Microwave diversity imaging and automated target identification based on models of neural networks
Microwave diversity imaging and automated target identification based on models of neural networks
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基于神经网络模型的微波分集成像和自动目标识别
DOI：
10.1109/5.32058
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发表时间：
1989
期刊：
影响因子：
--
通讯作者：
N. Farhat
中科院分区：
文献类型：
--
作者：
N. Farhat
雷达目标可以通过形成具有足够分辨率以供人类观察者识别的图像或通过形成目标的签名或表示以供自动机器识别来识别。正如本文第一部分所总结的，结合了角度（方位）、光谱和偏振自由度的层析微波分集成像技术已被证明能够生成具有接近光学分辨率的目标散射中心的图像。在论文的第二部分中，作者表明，基于神经网络模型的集体非线性信号处理与合适的目标特征（此处为正弦图表示）的使用相结合，有望从部分信息中进行鲁棒的超分辨率目标识别。给出的结果是神经形态处理器的数值模拟，其中神经网络通过自动生成识别对象标签来同时执行数据存储、处理和识别的功能，并简要提到了快速光电架构和硬件实现。简要讨论了实际考虑因素和对实际系统的扩展。 >
Radar targets can be identified by either forming images with sufficient resolution to be recognized by the human observer or by forming signatures or representations of the target for automated machine recognition. Tomographic microwave diversity imaging techniques that combine angular (aspect), spectral, and polarization degrees of freedom have been shown, as summarized in the first part of this paper, to be capable of producing images of the scattering centers of a target with near optical resolution. In the second part of the paper the author shows that collective nonlinear signal processing based on models of neural networks combined with the use of suitable target signatures (here sinogram representations) offer the promise of robust super-resolved target identification from partial information. Results presented are of numerical simulations for a neuromorphic processor where the neural net performs simultaneously the functions of data storage, processing, and recognition by automatically generating an identifying object label, and fast optoelectronic architectures and hardware implementations are briefly mentioned. Practical considerations and extensions to real systems are briefly discussed. >