scholarly journals High-precision pose and velocity measuring method for projectiles based on kalman filtering algorithm

2015 ◽  
Author(s):  
Liang Zhang ◽  
Lizhi Qian ◽  
QuanLi Ning ◽  
Jingxiao Wang
Keyword(s):  
High Precision ◽  
Kalman Filtering ◽  
Measuring Method ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm ◽  
Velocity Measuring

A North Finder Scheme for FOG SINS Based on Kalman Filtering

2014 ◽  
Vol 644-650 ◽  
pp. 4040-4043
Author(s):  
Wei Zhen Zheng ◽  
Gong Liu Yang ◽  
Kui Zhang ◽  
Ren Dong Ma
Keyword(s):  
High Precision ◽  
Kalman Filtering ◽  
Short Term ◽  
Filtering Algorithm ◽  
The North ◽  
Simulation Results ◽  
Kalman Filtering Algorithm ◽  
Initial Attitude ◽  
Gyro Bias

In the background of using SINS technology to the car north finder, how to use FOG SINS in-vehicle vibration environment to obtain accurate initial attitude information is analyzed. Based on two-point north finder algorithm, north finder algorithm and kalman filtering algorithm are combined, and the north finder scheme for FOG SINS based on kalman filtering is studied. Simulation results show that, the scheme can accelerate the estimation of gyro bias and accelerometer bias, and effectively filter out short-term disturbances. This scheme can obtain accurate high-precision attitude information in the disturbance environment.

Transmission rate conditions for distributed filtering in sensor networks against eavesdropper

2021 ◽  
pp. 014233122110056
Author(s):  
Bingya Zhao ◽  
Ya Zhang
Keyword(s):  
Sensor Networks ◽  
Topological Entropy ◽  
Kalman Filtering ◽  
Transmission Rate ◽  
Necessary Condition ◽  
Estimation Errors ◽  
Positive Role ◽  
Filtering Algorithm ◽  
Wiretap Channels ◽  
Kalman Filtering Algorithm

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.

scholarly journals Distributed Diffusion Unscented Kalman Filtering Algorithm with Application to Object Tracking

2020 ◽  
Vol 53 (2) ◽  
pp. 3577-3582
Author(s):  
Hao Chen ◽  
Jianan Wang ◽  
Chunyan Wang ◽  
Dandan Wang ◽  
Jiayuan Shan ◽  
...  
Keyword(s):  
Object Tracking ◽  
Kalman Filtering ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm

Linear Kalman Filtering Algorithm With Noisy Control Input Variable

2019 ◽  
Vol 66 (7) ◽  
pp. 1282-1286 ◽  
Author(s):  
Wentao Ma ◽  
Jinzhe Qiu ◽  
Junli Liang ◽  
Badong Chen
Keyword(s):  
Kalman Filtering ◽  
Control Input ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm
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