累加權(quán)值。
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關(guān)鍵詞:
- 熵;神經(jīng)網(wǎng)絡(luò);雷達(dá)網(wǎng);融合;信噪比
Abstract: Detail entropy is proposed which can indicate the image clarity much accurately in the case of that image has slowly changed part. A technique based on detail entropy minimization principle is developed for fusing netted radar data. For complex radar target, the weights are calculated iteratively. But in consider of real-time requirement, the weights are gotten by neural network alternatively. -
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