scholarly journals Estimation and Error Analysis for Optomechanical Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6101
Author(s):  
Patrick Kelly ◽  
Manoranjan Majji ◽  
Felipe Guzmán
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Measurement Noise ◽  
Sensor Model ◽  
Sensing Technology ◽  
Operation Period ◽  
Extended Operation ◽  
Calibration Step

A sensor model and methodology to estimate the forcing accelerations measured using a novel optomechanical inertial sensor with the inclusion of stochastic bias and measurement noise processes is presented. A Kalman filter for the estimation of instantaneous sensor bias is developed; the outputs from this calibration step are then employed in two different approaches for the estimation of external accelerations applied to the sensor. The performance of the system is demonstrated using simulated measurements and representative values corresponding to a bench-tested 3.76 Hz oscillator. It is shown that the developed methods produce accurate estimates of the bias over a short calibration step. This information enables precise estimates of acceleration over an extended operation period. These results establish the feasibility of reliably precise acceleration estimates using the presented methods in conjunction with state of the art optomechanical sensing technology.

scholarly journals Smartphone-Based Inertial Odometry for Blind Walkers

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4033
Author(s):  
Peng Ren ◽  
Fatemeh Elyasi ◽  
Roberto Manduchi
Keyword(s):  
Particle Filtering ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Mean Shift ◽  
Gait Pattern ◽  
Comparative Assessment ◽  
Pedestrian Tracking ◽  
Step Counter ◽  
Assisted Navigation

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

A new method to reduce the noise of the miniaturised inertial sensors disposed in redundant linear configurations

2013 ◽  
Vol 117 (1188) ◽  
pp. 111-132 ◽  
Author(s):  
T. L. Grigorie ◽  
R. M. Botez
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Frequency ◽  
Minimum Variance ◽  
Measurement Unit ◽  
Time Step ◽  
Accelerometer Sensor ◽  
Sensor Model ◽  
Input Acceleration

Abstract This paper presents a new adaptive algorithm for the statistical filtering of miniaturised inertial sensor noise. The algorithm uses the minimum variance method to perform a best estimate calculation of the accelerations or angular speeds on each of the three axes of an Inertial Measurement Unit (IMU) by using the information from some accelerometers and gyros arrays placed along the IMU axes. Also, the proposed algorithm allows the reduction of both components of the sensors’ noise (long term and short term) by using redundant linear configurations for the sensors dispositions. A numerical simulation is performed to illustrate how the algorithm works, using an accelerometer sensor model and a four-sensor array (unbiased and with different noise densities). Three cases of ideal input acceleration are considered: 1) a null signal; 2) a step signal with a no-null time step; and 3) a low frequency sinusoidal signal. To experimentally validate the proposed algorithm, some bench tests are performed. In this way, two sensors configurations are used: 1) one accelerometers array with four miniaturised sensors (n = 4); and 2) one accelerometers array with nine miniaturised sensors (n = 9). Each of the two configurations are tested for three cases of input accelerations: 0ms−1, 9·80655m/s2 and 9·80655m/s2.

Global Positioning System (GPS): A Low-Cost Velocity Sensor for Correcting Inertial Sensor Errors on Ground Vehicles

2004 ◽  
Vol 126 (2) ◽  
pp. 255-264 ◽  
Author(s):  
David M. Bevly
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Positioning System ◽  
Ground Vehicles ◽  
Velocity Sensor ◽  
Global Positioning ◽  
Sensor Errors

This paper demonstrates the ability of a standard low-cost Global Positioning System (GPS) receiver to reduce errors inherent in low-cost accelerometers and rate gyroscopes used on ground vehicles. Specifically GPS velocity is used to obtain vehicle course, velocity, and road grade, as well as to correct inertial sensors errors, providing accurate longitudinal and lateral acceleration, and pitch, roll, and yaw angular velocities. Additionally, it is shown that transient changes in sideslip (or lateral velocity), roll, and pitch angles can be measured. The method utilizes GPS velocity measurements to determine the inertial sensor errors using a kinematic Kalman Filter estimator. Simple models of the inertial sensors, which take into account the sensor noise and bias drift properties, are developed and used to design the estimator. Based on the characteristics of low-cost GPS receivers and IMU sensors, this paper presents the achievable performance of the combined system using the covariance analysis from the Kalman filter. Subsequent simulations and experiments validate both the error analysis and the methodology for utilizing GPS as a velocity sensor for correcting low-cost inertial sensor errors and providing critical vehicle state measurements.

Novel Hybrid of LS-SVM and Kalman Filter for GPS/INS Integration

2010 ◽  
Vol 63 (2) ◽  
pp. 289-299 ◽  
Author(s):  
Zhenkai Xu ◽  
Yong Li ◽  
Chris Rizos ◽  
Xiaosu Xu
Keyword(s):  
Support Vector Machine ◽  
Kalman Filter ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Micro Electro Mechanical System ◽  
Research Area ◽  
Optimal Number ◽  
Integrated System ◽  
Support Vector ◽  
Highly Nonlinear

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) technologies can overcome the drawbacks of the individual systems. One of the advantages is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when Micro-Electro-Mechanical System (MEMS) inertial sensors are used. Methods being currently explored by the research community include applying vehicle motion constraints, optimal smoother, and artificial intelligence (AI) techniques. In the research area of AI, the neural network (NN) approach has been extensively utilised up to the present. In an NN-based integrated system, a Kalman filter (KF) estimates position, velocity and attitude errors, as well as the inertial sensor errors, to output navigation solutions while GPS signals are available. At the same time, an NN is trained to map the vehicle dynamics with corresponding KF states, and to correct INS measurements when GPS measurements are unavailable. To achieve good performance it is critical to select suitable quality and an optimal number of samples for the NN. This is sometimes too rigorous a requirement which limits real world application of NN-based methods.The support vector machine (SVM) approach is based on the structural risk minimisation principle, instead of the minimised empirical error principle that is commonly implemented in an NN. The SVM can avoid local minimisation and over-fitting problems in an NN, and therefore potentially can achieve a higher level of global performance. This paper focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems. This paper explores the application of the LS-SVM to aid the GPS/INS integrated system, especially during GPS outages. The paper describes the principles of the LS-SVM and of the KF hybrid method, and introduces the LS-SVM regression algorithm. Field test data is processed to evaluate the performance of the proposed approach.

scholarly journals Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Matthew Cossaboom ◽  
Jacques Georgy ◽  
Tashfeen Karamat ◽  
Aboelmagd Noureldin
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Map Matching ◽  
Loosely Coupled ◽  
The Road ◽  
Land Vehicles ◽  
Global Positioning ◽  
Reduced Inertial Sensor System

Owing to their complimentary characteristics, global positioning system (GPS) and inertial navigation system (INS) are integrated, traditionally through Kalman filter (KF), to obtain improved navigational solution. To reduce the overall cost of the system, microelectromechanical system- (MEMS-) based INS is utilized. One of the approaches is to reduce the number of low-cost inertial sensors, decreasing their error contribution which leads to a reduced inertial sensor system (RISS). This paper uses KF to integrate GPS and 3D RISS in a loosely coupled fashion to enhance navigational solution while further improvement is achieved by augmenting it with map matching (MM). The 3D RISS consists of only one gyroscope and two accelerometers along with the vehicle’s built-in odometer. MM limits the error growth during GPS outages by restricting the predicted positions to the road networks. The performance of proposed method is compared with KF-only 3D RISS/GPS integration to demonstrate the efficacy of the proposed technique.

scholarly journals Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Transformation Matrices ◽  
True Model ◽  
Advance Research ◽  
Manual Palpation ◽  
Visualization Tools ◽  
Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

scholarly journals Inertial Sensor Reliability and Validity for Static and Dynamic Balance in Healthy Adults: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5167
Author(s):  
Nicky Baker ◽  
Claire Gough ◽  
Susan J. Gordon
Keyword(s):  
Systematic Review ◽  
Discriminant Validity ◽  
Inertial Sensors ◽  
Postural Sway ◽  
Inertial Sensor ◽  
Dynamic Balance ◽  
Healthy Adults ◽  
Gait Velocity ◽  
Time Data ◽  
Targeted Interventions

Compared to laboratory equipment inertial sensors are inexpensive and portable, permitting the measurement of postural sway and balance to be conducted in any setting. This systematic review investigated the inter-sensor and test-retest reliability, and concurrent and discriminant validity to measure static and dynamic balance in healthy adults. Medline, PubMed, Embase, Scopus, CINAHL, and Web of Science were searched to January 2021. Nineteen studies met the inclusion criteria. Meta-analysis was possible for reliability studies only and it was found that inertial sensors are reliable to measure static standing eyes open. A synthesis of the included studies shows moderate to good reliability for dynamic balance. Concurrent validity is moderate for both static and dynamic balance. Sensors discriminate old from young adults by amplitude of mediolateral sway, gait velocity, step length, and turn speed. Fallers are discriminated from non-fallers by sensor measures during walking, stepping, and sit to stand. The accuracy of discrimination is unable to be determined conclusively. Using inertial sensors to measure postural sway in healthy adults provides real-time data collected in the natural environment and enables discrimination between fallers and non-fallers. The ability of inertial sensors to identify differences in postural sway components related to altered performance in clinical tests can inform targeted interventions for the prevention of falls and near falls.

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

2020 ◽  
pp. 1-21
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Moving Average ◽  
Integrated System ◽  
Measurement Noise ◽  
Noise Estimation ◽  
Doppler Velocity ◽  
Forgetting Factor ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

scholarly journals A Novel Robust Kalman Filter With Non-stationary Heavy-tailed Measurement Noise

2020 ◽  
Vol 53 (2) ◽  
pp. 368-373
Author(s):  
Guangle Jia ◽  
Yulong Huang ◽  
Mingming B. Bai ◽  
Yonggang zhang
Keyword(s):  
Kalman Filter ◽  
Measurement Noise ◽  
Robust Kalman Filter ◽  
Heavy Tailed
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