Noise Tomography and Adaptive Illumination in Noise Radar
Abstract
Ultra-wideband (UWB) waveforms achieve excellent spatial resolution for better characterization of targets in tomographic imaging applications compared to narrowband waveforms. In this report, two-dimensional tomographic images of multiple scattering objects are successfully obtained using the diffraction tomography approach by transmitting multiple independent and identically distributed (iid) UWB random noise waveforms. The feasibility of using a random noise waveform for tomography is investigated by formulating a white Gaussian noise (WGN) model using spectral estimation. The analytical formulation of object image formation using random noise waveforms is established based on the backward scattering, and several numerical diffraction tomography simulations are performed in the spatial frequency domain to validate the analytical results by reconstructing the tomographic images of scattering objects. The final image of the object based on multiple transmitted noise waveforms is reconstructed by averaging individually formed images which compares very well with the image created using the traditional Gaussian pulse. Pixel difference-based measure is used to analyze and estimate the image quality of the final reconstructed tomographic image under various signal-to-noise ratio (SNR) conditions. A UWB noise radar was designed to transmit multiple UWB random noise waveforms over the 3-5 GHz frequency range and to measure the backward scattering data for the validation of the theoretical analysis and numerical simulation results. The reconstructed tomographic images of the rotating cylindrical objects based on experimental results are seen to be in good agreement with the simulation results, which demonstrates the capability of UWB noise radar for complete two-dimensional tomographic image reconstruction of various shaped metallic and dielectric target objects.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2015
- Accession Number
- ADA622664
Entities
People
- Hee J. Shin
- Mark A. Asmuth
- Ram M Narayanan
Organizations
- Pennsylvania State University