A Novel Range Processing Method of Surface-Based FMCW Ice-Sounding Radar for Accurately Mapping the Internal Reflecting Horizons in Antarctica

Abstract

In this paper, we establish the nonlinear signal model of the surface-based frequency-modulated continuous wave (FMCW) ice-sounding radar, and propose a novel range processing strategy which aims at removing the frequency ramp nonlinearity effectively. The proposed algorithm takes fully consideration of the dependence of nonlinearity on the round-trip delay time while providing certain robustness of noise. The theory analysis and implementation steps of the proposed algorithm are demonstrated. The full-scale simulations with different kinds of nonlinearities and various signal-to-noise ratios (SNRs) verify the effectiveness and robustness of the proposed algorithm. We also apply the proposed method to real data set collected during the 31 st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33. The result shows the effectiveness of our algorithm on illustrating clarified Internal Reflecting Horizons (IRHs) of ice sheets. Compared with the echograms processed by the typical range processing scheme, our algorithm performs better in nonlinearity elimination.

Machine Learning-Based Human Posture Identification from Point Cloud Data Acquisitioned by FMCW Millimetre-Wave Radar

“…Compared with the traditional calibration method, the calibration error is reduced significantly. Other applications include automotive radar [ 10 ], drone detection [ 11 ], snow research [ 12 ], contactless measurement [ 13 , 14 ], vital sign detection [ 15 ], remote sensing [ 16 ], gait recognition [ 17 ], etc. With the increasing demand for healthcare technology, surveillance, and human-machine interfaces, the use of FMCW mmWave radar to recognize human postures has become an important topic.…”