Synthetic aperture radar image formation with neural networks

Abstract

This paper discusses the use of neural networks to perform synthetic aperture radar (SAR) azimuthal image generation. With a SAR, the positional geometry of a moving radar antenna can be related to the doppler shift of distributed (and possibly moving) targets on the surface. The cross-range image formation can be done with simple linear transforms and is not investigated. Digital matched filter processors require that a computer be programmed to perform sequential correlations between all expected variations of the return waveform and the actual radar return data. For the SAR processor, these operations must be performed for all positions of the antenna to form an image. Image formation is a computation intensive process that may take hours or days, depending on the size and complexity of the image. For example, the SEASAT satellite, launched in 1978, carried a L-band (1.25 Ghz) SAR for ocean imaging. Figure 1 is a SEASAT image of the Long Beach, California area. Only recently has all the data from this system been processed digitally. Interestingly, because digital technology was relatively primitive in the late 1970’s, SEASAT radar data was manipulated as analog data, The image formation was done with optical processors that use light beams and lenses to perform the transforms. These optical processors operate at the speed of light, therefore the image formation is near instantaneous. The image size, resolution, and duty cycle of the analog SEASAT is just now being matched by most “modem” digital data radars. When true large scale analog neural networks become available, SAR image formation could again become a mundane instantaneous operation. SAR processing of coherent complex signal histories is a good candidate for neural network