A New Configuration of ARS for Period Estimation to Reduce Computational Load

2021 ◽  
Author(s):  
Takashi Kuboya ◽  
Yukihiro Kamiya
Keyword(s):  
Computational Load ◽  
Period Estimation

scholarly journals Estimation of Wave Period from Pitch and Roll of a Lidar Buoy

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1310
Author(s):  
Andreu Salcedo-Bosch ◽  
Francesc Rocadenbosch ◽  
Miguel A. Gutiérrez-Antuñano ◽  
Jordi Tiana-Alsina
Keyword(s):  
Degrees Of Freedom ◽  
Translational Motion ◽  
Cutoff Frequency ◽  
Peak Level ◽  
Wave Period ◽  
Estimation Methods ◽  
Zero Crossing ◽  
Power Spectral ◽  
Period Estimation ◽  
Wind Lidar

This work proposes a new wave-period estimation (L-dB) method based on the power-spectral-density (PSD) estimation of pitch and roll motional time series of a Doppler wind lidar buoy under the assumption of small angles (±22 deg) and slow yaw drifts (1 min), and the neglection of translational motion. We revisit the buoy’s simplified two-degrees-of-freedom (2-DoF) motional model and formulate the PSD associated with the eigenaxis tilt of the lidar buoy, which was modelled as a complex-number random process. From this, we present the L-dB method, which estimates the wave period as the average wavelength associated to the cutoff frequency span at which the spectral components drop off L decibels from the peak level. In the framework of the IJmuiden campaign (North Sea, 29 March–17 June 2015), the L-dB method is compared in reference to most common oceanographic wave-period estimation methods by using a TriaxysTM buoy. Parametric analysis showed good agreement (correlation coefficient, ρ = 0.86, root-mean-square error (RMSE) = 0.46 s, and mean difference, MD = 0.02 s) between the proposed L-dB method and the oceanographic zero-crossing method when the threshold L was set at 8 dB.

scholarly journals Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Satellite System ◽  
Radar System ◽  
Target Localization ◽  
Computational Load ◽  
Matrix Fusion ◽  
Global Navigation Satellite ◽  
Multistatic Radar ◽  
Combined Algorithm

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

Adaptive iterative image restoration with reduced computational load

1990 ◽  
Vol 29 (12) ◽  
pp. 1458 ◽  
Author(s):  
Aggelos K. Katsaggelos
Keyword(s):  
Image Restoration ◽  
Computational Load

Multiscale Off-Road Mobility Simulation With Computational Load Balancing for Lower-Scale Discrete-Element Models

2021 ◽  
Author(s):  
Guanchu Chen ◽  
Hiroki Yamashita ◽  
Yeefeng Ruan ◽  
Paramsothy Jayakumar ◽  
Hiroyuki Sugiyama
Keyword(s):  
Load Balancing ◽  
Discrete Element ◽  
Computational Load

scholarly journals GRAPE: Genetic Routine for Astronomical Period Estimation

2018 ◽  
Vol 479 (4) ◽  
pp. 5196-5213 ◽  
Author(s):  
Paul R McWhirter ◽  
Iain A Steele ◽  
Abir Hussain ◽  
Dhiya Al-Jumeily ◽  
Marley M B R Vellasco
Keyword(s):  
Period Estimation
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