A Novel Initial Alignment Based on the HMM/Steady State KALMAN Filter

2013 ◽  
Vol 562-565 ◽  
pp. 426-430
Author(s):  
Xing Ma ◽  
Lun Chao Zhong ◽  
Jun Li Han ◽  
Chang Shun Liu
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Alignment Algorithm ◽  
Acceleration Sensor ◽  
Initial Alignment ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Acceleration Data ◽  
Low Pass ◽  
Poor Stability

In allusion to low accurary and poor stability of the initial alginment based on the acceleration sensor, the HMM (Hidden Marco Model) / steady state Kalman filter is designed to solve the problem above. C8051F340 is used to sample the acceleration data of the MEMS acceleration sensor Model1221, then the digital low-pass filter is used to filter the acceleration data. The steady state Kalman filter based on the second order HMM model. The initial alignment algorithm outputs the angle value. The experiments demonstrate that the the HMM / steady state Kalman filter is more stable and more precise than the traditional algorithm.

scholarly journals State estimation for dynamic weighing using Kalman filter

2021 ◽  
Author(s):  
◽  
Sunethra Pitawala
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
High Speed ◽  
Sensor Signal ◽  
Pass Filter ◽  
Diverse Range ◽  
Low Pass Filter ◽  
Low Pass ◽  
Using Data ◽  
Dynamic Weighing

<p>Dynamic weighing has become an essential requirement in a diverse range of industries. Dynamic weighing is different from static weighing in that static weighing involves determining the weight while the product being weighed is stationary whereas dynamic weighing weighs the products while they are moving. Force sensors are commonly used in these weighing systems. In static weighing, the weighed object is placed stationary on the platform and the steady state of the sensor signal is used to assess the weight. However, in dynamic weighing the sensor signal may not reach the steady state during the brief time of weighing, hence the weight is assessed for example, by averaging the tail end of the signal after it has been through a low-pass filter. The resulting mass estimates can be inaccurate for faster heavier items. It is useful to consider better ways of estimating the true weight, in high speed weighing applications.  The proposed method is to employ the 1-D Kalman filter algorithm to estimate the optimal state of the signal. The improved steady state signal is then used in weight estimation. The proposed method has been tested using data collected from a loadcell when different masses pass over the loadcell. The results show a significant improvement in the filtered signal quality which is then used to improve the weight assessment.</p>

scholarly journals State estimation for dynamic weighing using Kalman filter

2021 ◽  
Author(s):  
◽  
Sunethra Pitawala
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
High Speed ◽  
Sensor Signal ◽  
Pass Filter ◽  
Diverse Range ◽  
Low Pass Filter ◽  
Low Pass ◽  
Using Data ◽  
Dynamic Weighing

<p>Dynamic weighing has become an essential requirement in a diverse range of industries. Dynamic weighing is different from static weighing in that static weighing involves determining the weight while the product being weighed is stationary whereas dynamic weighing weighs the products while they are moving. Force sensors are commonly used in these weighing systems. In static weighing, the weighed object is placed stationary on the platform and the steady state of the sensor signal is used to assess the weight. However, in dynamic weighing the sensor signal may not reach the steady state during the brief time of weighing, hence the weight is assessed for example, by averaging the tail end of the signal after it has been through a low-pass filter. The resulting mass estimates can be inaccurate for faster heavier items. It is useful to consider better ways of estimating the true weight, in high speed weighing applications.  The proposed method is to employ the 1-D Kalman filter algorithm to estimate the optimal state of the signal. The improved steady state signal is then used in weight estimation. The proposed method has been tested using data collected from a loadcell when different masses pass over the loadcell. The results show a significant improvement in the filtered signal quality which is then used to improve the weight assessment.</p>

LC-Resonant and High-Temperature Resistant Acceleration Sensor Based on DSP

2011 ◽  
Vol 105-107 ◽  
pp. 1966-1969
Author(s):  
Tao Guo ◽  
Jie Zhu ◽  
Gui Tang ◽  
Yan Xu
Keyword(s):  
High Temperature ◽  
High Speed ◽  
Space Shuttle ◽  
Digital Signal ◽  
Acceleration Sensor ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Analog To Digital ◽  
Acceleration Sensors ◽  
Low Pass

It is a challenging problem to test the acceleration of the high-speed missiles and space shuttle under high temperature. This paper proposed a design of LC-resonant and high-temperature resistant acceleration sensors about the phenomenon. With the operational amplifier OP4177, it produces the input signal that contents with A/D (Analog to Digital) signal. An eight level low-pass filter MAX291 is used for testing after the signal is regulated. This design mainly uses AD7934 to complete the conversion from analog signals to digital signals. It also recognizes the resonant point of LC acceleration sensor by the DSP （Digital Signal Processing）recognizing program. The acceleration is computed finally by the DSP chip.

Motion Estimation Using Point Cluster Method and Kalman Filter

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
M. Senesh ◽  
A. Wolf
Keyword(s):  
Kalman Filter ◽  
Rigid Body ◽  
Rigid Body Motion ◽  
Body Motion ◽  
Cluster Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
The Real ◽  
Low Pass ◽  
Instantaneous Frequencies

The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures—PCT, Kalman filter followed by PCT, and low pass filter followed by PCT—enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal instantaneous frequencies.

scholarly journals A Vondrak Low Pass Filter for IMU Sensor Initial Alignment on a Disturbed Base

Sensors ◽  
2014 ◽  
Vol 14 (12) ◽  
pp. 23803-23821 ◽  
Author(s):  
Zengke Li ◽  
Jian Wang ◽  
Jingxiang Gao ◽  
Binghao Li ◽  
Feng Zhou
Keyword(s):  
Initial Alignment ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Comparison of Estimated Torques Using Low Pass Filter and Extended Kalman Filter for Induction Motor Drives

2015 ◽  
Vol 6 (1) ◽  
pp. 92
Author(s):  
Ibrahim Mohd Alsofyani ◽  
Nik Rumzi Nik Idris ◽  
Yahya A. Alamri ◽  
Tole Sutikno ◽  
Aree Wangsupphaphol ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Nonlinear Models ◽  
Motor Drives ◽  
Computational Time ◽  
Induction Motor Drives ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Calculation Process ◽  
Low Pass

<span lang="EN-US">Torque calculation process is one of the major concerns for controlling induction motors in industry, which requires very accurate state estimation of unmeasurable variables of nonlinear models. This can be solved if the variables used for torque calculation is accurately estimated.  This paper presents a torque calculation based on a voltage model represented with a low-pass filter (LPF), and an extended Kalman filter (EKF). The experimental results showed that the estimated torque at low speed based on EKF is more accurate in the expense of more complicated and larger computational time. </span>

scholarly journals Dynamics of Excitation and Inhibition in the Light-Adapted Limulus Eye in situ

1971 ◽  
Vol 58 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Marguerite Biederman-Thorson ◽  
John Thorson
Keyword(s):  
Steady State ◽  
Lateral Inhibition ◽  
Low Frequency ◽  
Dynamic Responses ◽  
Relaxation Processes ◽  
Pass Filter ◽  
Generator Potential ◽  
Low Pass Filter ◽  
Low Pass

The dynamics of spike discharge in eccentric cell axons from the in situ lateral eye of Limulus, under small sinusoidal modulation of light to which the eye is adapted, are described over two decades of light intensity and nearly three decades of frequency. Steady-state lateral inhibition coefficients, derived from the very low-frequency response, average 0.04 at three interommatidial spacings. The gain vs. frequency of a singly illuminated ommatidium is described closely from 0.004 to 0.4 cps by the linear transfer function s0.25; this function also accounts approximately for the measured phase leads, the small signal adaptation following small step inputs, and for Pinter's (1966) earlier low-frequency generator potential data. We suggest that such dynamics could arise from a summation in the generator potential of distributed intensity-dependent relaxation processes along the dendrite and rhabdome. Analysis of the dynamic responses of an eccentric cell with and without simultaneously modulated illumination of particular neighbors indicates an effect equivalent to self-inhibition acting via a first-order low-pass filter with time constant 0.42 sec, and steady-state gain near 4.0. The corresponding filters for lateral inhibition required time constants from 0.35 to 1 sec and effective finite delay of 50–90 msec.

scholarly journals Enhancement in Steady State and Dynamic Performance of Direct Torque Control Induction Motor Drive

2013 ◽  
Vol 64 (5) ◽  
pp. 283-290 ◽  
Author(s):  
Bhoopendra Singh ◽  
Shailendra Jain ◽  
Sanjeet Dwivedi
Keyword(s):  
Steady State ◽  
Dynamic Performance ◽  
Cutoff Frequency ◽  
Direct Torque Control ◽  
Torque Ripple ◽  
Torque Control ◽  
Test Drive ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Abstract An enhancement in dynamic performance of a traditional DTC drive can be achieved by a robust speed control algorithm while the steady state performance depends upon the switching strategy selected for minimization of torque ripples and an efficient flux control loop. In this paper a new torque ripple reduction technique with a modified look up table incorporating a larger number of synthesized non zero active voltage vectors is utilized to overcome the limitations of the conventionally controlled DTC drive. A fuzzy logic based speed controller and a low pass filter with tunable cutoff frequency for flux estimation is proposed in this paper. The proposed study is investigated through simulation and experimentally validated on a test drive.

Application of Kalman Filtering and Smoothing for Airborne Gravimetry

2014 ◽  
Vol 548-549 ◽  
pp. 1192-1195
Author(s):  
Wei Zheng ◽  
Gui Bin Zhang ◽  
Rui Li
Keyword(s):  
Kalman Filter ◽  
Gravity Anomaly ◽  
Kalman Filtering ◽  
Accurate Result ◽  
System Model ◽  
Noise Filtering ◽  
Airborne Gravimetry ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Due to the interference of noise, filtering technology is applied to achieve gravity anomaly for airborne gravimetry. Kalman filtering and smoothing are discussed and implemented for data processing of airborne gravimetry in this paper. Firstly, the algorithms of Kalman filtering and smoothing are introduced. Then, the system model for solving the gravity anomaly is established which is based on the dynamic equation and the hardware design equations. Finally, the result of Kalman filtering and smoothing would be compared with digital FIR low pass filter, and it is proved that Kalman filter and smoother could obtain more accurate result than FIR low pass filter as that the solving error of Kalman filter and smoother is improved within 1 mGal compared with the theory standard obtained by GT-1A software.

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