IEEE Transactions on Geoscience and Remote Sensing | 2021

Simulation of the Signal-to-Noise Ratio of Sanya Incoherent Scatter Radar Tristatic System

 
 
 
 
 
 
 
 
 
 

Abstract


Incoherent scatter radar (ISR), as one of the most powerful ionospheric detection methods on the ground, can obtain a variety of important ionospheric parameters with high spatiotemporal resolution over the entire ionosphere. A high-power large-aperture phased array (PA) ISR, named Sanya ISR (SYISR), is under construction at Sanya (18.3°N, 109.6°E), China. Two remote receive-only arrays of SYISR will be built in the future to form a tristatic ISR system, which will be the only multistatic ISR in a low-latitude region. Estimating the signal-to-noise ratio (SNR) is the key task for analyzing the detection accuracy and evaluating the radar performance. In this article, the SNR of the SYISR tristatic system is simulated for the first time using a suitable PA bistatic radar equation. The parameters of the designed SYISR tristatic system are used in the simulation. Subsequently, the SNR spatial variability of the bistatic PA radar and bistatic equivalent parabolic dish (EPD) radar is analyzed and compared. The results show that the radar system constants, effective scattering volume, and spatial variable terms are the three different sources of factors affecting the SNR. They jointly determine the spatial distribution characteristics of the SNR. Moreover, the PA SNR is significantly different from the EPD SNR numerically and morphologically. The PA SNR is weaker than the EPD SNR in the whole observational space. In the horizontal direction, the PA SNR has a single-peak structure, while the EPD SNR has a double-peak structure. The root cause of these differences is that the beamwidth of a PA changes with the beam directions, while that of the EPD is fixed. This research provides a theoretical basis for the design of tristatic PA ISR in hardware optimization and error analysis, which will be shown in another article.

Volume 59
Pages 2982-2993
DOI 10.1109/TGRS.2020.3008427
Language English
Journal IEEE Transactions on Geoscience and Remote Sensing

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