Data Quality Control of Wind Profiler Radar Based on Extended Kalman Filter

2012 ◽  
Vol 588-589 ◽  
pp. 897-901
Author(s):  
Jia Qiang Li ◽  
Xing Juan Chen ◽  
Jin Li Chen
Keyword(s):  
Kalman Filter ◽  
Data Processing ◽  
Data Quality ◽  
Extended Kalman Filter ◽  
Engineering Application ◽  
Nonlinear Problems ◽  
Wind Data ◽  
Wind Profiler ◽  
Data Quality Control ◽  
Wind Profiler Radar

As a new type of Doppler wind radar, Wind profiler radar often suffers from a variety of meteorological factors. These interferences have great effect on the detection data precision, which leads to radar data quality problem. A data processing method based on Extended Kalman Filter (EKF) is presented in this paper, which focuses on the nonlinear problems of wind data of wind profiler radar. This method is the development of traditional Kalman Filter (KF) in practical engineering applications. To verify the validity of this first-order EKF from different situation, one day’s and one moment’s wind data are selected respectively, as examples for filtering. EKF is better for data processing of wind profiler radar and has some engineering application prospects.

scholarly journals Extraction and Filter Algorithm of Guidance Information for Full-strapdown Seeker on Rotation Missile

2018 ◽  
Vol 214 ◽  
pp. 03008 ◽  
Author(s):  
YongShan Liu ◽  
Li Song ◽  
JingLong Li
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Simple Structure ◽  
Unscented Kalman Filter ◽  
High Reliability ◽  
Engineering Application ◽  
Line Of Sight ◽  
Light Weight ◽  
Rate Information ◽  
Guidance Information

Strapdown seekers are superior to platform seekers for their simple structure, high reliability and light weight but cannot measure the line-of-sight angle rate information for the guidance of rotation missile directly. This paper aims at the engineering application of full-strapdown seekers on rotation missile problem. Firstly, a line-of-sight angle rate solution model is established. Based on the MATLAB, the extended Kalman filter (EKF) algorithm and unscented Kalman filter (UKF) algorithm are used to estimate the line-of-sight angle rate information of the full-strapdown seekers. The results show that using EKF filter and UKF filter both can obtain effective guidance information and the UKF’s effect is better.

The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter

2014 ◽  
Vol 953-954 ◽  
pp. 796-799
Author(s):  
Huan Huan Sun ◽  
Jun Bi ◽  
Sai Shao
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Nonlinear Problems ◽  
Least Square Method ◽  
State Equation ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery State Of Charge

Accurate estimation of battery state of charge (SOC) is important to ensure operation of electric vehicle. Since a nonlinear feature exists in battery system and extended kalman filter algorithm performs well in solving nonlinear problems, the paper proposes an EKF-based method for estimating SOC. In order to obtain the accurate estimation of SOC, this paper is based on composite battery model that is a combination of three battery models. The parameters are identified using the least square method. Then a state equation and an output equation are identified. All experimental data are collected from operating EV in Beijing. The results of the experiment show  that the relative error of estimation of state of charge is reasonable, which proves this method has good estimation performance.

Pycheron: A Python-Based Seismic Waveform Data Quality Control Software Package

2021 ◽  
Author(s):  
Katherine Anderson Aur ◽  
Jessica Bobeck ◽  
Anthony Alberti ◽  
Phillip Kay
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
Data Quality ◽  
Monitoring Network ◽  
Downstream Processing ◽  
Data Quality Control ◽  
Data Set ◽  
Waveform Data ◽  
Seismic Waveform ◽  
Wide Range

Abstract Supplementing an existing high-quality seismic monitoring network with openly available station data could improve coverage and decrease magnitudes of completeness; however, this can present challenges when varying levels of data quality exist. Without discerning the quality of openly available data, using it poses significant data management, analysis, and interpretation issues. Incorporating additional stations without properly identifying and mitigating data quality problems can degrade overall monitoring capability. If openly available stations are to be used routinely, a robust, automated data quality assessment for a wide range of quality control (QC) issues is essential. To meet this need, we developed Pycheron, a Python-based library for QC of seismic waveform data. Pycheron was initially based on the Incorporated Research Institutions for Seismology’s Modular Utility for STAtistical kNowledge Gathering but has been expanded to include more functionality. Pycheron can be implemented at the beginning of a data processing pipeline or can process stand-alone data sets. Its objectives are to (1) identify specific QC issues; (2) automatically assess data quality and instrumentation health; (3) serve as a basic service that all data processing builds on by alerting downstream processing algorithms to any quality degradation; and (4) improve our ability to process orders of magnitudes more data through performance optimizations. This article provides an overview of Pycheron, its features, basic workflow, and an example application using a synthetic QC data set.

Strong Tracking Filter Based on Extended Kalman Filter for Data Processing of Underwater Vehicle

2011 ◽  
Vol 219-220 ◽  
pp. 569-573
Author(s):  
Ye Li ◽  
Zhen Lu ◽  
Yong Jie Pang
Keyword(s):  
Kalman Filter ◽  
Nonlinear System ◽  
Data Processing ◽  
Extended Kalman Filter ◽  
Underwater Vehicle ◽  
Underwater Navigation ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter ◽  
Navigation Information

A strong tracking filter based on suboptimal fading extended Kalman filter was proposed to ensure the perception for the motion state of underwater vehicles accurate in the paper. For the uncertainty of nonlinear system model, the strong tracking filter theory was introduced, orthogonality principle was put forward. Then suboptimal fading factor was pulled in, and extended Kalman filter for nonlinear system was established. The strong tracking filter was applied to data processing of underwater vehicle, and results indicate that it can effectively improve the accuracy and robustness of underwater navigation information.

scholarly journals Development of a new data-processing method for SKYNET sky radiometer observations

2012 ◽  
Vol 5 (11) ◽  
pp. 2723-2737 ◽  
Author(s):  
M. Hashimoto ◽  
T. Nakajima ◽  
O. Dubovik ◽  
M. Campanelli ◽  
H. Che ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Radiative Forcing ◽  
Control Method ◽  
Processing Method ◽  
Observation Data ◽  
Data Quality Control ◽  
Sensitivity Experiments ◽  
Data Processing Method ◽  
Cloud Screening

Abstract. In order to reduce uncertainty in the estimation of Direct Aerosol Radiative Forcing (DARF), it is important to improve the estimation of the single scattering albedo (SSA). In this study, we propose a new data processing method to improve SSA retrievals for the SKYNET sky radiometer network, which is one of the growing number of networks of sun-sky photometers, such as NASA AERONET and others. There are several reports that SSA values from SKYNET have a bias compared to those from AERONET, which is regarded to be the most accurate due to its rigorous calibration routines and data quality and cloud screening algorithms. We investigated possible causes of errors in SSA that might explain the known biases through sensitivity experiments using a numerical model, and also using real data at the SKYNET sites at Pune (18.616° N/73.800° E) in India and Beijing (39.586° N/116.229° E) in China. Sensitivity experiments showed that an uncertainty of the order of ±0.03 in the SSA value can be caused by a possible error in the ground surface albedo or solid view angle assumed for each observation site. Another candidate for possible error in the SSA was found in cirrus contamination generated by imperfect cloud screening in the SKYNET data processing. Therefore, we developed a new data quality control method to get rid of low quality or cloud contamination data, and we applied this method to the real observation data at the Pune site in SKYNET. After applying this method to the observation data, we were able to screen out a large amount of cirrus-contaminated data and to reduce the deviation in the SSA value from that of AERONET. We then estimated DARF using data screened by our new method. The result showed that the method significantly reduced the difference of 5 W m−2 that existed between the SKYNET and AERONET values of DARF before screening. The present study also suggests the necessity of preparing suitable a priori information on the distribution of coarse particles ranging in radius between 10 μm and 30 μm for the analysis of heavily dust-laden atmospheric cases.

Realizing Maximum Transparency of Oceanographic Data Processing and Data Quality Control for Different End-User Communities

2020 ◽  
Author(s):  
Manuela Köllner ◽  
Mayumi Wilms ◽  
Anne-Christin Schulz ◽  
Martin Moritz ◽  
Katrin Latarius ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
Data Quality ◽  
Service Research ◽  
Published Data ◽  
Data Quality Control ◽  
End User ◽  
Wide Range ◽  
User Communities ◽  
Oceanographic Data

<p>Reliable data are the basis for successful research and scientific publishing. Open data policies assure the availability of publicly financed field measurements to the public, thus to all interested scientists. However, the variety of data sources and the availability or lack of detailed metadata cause a huge effort for each scientist to decide if the data are usable for their own research topic or not. Data end-user communities have different requirements in metadata details and data handling during data processing. For data providing institutes or agencies, these needs are essential to know, if they want to reach a wide range of end-user communities.</p><p>The Federal Maritime and Hydrographic Agency (BSH, Bundesamt für Seeschifffahrt und Hydrographie, Hamburg, Germany) is collecting a large variety of field data in physical and chemical oceanography, regionally focused on the North Sea, Baltic Sea, and North Atlantic. Data types vary from vertical profiles, time-series, underway measurements as well as real-time or delayed-mode from moored or ship-based instruments. Along other oceanographic data, the BSH provides all physical data via the German Oceanographic Data Center (DOD). It is crucial to aim for a maximum in reliability of the published data to enhance the usage especially in the scientific community.</p><p>Here, we present our newly established data processing and quality control procedures using agile project management and workflow techniques, and outline their implementation into metadata and accompanied documentation. To enhance the transparency of data quality control, we will apply a detailed quality flag along with the common data quality flag. This detailed quality flag, established by Mayumi Wilms within the research project RAVE Offshore service (research at alpha ventus) enables data end-users to review the result of several individual quality control checks done during processing and thus to identify easily if the data are usable for their research.</p>

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