Multiple nodes transfer alignment for airborne missiles based on inertial sensor network

2017 ◽  
Vol 28 (9) ◽  
pp. 095001
Author(s):  
Fan Si ◽  
Yan Zhao
Keyword(s):  
Sensor Network ◽  
Inertial Sensor ◽  
Transfer Alignment

Optimising the Transfer Alignment of Weapon INS

2003 ◽  
Vol 56 (2) ◽  
pp. 323-335 ◽  
Author(s):  
Paul D. Groves
Keyword(s):  
Inertial Sensor ◽  
Integrated Navigation ◽  
Velocity Measurements ◽  
Transfer Alignment ◽  
Navigation Solution ◽  
Alignment Technique ◽  
Alignment Process ◽  
Using Data ◽  
Design Options ◽  
Better Than

Transfer alignment is the process of initialising and calibrating a weapon INS using data from the host aircraft's navigation system. To determine which transfer alignment technique performs best, different design options have been assessed, supported by simulation work. The dependence of transfer alignment performance on environmental factors, such as manoeuvres, alignment duration, lever arm and inertial sensor quality has also been studied. ‘Rapid’ alignment, using attitude as well as velocity measurements was found to perform better than ‘conventional’ techniques using only velocity. Innovative developments include the estimation of additional acceleration and gyro states and estimation of force dependent relative orientation, which has enabled robust alignment using wing rock manoeuvres, which do not require the pilot to change trajectory. Transfer alignment has been verified in real-time by flight trials on a Tornado aircraft. In addition, techniques have been developed to prevent transients in the aircraft integrated navigation solution following GPS re-acquisition after an outage of several minutes from disrupting the transfer alignment process.

scholarly journals A Rapid Transfer Alignment Method for SINS Based on the Added Backward-Forward SINS Resolution and Data Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang
Keyword(s):  
Data Fusion ◽  
Reference Data ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Alignment Method ◽  
Initial Alignment ◽  
Transfer Alignment ◽  
Gyro Bias ◽  
Sensor Errors ◽  
Rapid Transfer

Two viewpoints are given: (1) initial alignment of strapdown inertial navigation system (SINS) can be fulfilled with a set of inertial sensor data; (2) estimation time for sensor errors can be shortened by repeated data fusion on the added backward-forward SINS resolution results and the external reference data. Based on the above viewpoints, aiming to estimate gyro bias in a shortened time, a rapid transfer alignment method, without any changes for Kalman filter, is introduced. In this method, inertial sensor data and reference data in one reference data update cycle are stored, and one backward and one forward SINS resolutions are executed. Meanwhile, data fusion is executed when the corresponding resolution ends. With the added backward-forward SINS resolution, in the above mentioned update cycle, the estimating operations for gyro bias are added twice, and the estimation time for it is shortened. In the ship swinging condition, with the “velocity plus yaw” matching, the effectiveness of this method is proved by the simulation.

scholarly journals Gait Cycle Recognition based on Wireless Inertial Sensor Network

IERI Procedia ◽  
2013 ◽  
Vol 4 ◽  
pp. 44-52 ◽  
Author(s):  
Yundong Xuan ◽  
Zhan Zhao ◽  
Zhen Fang ◽  
Fangmin Sun ◽  
Zhihong Xu ◽  
...  
Keyword(s):  
Sensor Network ◽  
Gait Cycle ◽  
Inertial Sensor

scholarly journals Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 31715-31728 ◽  
Author(s):  
Yu-Liang Hsu ◽  
Shih-Chin Yang ◽  
Hsing-Cheng Chang ◽  
Hung-Che Lai
Keyword(s):  
Sensor Network ◽  
Activity Recognition ◽  
Inertial Sensor ◽  
Sport Activity

scholarly journals Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5024 ◽  
Author(s):  
Kee S. Moon ◽  
Sung Q Lee ◽  
Yusuf Ozturk ◽  
Apoorva Gaidhani ◽  
Jeremiah A. Cox
Keyword(s):  
Sensor Network ◽  
Inertial Sensor ◽  
Three Dimensional ◽  
Gait Pattern ◽  
Health Condition ◽  
Knee Joints ◽  
Measurement Unit ◽  
Abnormal Gait ◽  
Custom Made ◽  
The Individual

Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns.

Optimisation-based Transfer Alignment and Calibration Method for Inertial Measurement Vector Integration Matching

2016 ◽  
Vol 70 (3) ◽  
pp. 595-606 ◽  
Author(s):  
Lili Xie ◽  
Jiazhen Lu
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Prior Information ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Inertial Measurement ◽  
Transfer Alignment ◽  
Sensor Error ◽  
Vector Integration ◽  
Measurement Vector

The traditional Kalman filtering-based transfer alignment methods largely depend on prior information for initialisation. However, the initialisation process is hard to fulfil on a moving base. In this paper, a type of inertial measurement vector integration matching for optimisation-based transfer alignment and calibration is proposed to estimate the misalignment between the Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS), and main inertial sensor error parameters of SINS, including bias and scale factor error. In contrast to filter techniques, the proposed method has the capability of self-initialisation and provides a new idea to solve the alignment and calibration problem. No prior information is needed. Moreover, the integration time interval for the matching inertial measurement vector is selected by considering both the observation degree of inertial sensor error parameters and the noise effect. Simulation results demonstrate that the proposed method has faster convergence and is more accurate than Kalman filter techniques.

Long-distance running training system based on inertial sensor network

2021 ◽  
pp. 1-9
Author(s):  
Yingjiao Xiang ◽  
Baishun Sun ◽  
Zhiqin Wang ◽  
Fatma Taher
Keyword(s):  
Gait Analysis ◽  
Sensor Network ◽  
Inertial Sensor ◽  
Step Length ◽  
Training System ◽  
Distance Running ◽  
Long Distance ◽  
Running Training ◽  
Long Distance Running ◽  
Analysis System

Long-distance running is an advantage of Chinese sports, but compared with the world level, there is still a big gap. Therefore, an advanced long-distance running training system is urgently needed to scientifically train our long-distance runners to change this situation. The purpose of this article is to study the long-distance running training system under inertial sensor network. According to the actual situation at home and abroad, a human gait analysis system based on inertial sensors is designed. Gait parameters are transformed into clinical medicine through related algorithms and software platforms. Experimental results show that although the step length calculated by the gait analysis system is different from the actual step length, the error value is small, kept below 3 cm, and the error percentage is less than 2%, which meets the accuracy requirements of gait analysis. This fully proves the feasibility of the zero-speed correction method in gait analysis.

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