Abstract
In this article, we establish the nonlinear signal model of the surface-based frequency-modulated continuous wave ice-sounding radar, and propose a novel range processing strategy that aims at removing the frequency ramp nonlinearity effectively. The proposed algorithm takes full 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 verify the effectiveness and robustness of the proposed algorithm. We also apply the proposed method to real dataset collected during the 31st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33. The result shows the effectiveness of our algorithm on illustrating clarified internal reflecting horizons of ice sheets. Compared with the echograms processed by the typical range processing scheme, our algorithm performs better in nonlinearity elimination.
Highlights
I N CONTRARY to the conventional ice-sounding radar works in pulsed mode [1]–[3], the frequency-modulated continuous wave (FMCW) radar requires less peak transmit power and offers the benefits of compacted size, light weight, low power cost, and high range resolution [4]
2) The ability of nonlinearity elimination is evaluated by selecting several typical periodic nonlinearities Pp(t) under the assumption that the phase noise keeps constant throughout the elimination simulations
The frequency response Pat(f ) is consistent with the measurement result for antenna array equipped in our ultrawideband FMCW ice-sounding radar system, which will be discussed in Section IV-B
Summary
I N CONTRARY to the conventional ice-sounding radar works in pulsed mode [1]–[3], the frequency-modulated continuous wave (FMCW) radar requires less peak transmit power and offers the benefits of compacted size, light weight, low power cost, and high range resolution [4]. The fractional system transfer function merely containing the transmitted nonlinearity integrated with the impact of antennas’ distortions is achieved by combing the transmitted nonlinearity estimation result and the measurement result for transmitting and receiving antennas in the microwave anechoic chamber At this point, the 1-D inverse filtering named the acceleration Richardson–Lucy (R–L) deconvolution algorithm [20], [21] is implemented to counteract the transmitted nonlinearity and the impact of antennas’ distortions in consideration of alleviating the ill-posed problem and promoting the noise robustness. Compared with the aforementioned nonlinearity removal method, our proposed nonlinearity elimination strategy takes full consideration of the dependence of nonlinearity on the round-trip delay time while providing certain robustness of noise.
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