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
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neurons

Neural networks

Doppler effect

Signal processing

Image processing

Reflectivity

Image resolution

RELATED CONTENT

SAR image formation processing using planar subarrays
Proceedings of SPIE (June 09 1994)
Flash radar geometric simulator
Proceedings of SPIE (October 20 1993)
Inverse synthetic aperture radar image processing
Proceedings of SPIE (June 01 1992)

Back to Top