Development of AESA radar simulator for space observation using GEDAE

2016 ◽  
Author(s):  
Chen Cho ◽  
Minseok Choi ◽  
Hyun-ki Min ◽  
EunHee Kim
Keyword(s):  
Radar Simulator

Study on Echo Fidelity of Radar Simulator

2014 ◽  
Vol 687-691 ◽  
pp. 1117-1120
Author(s):  
Yin Hui Xu ◽  
Fu Zhi Wang ◽  
Yi Long Liu ◽  
Da Zhi Zeng
Keyword(s):  
Numerical Calculation ◽  
Evaluation System ◽  
Calculation Methods ◽  
Simulation Fidelity ◽  
Research Status ◽  
Similarity Calculation ◽  
Radar Echo ◽  
Echo Simulation ◽  
Radar Simulator ◽  
The Evaluation System

Echo fidelity is an important characteristic of radar simulator and it influences accuracy and reliability of radar test directly. This paper introduces the research status of simulator fidelity and sort out the definition and property of radar echo simulation fidelity. Then we propose the numerical calculation methods for individual indicators, including similarity calculation, dissimilarity computing and interval changes three parts. Finally, on the basis of the evaluation system, we propose two calculation methods for entirety fidelity, they are weighted numerical calculation and umbrella figure methods.

Advanced Polarimetric Doppler Weather Radar Simulator

2014 ◽  
Author(s):  
S. Lischi ◽  
A. Lupidi ◽  
M. Martorella ◽  
F. Cuccoli ◽  
L. Facheris ◽  
...  
Keyword(s):  
Weather Radar ◽  
Doppler Weather Radar ◽  
Radar Simulator

Tracking Position of Airborne Target on SPx-Radar-Simulator Using Probabilistic Data Association Filter

2021 ◽  
Author(s):  
Mochammad Sahal ◽  
Zaidan Adenin Said ◽  
Rusdhianto Effendi Abdul Kadir ◽  
Zulkifli Hidayat ◽  
Yusuf Bilfaqih ◽  
...  
Keyword(s):  
Data Association ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Radar Simulator ◽  
Airborne Target

Research on Radar Target Simulator

2014 ◽  
Vol 971-973 ◽  
pp. 1726-1729
Author(s):  
Ying Liu ◽  
Dian Ren Chen ◽  
Lei Chen
Keyword(s):  
Analog Circuits ◽  
Simulation System ◽  
Host Computer ◽  
Radar Signal ◽  
Radar Target ◽  
Sampled Data ◽  
Characteristic Parameters ◽  
Target Characteristic ◽  
Radar Simulator ◽  
Simulation Signal

A radar target simulation system based on DRFM is designed in this paper ,in this system, the radar signal that is amplified and conversioned by the receive analog circuits is directly sampled by the ADC of DRFM, then the sampled data is stored in QDR2 SRAM array. When need to generate radar target simulation signal, the radar signal data is read from the QDR2 SRAM array and synthesis radar target simulation signal with the target characteristic parameters provided by the host computer. It can be widely used in various radar simulator occasions.

General Purpose Radar Simulator based on Blender Cycles Path Tracer

2020 ◽  
Author(s):  
Rômulo Costa ◽  
Diego Medeiros ◽  
Raíssa Andrade ◽  
Osamu Saotome ◽  
Renato Machado
Keyword(s):  
General Purpose ◽  
Radar Simulator

scholarly journals Use of a Radar Simulator on the Output Fields from a Numerical Mesoscale Model to Analyze X-Band Rain Estimators

2008 ◽  
Vol 25 (3) ◽  
pp. 341-367 ◽  
Author(s):  
E-P. Zahiri ◽  
M. Gosset ◽  
J-P. Lafore ◽  
V. Gouget
Keyword(s):  
Measurement Uncertainty ◽  
Size Distribution ◽  
Mesoscale Model ◽  
Squall Line ◽  
Model Case ◽  
Differential Phase Shift ◽  
Drop Shape ◽  
X Band ◽  
Radar Simulator ◽  
Rain Rates

Abstract A full radar simulator, which works with the 3D output fields from a numerical mesoscale model, has been developed. This simulator uses a T-matrix code to calculate synthetic radar measurements, accounts for both backscattering and propagation effects, and includes polarimetric variables. The tool is modular to allow several options in the derivation of the synthetic radar variables. A measurement uncertainty can be taken into account on both the simulated reflectivities and the differential phase shift. A scheme can also be switched on to allow for the gate-to-gate variability of the rain drops size distribution or, also, their oblateness. This work was done in the framework of the installation in West Africa of a polarimetric X-band radar for the observation of tropical rain. Accordingly, the first objective pursued with this simulation setup is a detailed analysis of X-band polarimetric rain retrieval algorithms. Two retrieval schemes, a simple R–KDP formula and a profiler that uses both reflectivity and ϕDP, are tested. For that purpose the simulator is run on a model case study of an African squall line, then the two schemes are used to retrieve the rain rates from the synthetic radar variables and compare them to the original. The scores of the schemes are discussed and compared. The authors analyze the sensitivity of the results to the measurement uncertainty and also to several aspects of drop size distribution and drop shape variability.

scholarly journals The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory

2020 ◽  
Vol 13 (4) ◽  
pp. 1975-1998 ◽  
Author(s):  
Mariko Oue ◽  
Aleksandra Tatarevic ◽  
Pavlos Kollias ◽  
Dié Wang ◽  
Kwangmin Yu ◽  
...  
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Model Simulation ◽  
Atmospheric Model ◽  
Cloud Microphysics ◽  
Virtual Observatory ◽  
Design Strategies ◽  
Modeling Framework ◽  
Cloud Resolving Model ◽  
Radar Simulator

Abstract. Ground-based observatories use multisensor observations to characterize cloud and precipitation properties. One of the challenges is how to design strategies to best use these observations to understand these properties and evaluate weather and climate models. This paper introduces the Cloud-resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution cloud-resolving models (CRMs) to emulate multiwavelength, zenith-pointing, and scanning radar observables and multisensor (radar and lidar) products. CR-SIM allows for direct comparison between an atmospheric model simulation and remote-sensing products using a forward-modeling framework consistent with the microphysical assumptions used in the atmospheric model. CR-SIM has the flexibility to easily incorporate additional microphysical modules, such as microphysical schemes and scattering calculations, and expand the applications to simulate multisensor retrieval products. In this paper, we present several applications of CR-SIM for evaluating the representativeness of cloud microphysics and dynamics in a CRM, quantifying uncertainties in radar–lidar integrated cloud products and multi-Doppler wind retrievals, and optimizing radar sampling strategy using observing system simulation experiments. These applications demonstrate CR-SIM as a virtual observatory operator on high-resolution model output for a consistent comparison between model results and observations to aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are publicly available to the scientific community.

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