scholarly journals Real-Time Estimation of Temperature Time Derivative in Inertial Measurement Unit by Finite-Impulse-Response Exponential Regression on Updates

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1299
Author(s):  
Alexander Kozlov ◽  
Ilya Tarygin
Keyword(s):  
Real Time ◽  
Impulse Response ◽  
Measurement Errors ◽  
Finite Impulse Response ◽  
Time Derivative ◽  
Time Estimation ◽  
Measurement Unit ◽  
Model Parameters ◽  
Navigation Systems ◽  
Sensor Output

We present a filtering technique that allows estimating the time derivative of slowly changing temperature measured via quantized sensor output in real time. Due to quantization, the output may appear constant for several minutes in a row with the temperature actually changing over time. Another issue is that measurement errors do not represent any kind of white noise. Being typically the case in high-grade inertial navigation systems, these phenomena amid slow variations of temperature prevent any kind of straightforward assessment of its time derivative, which is required for compensating hysteresis-like thermal effects in inertial sensors. The method is based on a short-term temperature prediction represented by an exponentially decaying function, and on the finite-impulse-response Kalman filtering in its numerically stable square-root form, employed for estimating model parameters in real time. Instead of using all of the measurements, the estimation involves only those received when quantized sensor output is updated. We compare the technique against both an ordinary averaging numerical differentiator and a conventional Kalman filter, over a set of real samples recorded from the inertial unit.

scholarly journals Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems

2018 ◽  
Vol 12 (3) ◽  
pp. 639-649 ◽  
Author(s):  
Iman Hajizadeh ◽  
Mudassir Rashid ◽  
Sediqeh Samadi ◽  
Jianyuan Feng ◽  
Mert Sevil ◽  
...  
Keyword(s):  
Real Time ◽  
Plasma Insulin ◽  
Artificial Pancreas ◽  
Time Estimation ◽  
The Body ◽  
Insulin Concentration ◽  
Model Parameters ◽  
Plasma Insulin Concentration ◽  
Real Time Estimation ◽  
Clinical Experiments

Background: The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. Method: An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka’s glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. Results: The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. Conclusions: The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.

Near‐real time reduction of shipboard gravity using Kalman‐filtered GPS measurements

Geophysics ◽  
1991 ◽  
Vol 56 (12) ◽  
pp. 1971-1979 ◽  
Author(s):  
J. F. Genrich ◽  
J.-B. Minster
Keyword(s):  
Real Time ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Yield Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Marine Gravity ◽  
Gravity Measurements ◽  
Low Pass ◽  
Stabilized Platform

We have developed a Kalman filter to estimate accurate Eötvös corrections and horizontal ship accelerations from Global Positioning System (GPS) fixes. High‐resolution shipboard gravity measurements are obtained with a newly designed, linear phase, Finite Impulse Response (FIR) low‐pass filter. Both filters are combined to yield accurate, near‐real time, Eötvös‐corrected underway gravity estimates. Error ranges that reflect uncertainty in navigation for these estimates are calculated from autocovariances of Kalman velocity estimates by means of variance propagation expressions for time‐invariant linear digital filters. Estimates of horizontal ship acceleration are combined with a simplified instrument impulse response model in an attempt to remove transient noise from the gravimeter output. We apply the technique to data collected by two shipboard gravimeters, a LaCoste & Romberg Model S Air‐Sea Gravity Meter and a Bell Aerospace BGM-3 Marine Gravity Meter System, operated side‐by‐side on the Scripps R/V Thomas Washington during Leg 1 of the Roundabout expedition. In the absence of significant horizontal accelerations due to course or speed changes, both instruments yield data with good repeatability, characterized by rms differences of less than 1 mGal. Horizontal accelerations generate transient signals that cannot be modeled at present to an accuracy of better than 5 mGal. Difficulties in removing these transients are primarily due to insufficient quantitative knowledge of the response of the instrument, including the gyro‐stabilized platform. This can be determined analytically or empirically.

Modelling, Identification and Control of Thermal Deformation of Machine Tool Structures, Part 3: Real-Time Estimation of Heat Sources

1999 ◽  
Vol 121 (3) ◽  
pp. 501-508 ◽  
Author(s):  
S. Fraser ◽  
M. H. Attia ◽  
M. O. M. Osman
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Measurement Errors ◽  
Thermal Deformation ◽  
Heat Conduction Problem ◽  
Time Estimation ◽  
Inverse Heat Conduction Problem ◽  
Dimensional Structure ◽  
Measured Temperature ◽  
Real Time Estimation

Compensation of thermal deformation of machine tools requires real-time estimation of the heat input to the structure in order to fully describe its thermoelastic response. Available solutions of the inverse heat conduction problem IHCP are not suitable for real-time feedback control applications, since they are too slow and/or rely on future data to stabilize the solution. A new real-time IHCP solver is derived in the form of a convolution integral of the inverse thermal transfer function G−1(s) and the measured temperature difference at two points near the heat source. An expression for G−1(s) is derived for multi-dimensional structural components. To transform G−1(s) to the time domain, a special consideration is given to the treatment of its complex singularity functions. Analytical approach was followed to identify these functions and to determine their time-domain representation. Computer-simulation test cases were conducted using a finite element model of a three-dimensional structure. The random temperature measurement errors, which can lead to non-uniqueness and instability problems, have also been simulated. The test results showed that the computation time can significantly be improved to achieve a control cycle of less than one second, without compromising the accuracy and stability requirements.

scholarly journals Real Time Estimation of the Pose of a Lower Limb Prosthesis from a Single Shank Mounted IMU

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2865 ◽  
Author(s):  
Duraffourg ◽  
Bonnet ◽  
Dauriac ◽  
Pillet
Keyword(s):  
Real Time ◽  
Lower Limb ◽  
Time Estimation ◽  
Measurement Unit ◽  
Correct Identification ◽  
Accelerometer Data ◽  
Mode Recognition ◽  
Lower Limb Prosthesis ◽  
Real Time Estimation ◽  
Limb Prosthesis

The command of a microprocessor-controlled lower limb prosthesis classically relies on the gait mode recognition. Real time computation of the pose of the prosthesis (i.e., attitude and trajectory) is useful for the correct identification of these modes. In this paper, we present and evaluate an algorithm for the computation of the pose of a lower limb prosthesis, under the constraints of real time applications and limited computing resources. This algorithm uses a nonlinear complementary filter with a variable gain to estimate the attitude of the shank. The trajectory is then computed from the double integration of the accelerometer data corrected from the kinematics of a model of inverted pendulum rolling on a curved arc foot. The results of the proposed algorithm are evaluated against the optoelectronic measurements of walking trials of three people with transfemoral amputation. The root mean square error (RMSE) of the estimated attitude is around 3°, close to the Kalman-based algorithm results reported in similar conditions. The real time correction of the integration of the inertial measurement unit (IMU) acceleration decreases the trajectory error by a factor of 2.5 compared to its direct integration which will result in an improvement of the gait mode recognition.

scholarly journals Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Chao Gao ◽  
Jianhua Lu ◽  
Guorong Zhao ◽  
Shuang Pan
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Time Estimation ◽  
Finite Horizon ◽  
Navigation Systems ◽  
Robust Performance ◽  
Stochastic Parameters ◽  
Real Time Estimation ◽  
New Applications ◽  
Jump Markov System

Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper, an adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of remote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs are the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been modeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE) has been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays. First, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies. Second, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus realized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more robust performance than the FIR estimator using constant optimal horizon size.

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.

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