Sliding Average Allan Variance for Inertial Sensor Stochastic Error Analysis

2013 ◽  
Vol 62 (12) ◽  
pp. 3291-3300 ◽  
Author(s):  
Jintao Li ◽  
Jiancheng Fang
Keyword(s):  
Error Analysis ◽  
Inertial Sensor ◽  
Allan Variance ◽  
Stochastic Error

scholarly journals Allan variance in dynamically tuned inertial-unit gyro error analysis

2019 ◽  
pp. 69-77
Author(s):  
M. A. Sharova ◽  
S. S. Diadin
Keyword(s):  
Angular Velocity ◽  
Error Analysis ◽  
Output Signal ◽  
Allan Variance ◽  
Measurement Information ◽  
Noise Component ◽  
Velocity Sensor ◽  
Signal Noise ◽  
Variance Method ◽  
Inertial Unit

The purpose of the study was to consider an algorithm for obtaining the measurement information from a dynamically tuned gyroscope in the mode of an angular velocity sensor and output signal noise component estimate, the algorithm being based on the Allan variance method. The results obtained were evaluated

scholarly journals A regression‐based methodology to improve estimation of inertial sensor errors using Allan variance data

Navigation ◽  
2019 ◽  
Vol 66 (1) ◽  
pp. 251-263 ◽  
Author(s):  
Juan Jurado ◽  
Christine M. Schubert Kabban ◽  
John Raquet
Keyword(s):  
Inertial Sensor ◽  
Allan Variance ◽  
Sensor Errors

Error Analysis for the In-Situ Fabrication of Mechanisms

2003 ◽  
Vol 125 (4) ◽  
pp. 809-822 ◽  
Author(s):  
Sanjay Rajagopalan ◽  
Mark Cutkosky
Keyword(s):  
Error Analysis ◽  
Solid Freeform Fabrication ◽  
General Technique ◽  
Ongoing Research ◽  
Stochastic Error ◽  
In Situ Fabrication ◽  
Driver Error ◽  
Joint Clearances ◽  
Component Assembly

Fabrication techniques like Solid Freeform Fabrication (SFF), or Layered Manufacturing, enable the manufacture of completely pre-assembled mechanisms (i.e. those that require no explicit component assembly after fabrication). We refer to this manner of building assemblies as in-situ fabrication. An interesting issue that arises in this domain is the estimation of errors in the performance of such mechanisms as a consequence of manufacturing variability. Assumptions of parametric independence and stack-up made in conventional error analysis for mechanisms do not hold for this method of fabrication. In this paper we formulate a general technique for investigating the kinematic performance of mechanisms fabricated in-situ. The technique presented admits deterministic and stochastic error estimation of planar and spatial linkages with ideal joints. The method is illustrated with a planar example. Errors due to joint clearances, form errors, or other effects like link flexibility and driver-error, are not considered in the analysis—but are part of ongoing research.

Compensation for Stochastic Error of Gyros in a Dual-axis Rotational Inertial Navigation System

2015 ◽  
Vol 69 (1) ◽  
pp. 169-182 ◽  
Author(s):  
Zhichao Zheng ◽  
Songlai Han ◽  
Jin Yue ◽  
Linglong Yuan
Keyword(s):  
Navigation System ◽  
High Performance ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Inertial Navigation ◽  
Field Tests ◽  
Stochastic Error ◽  
Stochastic Errors

A dual-axis rotational Inertial Navigation System (INS) has received wide attention in recent years because of high performance and low cost. However, some errors of inertial sensors such as stochastic errors are not averaged out automatically during navigation. Therefore a Twice Position-fix Reset (TPR) method is provided to enhance accuracy of a dual-axis rotational INS by compensating stochastic errors. According to characteristics of an azimuth error introduced by stochastic errors of an inertial sensor in the dual-axis rotational INS, both an azimuth error and a radial-position error are much better corrected by the TPR method based on an optimised error propagation equation. As a result, accuracy of the dual-axis rotational INS is prominently enhanced by the TPR method, as is verified by simulations and field tests.

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