Paper
1 October 1991 Application of neural networks to range-Doppler imaging
Xiaoqing Wu, Zhaoda Zhu
Author Affiliations +
Abstract
The use of neural networks are investigated for 2-D range Doppler microwave imaging. The range resolution of the microwave image is obtained by transmitting a wideband signal and the cross-range resolution is achieved by the Doppler frequency gradient in the same range bin. Hopfield neural networks are used to estimate the Doppler spectrum to enhance the cross- range resolution and reduce the processing time. There is a large number of neurons needed for the high cross-range resolution. In order to cut down the number of neurons, the reflectivities are replaced with their minimum norm estimates. The original Hopfield networks converge often to a local minina instead of the global minima. Simulated annealing is applied to control the gain of Hopfield networks to yield better convergence to the global minima. Results of imaging a model airplane from real microwave data are presented.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaoqing Wu and Zhaoda Zhu "Application of neural networks to range-Doppler imaging", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48403
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Cited by 1 scholarly publication.
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KEYWORDS
Neurons

Neural networks

Doppler effect

Signal processing

Image processing

Reflectivity

Image resolution

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