Influence of the ionospheric plasma density fluctuations on the subsurface sounding of the Martian soil by a synthetic aperture radar

Ilyushin, Ya. A.

doi:10.1007/s11141-009-9139-6

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…The compressed radar pulse in time domain can then be evaluated by common matched filtration technique. We used the same pulse compression procedure as in our previous studies, where this procedure is described in detail by Ilyushin, (, , ). Systematic phase shift, introduced by the ionosphere, can be introduced separately as an independent exponential factor at the signal compression stage.…”

Section: Synthetic Aperture Radar Clutter Simulatormentioning

confidence: 99%

CLUSIM: A Synthetic Aperture Radar Clutter Simulator for Planetary Exploration

et al. 2017

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In this paper we present the CLUtter SIMulator (CLUSIM), a special program simulating radar side echoes from rough planetary surfaces using realistic topography data sets. A numerical model of realistic topography of the Martian surface, based on Mars Orbiter Laser Altimeter data, is developed. A specially developed computer routine for evaluation of wideband radar echoes reflected from rough surfaces, capable of aperture synthesis simulation, is described. A synthetic radargram for a portion of Mars Express (MEX) orbit 9466 is computed and validated against experimental data obtained by the MARSIS radar instrument. Finally, a previously developed ionospheric phase correction procedure is numerically tested with new simulated echo signals. Impact of the surface clutter on the ionospheric correction procedure is investigated with a direct numerical comparison to a known benchmark result, which shows robustness of the correction algorithm with respect to the surface clutter.

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Section: Synthetic Aperture Radar Clutter Simulatormentioning

confidence: 99%

CLUSIM: A Synthetic Aperture Radar Clutter Simulator for Planetary Exploration

et al. 2017

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“…[5] For communication and navigation systems, single-frequency users may receive a positioning error of meters to ten meters. Other than GNSS, the operation of radio detection and ranging systems [6][7][8][9] and very-longbaseline-interferometry (VLBI) [10] also require ionospheric information to reduce the impact of the ionosphere.…”

Section: Introductionmentioning

confidence: 99%

Ionospheric vertical total electron content prediction model in low-latitude regions based on long short-term memory neural network

Liang

Wang

et al. 2022

Chinese Phys. B

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Ionosphere delay is one of the main sources of noise affecting Global Navigation Satellite Systems, operation of radio detection and ranging systems and, very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To improve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than 5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.

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High Performance Parallel Simulations of Subsurface Radar Sounding of Celestial Bodies

Ilyushin

2019

Communications in Computer and Information Science

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Influence of the ionospheric plasma density fluctuations on the subsurface sounding of the Martian soil by a synthetic aperture radar

Cited by 10 publications

References 17 publications

CLUSIM: A Synthetic Aperture Radar Clutter Simulator for Planetary Exploration

CLUSIM: A Synthetic Aperture Radar Clutter Simulator for Planetary Exploration

Ionospheric vertical total electron content prediction model in low-latitude regions based on long short-term memory neural network

High Performance Parallel Simulations of Subsurface Radar Sounding of Celestial Bodies

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