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 Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

Methods for Improving Foot Displacement Measurements Calculated from Inertial Sensors

2011 ◽  
pp. 93-116 ◽  
Author(s):  
Edgar Charry ◽  
Daniel T.H. Lai
Keyword(s):  
Measurement System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Human Movement ◽  
Sensor Data ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Band Pass ◽  
Sensor Errors ◽  
Displacement Measurements

The use of inertial sensors to measure human movement has recently gained momentum with the advent of low cost micro-electro-mechanical systems (MEMS) technology. These sensors comprise accelerometer and gyroscopes which measure accelerations and angular velocities respectively. Secondary quantities such as displacement can be obtained by integration of these quantities, a method which presents challenging issues due to the problem of accumulative sensor errors. This chapter investigates the spectral evaluation of individual sensor errors and looks at the effectiveness of minimizing these errors using static digital filters. The primary focus is on the derivation of foot displacement data from inertial sensor measurements. The importance of foot, in particular toe displacement measurements is evident in the context of tripping and falling which are serious health concerns for the elderly. The Minimum Toe Clearance (MTC) as an important gait variable for falls-risk prediction and assessment, and therefore the measurement variable of interest. A brief sketch of the current devices employing accelerometers and gyroscopes is presented, highlighting the problems and difficulties reported in literature to achieve good precision. These have been mainly due to the presence of sensor errors and the error accumulative process employed in obtaining displacement measurements. The investigation first proceeds to identify the location of these sensor errors in the frequency domain using the Fast Fourier Transform (FFT) on raw inertial sensor data. The frequency content of velocity and displacement measurements obtained from integrating the inertial data using a well known strap-down method is then explored. These investigations revealed that large sensor errors occurred mainly in the low frequency spectrum while white noise exists in all frequency spectra. The efficacy of employing a band-pass filter to remove a large portion of these errors and their effect on the derived displacements is elaborated on. The cross-correlation of the FFT power spectra from a highly accurate optical measurement system and processed sensor data is used as a metric to evaluate the performance of the band-pass filter at several stages of the processing stage. The motivation is that a more fundamental method would require less computational demand and could lead to more efficient implementations in low-power and systems with limited resources, so that portable sensor based motion measurement system would provide a good degree of measurement accuracy.

scholarly journals Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongda Zhang ◽  
Ting Zhang
Keyword(s):  
Parallel Processing ◽  
Data Fusion ◽  
Inertial Sensor ◽  
Processing Method ◽  
Feature Subset Selection ◽  
Sensor Data ◽  
Feature Subset ◽  
Vertical Projection ◽  
Multisensor Data Fusion ◽  
Multisensor Data

Aerobics is one of the main contents of physical education, which has a positive role in promoting the health of young people. This paper mainly studies the parallel processing method of inertial aerobics multisensor data fusion. In this paper, an aerobics exercise system is designed, which uses digital filter to remove the noise generated in the process of exercise. In this paper, Kalman filter is used to filter the pulse error of accelerometer, and the data structure of unidirectional link is used to achieve the effect of sliding window, which can reduce the memory cost to the greatest extent. In this paper, the region of moving object is determined by horizontal and vertical projection of binary symmetric difference image. At the same time, the optimal feature combination is selected from the reduced features by feature subset selection, and the classification algorithm is used as the evaluation function in the optimization process. Finally, the collected data are tested, analyzed, and sorted out. The experimental data show that, after calibrating the sensor data, the static x-axis and y-axis data are about 0 g, and the z-axis data are about 1 g, which is closer to the real value. The results show that the attitude data collected by the inertial sensor can be stably transmitted to the software of the computer wirelessly for attitude reconstruction, and the recognition of each attitude and parameter has reached a high accuracy.

Design of Two-Wheeled Self-Balanced Robot Based on Kalman Filter

2014 ◽  
Vol 971-973 ◽  
pp. 444-449
Author(s):  
Ting Gui Li
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
High Efficiency ◽  
Inertial Sensor ◽  
Optimal Estimation ◽  
Sensor Data ◽  
Wheeled Robot ◽  
Kalman Filter Algorithm ◽  
Balancing Robot ◽  
Balancing Control

Using the chip MC9S12XS128, two-wheeled self-balancing robot control system is designed. Its posture information is detected by accelerometer MMA7260 and gyro NEC-03, multi inertial sensor data fusion is realized by Kalman filter, posture data optimal estimation is gotten, and the accuracy of posture sensing system is improved. Using integral separation PID control algorithm, controlling the left and right motors are accelerated or decelerated, self-balancing control of two-wheeled robot is achieved. The experimental results show that, using the hardware platform MC9S12XS128, Kalman filter algorithm has high efficiency and posture data fusion is accurate and reliable, requirements which are posture optimal estimation and inclination data real-time feedback are met, and the system is stable and can accurately and quickly realize self-balancing control of two-wheeled robot.

scholarly journals An Improved Initial Alignment Method for Rocket Navigation Systems

2013 ◽  
Vol 66 (5) ◽  
pp. 737-749 ◽  
Author(s):  
Lihua Zhu ◽  
Xianghong Cheng
Keyword(s):  
Kalman Filter ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Wind Effect ◽  
Navigation Systems ◽  
Alignment Method ◽  
Initial Alignment ◽  
Online Estimation ◽  
Sensor Errors ◽  
Lever Arm Effect

This paper proposes an algorithm for the initial alignment method for rocket navigation systems. It uses the inertial freezing alignment to resolve the attitude matrix with respect to its fast and robust characteristics. Due to disturbances from the swaying base environment, such as people walking and wind effect, which would consequently result in a great lever arm effect, a Finite Impulse Response (FIR) filter is utilized to decrease the noise in the accelerometers' measurement. In addition, there are sensor errors in the system; the online estimation of gyroscopes' drift with a Kalman filter is adopted to achieve compensation. Numerical results from a simulated rocket initial alignment experiment are reported to demonstrate the effectiveness of the method.

scholarly journals A Fast SINS Initial Alignment Method Based on RTS Forward and Backward Resolution

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Houzeng Han ◽  
Jian Wang ◽  
Mingyi Du
Keyword(s):  
Data Processing ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Simulation Studies ◽  
Initial Alignment ◽  
High Quality ◽  
Data Set ◽  
Iterative Processing ◽  
Gyro Bias ◽  
Real Test

For the strapdown inertial navigation system (SINS), the procedure of initial alignment is a necessity before the navigation can commence. On the quasi-stationary base, the self-alignment can be fulfilled with the high quality inertial sensors, and the fine alignment is usually executed to improve the alignment performance. Generally, fast estimating of heading misalignment is still a challenge due to the existence of gyro errors. An innovative data processing strategy called forward and backward resolution is proposed for INS initial alignment. The Rauch-Tung-Striebel (RTS) smoothing is applied to obtain the smoothed attitude estimates with the filter information provided by the forward data processing. The obtained attitudes are then treated as aiding measurements to implement the forward resolution with the repeated data set, the converged sensor biases are used as constraints, and the iterative processing is conducted to obtain the updated attitudes. Simulation studies have been conducted to validate the proposed algorithm. The results have shown that the alignment accuracy and convergence rate have been improved with the added RTS aided forward and backward resolution; more stable heading estimates can be obtained by calibrating with estimated gyro bias. A real test with a high quality inertial sensor was also carried out to validate the effectiveness of the proposed algorithm.

scholarly journals Analytical Assessment of the Propagation of Colored Sensor Noise in Strapdown Inertial Navigation

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6914
Author(s):  
Christopher Blum ◽  
Johann Dambeck
Keyword(s):  
Gaussian Noise ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
White Gaussian Noise ◽  
Sensor Data ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sensor Noise ◽  
Sensor Errors ◽  
Strapdown Inertial Navigation

Knowledge of the propagation of sensor errors in strapdown inertial navigation is crucial for the design of inertial and integrated navigation systems. The propagation of initialization errors and deterministic sensor errors is well covered in the literature. If considered at all, the propagation of inertial sensor noise has typically been assessed for un-correlated (white) Gaussian noise. Real inertial sensor noise, however, is time-correlated (colored) and best described by a combination of different stochastic processes. In this paper, we demonstrate how a navigation system’s response to colored noise input differs from the response to bias-like or white noise inputs. We present a method for assessing the navigation error from various inertial sensor noise processes without the need for time-consuming Monte Carlo simulations and demonstrate its application and validity with real sensor data. The proposed method is used to determine in which scenarios the sensor’s real noise can be approximated by simple white Gaussian noise. The results indicate that neglecting colored sensor noise is justified for many applications, but should be checked individually for each sensor configuration and mission.

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