Feedback State Estimation for Multi-rotor Drones Stabilisation Using Low-Pass Filter and a Complementary Kalman Filter

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.

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

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v6.i1.pp92-99 ◽

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

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.

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State estimation for dynamic weighing using Kalman filter

10.26686/wgtn.17142506 ◽

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

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.

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Application of Kalman Filtering and Smoothing for Airborne Gravimetry

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.1192 ◽

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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State estimation for dynamic weighing using Kalman filter

10.26686/wgtn.17142506.v1 ◽

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

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.

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

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.562-565.426 ◽

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.

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State Estimation of RC Filters using Unscented Kalman Filter

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i7512.079920 ◽

2020 ◽

Vol 9 (9) ◽

pp. 91-96

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Estimation Error ◽

State Space Model ◽

Taylor Series Expansion ◽

System Model ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass ◽

High Pass

This paper implements unscented Kalman filter (UKF) for output voltage estimation of RC low pass filter (LPF) and high pass filter (HPF). At first, the state space model has been obtained using Kirchhoff’s current law (KCL). The performance of UKF has been compared with extended Kalman filter (EKF). The simulation results validate the superiority of UKF over EKF as the estimation error is smaller using UKF as compared to the EKF method. As the UKF uses unscented transform (UT) and EKF uses Taylor series expansion for linearization purpose, linearization error is smaller in UKF as compared to EKF method. Also, UKF implementation has the advantage that it does not require Jacobian computation of nonlinear system model.

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