Status fusion method for wireless network based on Kalman filtering and data associated

2013 ◽  
Vol 32 (11) ◽  
pp. 3112-3114 ◽  
Author(s):  
Mo-yi DUAN
Keyword(s):  
Wireless Network ◽  
Kalman Filtering ◽  
Fusion Method

Multi-Source Information Fusion Based on Kalman Filtering

2015 ◽  
Vol 727-728 ◽  
pp. 863-866
Author(s):  
Meng Meng Zhou ◽  
G.M. Gao ◽  
Hong Bo Yang
Keyword(s):  
Kalman Filtering ◽  
Information Fusion ◽  
Attitude Determination ◽  
Low Frequency ◽  
Fusion Method ◽  
Source Information ◽  
Satellite Platform ◽  
Pointing Accuracy ◽  
Micro Vibration ◽  
Better Than

Thehigh-frequency angular micro-vibration on satellite platform results in theoptical axis pointing decreasing accuracy. The Kalman filtering based on attitudeinformation fusion method is presented to solve this case and improve the pointing accuracy of attitude determination. Thesimulation results indicate that the estimated accuracy of Kalman filteringattitude information fusion method is better than the result only fromconventional low frequency sensor. Accordingly, the attitude information fusionmethod is verified and accuracy.

Vibration Correction With Kalman Filtering Based Data Fusion for Absolute Gravimeters

2019 ◽  
Author(s):  
Yi Wen ◽  
Kang Wu ◽  
Zhenxing Li ◽  
Jiamin Yao ◽  
Meiying Guo ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Kalman Filtering ◽  
Ground Vibration ◽  
Free Fall ◽  
Vibration Signal ◽  
Correction Method ◽  
Fusion Method ◽  
Absolute Gravimeter ◽  
Single Sensor ◽  
Better Than

Abstract Free-fall absolute gravimeters are important classical high precision absolute gravimeters in many branches of scientific research. But its performance is always troubled by the ground vibration. Vibration correction method is used to correct the result by detecting the ground vibration with sensors. A Kalman filter based fusion method is proposed to obtain more accurate ground vibration signal by fusing the outputs of the seismometer and the accelerometer. Experiment is conducted with the homemade T-1 absolute gravimeter, the standard deviation of the corrected results using seismometer data and fused data are 586.32 μGal (1 μGal = 10−8 m/s2) and 508.59 μGal respectively, much better than the uncorrected result’s 6548.96 μGal. The results prove the superiority of fused data over data measured from single sensor. It is believed that the application scene of the vibration correction will be broadened and the performance of the vibration correction will also be improved by using the proposed fusion method in the future.

scholarly journals A Hybrid Method to Improve the BLE-Based Indoor Positioning in a Dense Bluetooth Environment

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 424 ◽  
Author(s):  
Ke Huang ◽  
Ke He ◽  
Xuecheng Du
Keyword(s):  
Kalman Filtering ◽  
Hybrid Method ◽  
Dead Reckoning ◽  
Sliding Window ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Fusion Method ◽  
Filtering Method ◽  
Calculation Results ◽  
Received Signal Strength Indication

Indoor positioning using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. A number of efforts have been exerted to improve the performance of BLE-based indoor positioning. However, few studies pay attention to the BLE-based indoor positioning in a dense Bluetooth environment, where the propagation of BLE signals become more complex and more fluctuant. In this paper, we draw attention to the problems resulting from the dense Bluetooth environment, and it turns out that the dense Bluetooth environment would result in a high received signal strength indication (RSSI) variation and a longtime interval collection of BLE. Hence, to mitigate the effects of the dense Bluetooth environment, we propose a hybrid method fusing sliding-window filtering, trilateration, dead reckoning and the Kalman filtering method to improve the performance of the BLE indoor positioning. The Kalman filter is exploited to merge the trilateration and dead reckoning. Extensive experiments in a real implementation are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. The implementation results proved that the fusion method was the most effective method to improve the positioning accuracy and timeliness in a dense Bluetooth environment. The positioning root-mean-square error (RMSE) calculation results have showed that the hybrid method can achieve a real-time positioning and reduce error of indoor positioning.

Multi-Channel ARMA Signal Covariance Intersection Fusion Kalman Smoother

2013 ◽  
Vol 655-657 ◽  
pp. 701-704
Author(s):  
Peng Zhang
Keyword(s):  
Steady State ◽  
Kalman Filtering ◽  
Geometric Interpretation ◽  
Fusion Method ◽  
Filtering Method ◽  
Kalman Smoother ◽  
Cross Covariance ◽  
Covariance Intersection ◽  
Optimal Signal

For the multi-channel ARMA signal with two sensors, by the classical Kalman filtering method and the covariance intersection (CI) fusion method, a covariance intersection fusion steady-state Kalman signal smoother is presented, which is independent of the unknown cross-covariance. It is proved that its accuracy is higher than that of each local Kalman signal smoother, and is lower than that of the optimal signal fuser weighted by matrices. The geometric interpretation of the above accuracy relations are presented based on the covariance ellipses. A simulation example result shows its effectiveness and correctness.

scholarly journals Research on Classroom Online Teaching Model of “Learning” Wisdom Music on Wireless Network under the Background of Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jie Shan ◽  
Muhammad Talha
Keyword(s):  
Artificial Intelligence ◽  
Feature Selection ◽  
Wireless Network ◽  
Online Teaching ◽  
Feature Selection Method ◽  
Selection Method ◽  
Multimodal Fusion ◽  
Network Node ◽  
Fusion Method ◽  
Audio Features

This article uses a multimodal smart music online teaching method combined with artificial intelligence to address the problem of smart music online teaching and to compensate for the shortcomings of the single modal classification method that only uses audio features for smart music online teaching. The selection of music intelligence models and classification models, as well as the analysis and processing of music characteristics, is the subjects of this article. It mainly studies how to use lyrics and how to combine audio and lyrics to intelligently classify music and teach multimodal and monomodal smart music online. In the online teaching of smart music based on lyrics, on the basis of the traditional wireless network node feature selection method, three parameters of frequency, concentration, and dispersion are introduced to adjust the statistical value of wireless network nodes, and an improved wireless network is proposed. After feature selection, the TFIDF method is used to calculate the weights, and then artificial intelligence is used to perform secondary dimensionality reduction on the lyrics. Experimental data shows that in the process of intelligently classifying lyrics, the accuracy of the traditional wireless network node feature selection method is 58.20%, and the accuracy of the improved wireless network node feature selection method is 67.21%, combined with artificial intelligence and improved wireless, the accuracy of the network node feature selection method is 69.68%. It can be seen that the third method has higher accuracy and lower dimensionality. In the online teaching of multimodal smart music based on audio and lyrics, this article improves the traditional fusion method for the problem of multimodal fusion and compares various fusion methods through experiments. The experimental results show that the improved classification effect of the fusion method is the best, reaching 84.43%, which verifies the feasibility and effectiveness of the method.

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