Cramér-Rao bound for multiple parameters estimation using a polarization sensitive array

Author(s):  
Gui-mei ZHENG ◽  
Bai-xiao CHEN ◽  
Ming-lei YANG
2013 ◽  
Vol 22 (02) ◽  
pp. 1250091
Author(s):  
XIAOFEI ZHANG ◽  
CHEN CHEN ◽  
YINGJIE HUANG ◽  
HAILANG WU ◽  
JIANFENG LI ◽  
...  

This paper links the polarization-sensitive-array parameter estimation problem to the quadrilinear model. Exploiting this link, it derives a blind joint angle, frequency and polarization estimation algorithm. The simulation results reveal that the proposed algorithm has better angle, frequency and polarization estimation performance than ESPRIT. This algorithm relies on a fundamental result of the uniqueness of low-rank four-way data decomposition. Furthermore, the proposed algorithm does not require pairing among multiple parameters. Simulation results illustrate performance of this algorithm.


Author(s):  
Seong Beom Lee ◽  
Kishalay Mitra ◽  
Harry D. Pratt ◽  
Travis M. Anderson ◽  
Venkatasailanathan Ramadesigan ◽  
...  

Abstract In this paper, we study, analyze, and validate some important zero-dimensional physics-based models for vanadium redox batch cell (VRBC) systems and formulate an adequate physics-based model that can predict the battery performance accurately. In the model formulation process, a systems approach to multiple parameters estimation has been conducted using VRBC systems at low C-rates (∼C/30). In this batch cell system, the effect of ions' crossover through the membrane is dominant, and therefore, the capacity loss phenomena can be explicitly observed. Paradoxically, this means that using the batch system might be a better approach for identifying a more suitable model describing the effect of ions transport. Next, we propose an efficient systems approach, which enables to help understand the battery performance quickly by estimating all parameters of the battery system. Finally, open source codes, executable files, and experimental data are provided to enable people's access to robust and accurate models and optimizers. In battery simulations, different models and optimizers describing the same systems produce different values of the estimated parameters. Providing an open access platform can accelerate the process to arrive at robust models and optimizers by continuous modification from the users' side.


Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2453 ◽  
Author(s):  
Guangyong Zheng ◽  
Siqi Na ◽  
Tianyao Huang ◽  
Lulu Wang

Distributed multiple input multiple output (MIMO) radar has attracted much attention for its improved detection and estimation performance as well as enhanced electronic counter-counter measures (ECCM) ability. To protect the target from being detected and tracked by such radar, we consider a barrage jamming strategy towards a distributed MIMO. We first derive the Cramer–Rao bound (CRB) of target parameters estimation using a distributed MIMO under barrage jamming environments. We then set maximizing the CRB as the criterion for jamming resource allocation, aiming at degrading the accuracy of target parameters estimation. Due to the non-convexity of the CRB maximizing problem, particle swarm optimization is used to solve the problem. Simulation results demonstrate the advantages of the proposed strategy over traditional jamming methods.


2019 ◽  
Vol 27 (25) ◽  
pp. 37041 ◽  
Author(s):  
Zhenming Yu ◽  
Zhiquan Wan ◽  
Liang Shu ◽  
Shaohua Hu ◽  
Yilun Zhao ◽  
...  

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 164835-164843
Author(s):  
Wenxun Xiao ◽  
Ruigeng Shen ◽  
Bo Zhang ◽  
Dongyuan Qiu ◽  
Yanfeng Chen ◽  
...  

2021 ◽  
Author(s):  
Mengtian Lu ◽  
Sicheng Lu ◽  
Weihong Liao ◽  
Xiaohui Lei ◽  
Zhaokai Yin ◽  
...  

Abstract Although field measurements and using long hydrological datasets provide a reliable method for parameters' calibration, changes in the underlying basin surface and lack of hydrometeorological data may affect parameter accuracy in streamflow simulation. The ensemble Kalman filter (EnKF) can be used as a real-time parameter correction method to solve this problem. In this study, five representative Xin'anjiang model parameters are selected to study the effects of the initial parameter ensemble distribution and the specific function form of the parameter on EnKF parameter estimation process for both single and multiple parameters. Results indicate: (1) the method of parameter calibration to determine the initial distribution mean can improve the assimilation efficiency; (2) there is mutual interference among the parameters during multiple parameters' estimation which invalidates some conclusions of single-parameter estimation. We applied and evaluated the EnKF method in Jinjiang River Basin, China. Compared to traditional approaches, our method showed a better performance in both basins with long hydrometeorological dataset (an increase of Kling–Gupta efficiency (KGE) from 0.810 to 0.887 and a decrease of bias from −1.08% to −0.74%); and in basins with a lack of hydrometeorological data (an increase of KGE from 0.536 to 0.849 and a decrease of bias from −15.55% to −11.42%).


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