Feed-Forward Neural Network Denoising Applied to Goldstone Solar System Radar Images

Abstract

The study of Near-Earth Asteroids (NEA) is crucial for human safety. Small hazardous asteroids with small radar cross sections are not easy to detect, track, and characterize due to the small signal-to-noise ratio (SNR) of the radar echo. This manuscript describes the results obtained for the application of a feed-forward neural network (FFNN) denoising methodology to NEA data obtained from the Goldstone Solar System Radar (GSSR). We demonstrate an increase in the signal level of up to ×4 the original value—in terms of sigma above the mean noise—when applying the FFNN denoising technique to radar Z-score normalized Binary Phase Code (BPC) images. This improvement benefits better radar detection of NEAs in general. Reducing the noise background level for antennas that have lower aperture, e.g., 34 m dishes, enables the use of FFNN denoising to improve visual detections on those noisier conditions. In addition, reducing noise level benefits shorter integration times of the data to obtain adequate signal levels. When talking about detection of small bodies crossing the antenna beam, since the asteroids or debris can move across the beam quite fast, it is relevant to reduce the integration time to allow for an increased number of independent pieces of information crossing the target through the antenna beam. The increased distance between the signal level and the noise level enables a better detection of the small-bodies at shorter integration times and therefore would be very useful for the detection of objects in the cis-lunar space.

Keywords

• Goldstone Solar System Radar
• asteroid
• near-earth object
• denoising
• neural network