scholarly journals Using Inertial Sensors for Position and Orientation Estimation

2017 ◽  
Vol 11 (1-2) ◽  
pp. 1-153 ◽  
Author(s):  
Manon Kok ◽  
Jeroen D. Hol ◽  
Thomas B. Schön
Keyword(s):  
Inertial Sensors ◽  
Orientation Estimation ◽  
Position And Orientation

scholarly journals A Novel Separated Position and Orientation System Integrated with Inertially Stabilized Platform

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Yanshun Zhang ◽  
Shuangji Feng ◽  
Zhanqing Wang ◽  
Xiaopeng Xi ◽  
Ming Li
Keyword(s):  
Inertial Sensors ◽  
Orientation System ◽  
Imaging Payloads ◽  
Position And Orientation ◽  
Velocity Motion ◽  
Stabilized Platform ◽  
Application Requirements ◽  
Inertially Stabilized Platform ◽  
Centralized System ◽  
Effective Function

Considering the application requirements of independent imaging payloads design, a novel scheme of separated position and orientation system (POS) is proposed, in which the high-precision inertial sensors of traditional centralized POS fixed on the imaging payloads are mounted on three gimbals of the inertially stabilized platform (ISP), respectively, and make them integrated. Then, the kinematics model of the ISP system is built to transmit the inertial information measured by separated inertial sensors mounted on ISP gimbals and flight body to the imaging payloads, calculating the position and attitude of the imaging payloads to achieve the function of separated POS. Based on the model, a series of simulations indicate that the precision difference between separated system and centralized system is ignorable under the condition of angular motion and variable velocity motion. Besides the effective function equal to traditional centralized system, the separated POS enhances the integration with the ISP. Moreover, it improves the design independence of the imaging payloads significantly.

scholarly journals 5G Position and Orientation Estimation through Millimeter Wave MIMO

2015 ◽  
Author(s):  
Arash Shahmansoori ◽  
Gabriel E. Garcia ◽  
Giuseppe Destino ◽  
Gonzalo Seco-Granados ◽  
Henk Wymeersch
Keyword(s):  
Millimeter Wave ◽  
Orientation Estimation ◽  
Position And Orientation

Calibration of a Triaxial, Consumer-grade Magnetometer via an Extended Two-step Methodology

2020 ◽  
Author(s):  
Rogério P. Menezes Filho ◽  
Felipe O. Silva ◽  
Leonardo A. Vieira ◽  
Lucas P. S. Paiva ◽  
Gustavo S. Carvalho
Keyword(s):  
Law Enforcement ◽  
Hardware Implementation ◽  
Inertial Sensors ◽  
Film Industry ◽  
Low Cost ◽  
External Information ◽  
Navigation Systems ◽  
Smartphone Applications ◽  
Pros And Cons ◽  
Position And Orientation

Humans have always had the necessity of estimating their location in space for various reasons, e.g. hunting, traveling, sailing, battling, etc. Today, many other areas also demand that information, such as aviation, agriculture, multiple smartphone applications, law enforcement, and even film industry, to mention but a few. Estimating position and orientation is known as navigation, and the means to achieve it are called navigation systems. Each approach has its pros and cons, but sometimes it is possible to combine them into an improved architecture. For instance, inertial sensors (i.e. accelerometers and gyroscopes) can be integrated with magnetometers, producing an Attitude and Heading Reference System (AHRS); this process is referred to as sensor fusion. However, before sensors can be used to produce the navigation solution, calibration is often necessary, especially for low-cost devices. In this study,we perform the calibration of a triaxial consumer-grade magnetometer via an extended two-step methodology, correct small mistakes present in the original paper, and evaluate the technique in a restricted motion scenario. This technique can be implemented in-field, simply by rotating the sensors to multiple orientations; the only external information necessary is the local Earth's magnetic field density, easily estimated through reliable models. The error parameters, i.e. biases, scale factors, and misalignments, are indirectly estimated via a least squares algorithm. The calibration is first performed through software simulation, followed by hardware implementation to validate the results.

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