scholarly journals A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1340 ◽  
Author(s):  
Xudong Wen ◽  
Chunwu Liu ◽  
Zhiping Huang ◽  
Shaojing Su ◽  
Xiaojun Guo ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Low Cost ◽  
Attitude Estimation ◽  
Sensor Data ◽  
Processing Algorithm ◽  
Estimation Accuracy ◽  
First Order ◽  
Data Processing Algorithm ◽  
Adjustment Strategy

There are many algorithms that can be used to fuse sensor data. The complementary filtering algorithm has low computational complexity and good real-time performance characteristics. It is very suitable for attitude estimation of small unmanned aerial vehicles (micro-UAVs) equipped with low-cost inertial measurement units (IMUs). However, its low attitude estimation accuracy severely limits its applications. Though, many methods have been proposed by researchers to improve attitude estimation accuracy of complementary filtering algorithms, there are few studies that aim to improve it from the data processing aspect. In this paper, a real-time first-order differential data processing algorithm is proposed for gyroscope data, and an adaptive adjustment strategy is designed for the parameters in the algorithm. Besides, the differential-nonlinear complementary filtering (D-NCF) algorithm is proposed by combine the first-order differential data processing algorithm with the basic nonlinear complementary filtering (NCF) algorithm. The experimental results show that the first-order differential data processing algorithm can effectively correct the gyroscope data, and the Root Mean Square Error (RMSE) of attitude estimation of the D-NCF algorithm is smaller than when the NCF algorithm is used. The RMSE of the roll angle decreases from 1.1653 to 0.5093, that of the pitch angle decreases from 2.9638 to 1.5542, and that of the yaw angle decreases from 0.9398 to 0.6827. In general, the attitude estimation accuracy of D-NCF algorithm is higher than that of the NCF algorithm.

scholarly journals Effect of Strapdown Integration Order and Sampling Rate on IMU-Based Attitude Estimation Accuracy

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2775 ◽  
Author(s):  
Jung Lee ◽  
Mi Choi
Keyword(s):  
Low Cost ◽  
Sampling Rate ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Third Order ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
First Order ◽  
Noise Covariance ◽  
Integration Order

This paper deals with the strapdown integration of attitude estimation Kalman filter (KF) based on inertial measurement unit (IMU) signals. In many low-cost wearable IMU applications, a first-order is selected for strapdown integration, which may degrade attitude estimation performance in high-speed angular motions. The purpose of this research is to provide insights into the effect of the strapdown integration order and sampling rate on the attitude estimation accuracy for low-cost IMU applications. Experimental results showed that the effect of integration order was small when the angular velocity was low and the sampling rate was large. However, as the angular velocity increased and the sampling rate decreased, the effect of integration order increased, i.e., obviously, the third-order KF resulted in better estimations than the first-order KF. When comparing the case where both transient matrix and process noise covariance matrix are applied to the corresponding order and the case where only the transient matrix is applied to the corresponding order but the process noise covariance matrix for the first-order is still used, both cases had almost equivalent estimation accuracy. However, in terms of the calculation cost, the latter case was more economical than the former, particularly for the third-order KF (i.e., the ratio of the former to the latter is 1.22 to 1).

Improvement and Application of qPCR (Real-Time Quantitative Polymerase Chain Reaction) Data Processing Method for Home-Made Integrated Nucleic Acid Detection System

2020 ◽  
Vol 20 (12) ◽  
pp. 7369-7375
Author(s):  
Yile Fang ◽  
Pei Liao ◽  
Zhu Chen ◽  
Hui Chen ◽  
Yanqi Wu ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Data Processing ◽  
Real Time ◽  
Detection System ◽  
Disease Diagnosis ◽  
Processing Algorithm ◽  
Clinical Samples ◽  
Nucleic Acid Detection ◽  
Qpcr Data ◽  
Data Processing Algorithm

Because it has many advantages such as rapidity and accuracy, nucleic acid detection is applied to infectious disease diagnosis more and more. An automatic integrated nucleic acid detection system based on real-time PCR is developed by our research group to conduct point-of-care testing of infectious pathogens. The home-made detection system collects fluorescence data in each PCR cycle through an integrated dual-channel fluorescence detection module and then real-time fluorescence curves are drawn by the software, which can tell the results of the diagnostics after some processing and analysis. However, owing to the disturbance of the environment or the imperfect of nucleic acid extraction before PCR, the fluorescence curves sometimes may contain several abnormal points. For the purpose of enhancing its ability to deal with these iffy curves and improve the accuracy of the testing results, in this study, the SDM-based qPCR data processing algorithm was studied and 11 groups of qPCR data that have different flaws from the clinical samples detected by this system were chosen to prove the practicability of the method. In comparison with the conventional threshold-based method, the Cq values calculated by the SDM-based method were more close to the actual values, meaning it can overcome the shortcomings of the conventional methods such as being unable to accommodate noise and being unable to avoiding abnormal data. With the improvement of this data processing algorithm, the stability of our system and the reliability and accuracy of the results are greatly improved.

The PPP data processing algorithm to create geodetic networks

2021 ◽  
pp. 464-468
Author(s):  
A.D. Tikhonov ◽  
A.A. Kochiev
Keyword(s):  
Data Processing ◽  
Processing Algorithm ◽  
Navigation Systems ◽  
Geodetic Networks ◽  
Data Processing Algorithm ◽  
Global Navigation ◽  
Global Navigation Systems

The article deals with determination of coordinates using global navigation systems, and application of the PPP data processing algorithm to obtain coordinates. The authors conducted an experiment illustrating the algorithm accuracy.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

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