scholarly journals Denoising MAX6675 reading using Kalman filter and factorial design

2021 ◽  
Vol 11 (5) ◽  
pp. 3818
Author(s):  
Reski Septiana ◽  
Ibnu Roihan ◽  
Raldi A. Koestoer
Keyword(s):  
Kalman Filter ◽  
Measurement Error ◽  
Factorial Design ◽  
Low Cost ◽  
Optimum Combination ◽  
Process Noise ◽  
Measurement Results ◽  
Original Measurement ◽  
Time Gap ◽  
Input Variables

This paper aims to tune the Kalman filter (KF) input variables, namely measurement error and process noise, based on two-level factorial design. Kalman filter then was applied in inexpensive temperature-acquisition utilizing MAX6675 and K-type thermocouple with Arduino as its microprocessor. Two levels for each input variable, respectively, 0.1 and 0.9, were selected and applied to four K-type thermocouples mounted on MAX6675. Each sensor with a different combination of input variables was used to measure the temperature of ambient-water, boiling water, and sudden temperature drops in the system. The measurement results which consisted of the original and KF readings were evaluated to determine the optimum combination of input variables. It was found that the optimum combination of input variables was highly dependent on the system's dynamics. For systems with relatively constant dynamics, a large value of measurement error and small value of process noise results in higher precision readings. Nevertheless, for fast dynamic systems, the previous input variables' combination is less optimal because it produced a time-gap, which made the KF reading differ from the original measurement. The selection of the optimum input combination using two-level factorial design eased the KF tuning process, resulting in a more precise yet low-cost sensor.

scholarly journals Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 364 ◽  
Author(s):  
Ming Xia ◽  
Chundi Xiu ◽  
Dongkai Yang ◽  
Li Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Ultrasonic Sensor ◽  
Measurement Unit ◽  
Positioning Error ◽  
Process Noise ◽  
Pedestrian Navigation ◽  
Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

scholarly journals Development of Ultrasonic Anemometer Using HC-SR04 with Kalman Filter Based on Microcontroller Integrated IoT

2020 ◽  
Vol 202 ◽  
pp. 15011
Author(s):  
Ivan Adi Kuncara ◽  
Jatmiko Endro Suseno ◽  
Setyawan Agus ◽  
Isnain Gunadi
Keyword(s):  
Kalman Filter ◽  
Measurement Error ◽  
Wind Speed ◽  
Wind Tunnel ◽  
Low Cost ◽  
Analog Signal ◽  
Analog Filters ◽  
Measurement Resolution ◽  
Ultrasonic Anemometer ◽  
Efficient Type

Measurement of wind speed using an anemometer is very important in various fields. The most effective and efficient type of anemometer is the ultrasonic anemometer. This research develops an Arduino-based ultrasonic anemometer using the HC-SR04 sensor with a Kalman filter to reduce noise and integrated IoT with the Blynk application on a smartphone. The advantage of ultrasonic anemometer is not having analog signal conditioning and analog filters, thereby reducing the possibility of having noise, low cost, and can be monitored remotely. The ultrasonic anemometer has been calibrated on the wind tunnel and has a measurement resolution of 0.10 m/s and a measurement error of 0.14 m/s.

Low-Cost Heat Insulating Film Design for BIPV Modules Based on Multi-Layer Inorganic Materials

2012 ◽  
Vol 452-453 ◽  
pp. 1424-1428
Author(s):  
Han Min Tian ◽  
Li Jia Guo ◽  
Wen Feng Duan ◽  
Rui Xia Yang ◽  
Feng Lan Tian
Keyword(s):  
Refractive Index ◽  
Visible Light ◽  
Absorption Rate ◽  
Heat Insulation ◽  
Low Cost ◽  
Heat Rate ◽  
Optimum Combination ◽  
Inorganic Materials ◽  
Tempered Glass ◽  
Glass Panel

By analyzing the transmitionce and heat rate of insulating antireflection films conposed by refractive-index adjustable SiO2 layer and TiO2 layers, the optimum combination of antireflection films of BIPV is obtained. The absorption rate at the ultraviolet part that wavelenght excessive inadequate 400nm of the optimized fils is 99.9%, which are directly designed on the surface of the low iron tempered glass panel of BIPV, and in the wavelength range 400nm-800nm, the visible light transmitionce rate is up to 99.5%, and the heat that wavelenght excessive 800nm is reflected of 20%. For the multilayer heat insulation films are composed with the same kind of material while with different refractive indexes, there is no projecting stress between these films and no constraints during the production process of different films for the possible low cost heat insulating of BIPV.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

Online Dynamic Response Reconstruction in the Presence of Observation Outliers

2021 ◽  
Author(s):  
Chaodong Zhang ◽  
Jian’an Li ◽  
Youlin Xu
Keyword(s):  
Kalman Filter ◽  
Dynamic Response ◽  
Numerical Study ◽  
Frame Structure ◽  
System Model ◽  
Measurement Information ◽  
Model Errors ◽  
Process Noise ◽  
Robust Kalman Filter ◽  
Response Reconstruction

Previous studies show that Kalman filter (KF)-based dynamic response reconstruction of a structure has distinct advantages in the aspects of combining the system model with limited measurement information and dealing with system model errors and measurement Gaussian noises. However, because the recursive KF aims to achieve a least-squares estimate of state vector by minimizing a quadratic criterion, observation outliers could dramatically deteriorate the estimator’s performance and considerably reduce the response reconstruction accuracy. This study addresses the KF-based online response reconstruction of a structure in the presence of observation outliers. The outlier-robust Kalman filter (OKF), in which the outlier is discerned and reweighted iteratively to achieve the generalized maximum likelihood (ML) estimate, is used instead of KF for online dynamic response reconstruction. The influences of process noise and outlier duration to response reconstruction are investigated in the numerical study of a simple 5-story frame structure. The experimental work on a simply-supported overhanging steel beam is conducted to testify the effectiveness of the proposed method. The results demonstrate that compared with the KF-based response reconstruction, the proposed OKF-based method is capable of dealing with the observation outliers and producing more accurate response construction in presence of observation outliers.

scholarly journals Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3270 ◽  
Author(s):  
Hao Cai ◽  
Zhaozheng Hu ◽  
Gang Huang ◽  
Dunyao Zhu ◽  
Xiaocong Su
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Kinematic Model ◽  
Intelligent Vehicles ◽  
Monocular Vision ◽  
Measurement Unit ◽  
High Definition ◽  
Position Information ◽  
Vehicle Localization ◽  
Lateral Distance

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.

The Effect of Air Injection Method on the Airlift Pump Performance

2012 ◽  
Vol 134 (11) ◽  
Author(s):  
Dong Hu ◽  
Chuan-Lin Tang ◽  
Shu-Peng Cai ◽  
Feng-Hua Zhang
Keyword(s):  
Flow Rate ◽  
Volume Flow Rate ◽  
Low Cost ◽  
Air Injection ◽  
Water Volume ◽  
Pump Design ◽  
Pump Performance ◽  
Dimensionless Equation ◽  
Measurement Results ◽  
Injection Methods

With simple structure, excellent reliability, low cost, no restriction at depth of water, and easy control and operation, airlift pumps have special advantage in borehole hydraulic jet mining, river dredging and deep sea mining. To clarify the mechanism and process of action of air injection methods on air lift performance, and to enhance lifting capacity, the pump performance of a small airlift system in transporting river sands is investigated experimentally in the present study. The results are as the follows. The influences of air exit ports on water volume flow rate, mass flow rate of solids and lifting efficiency are studied and found to be very low when the number of air exit ports exceeds 3. The pump design show best pumping capability for water and solids at higher air flow rates, but the lifting efficiency is then very low. In addition, a dimensionless equation which describes the flows in the pipe is presented based on the Bernoulli equation, and compared with measurement results in the dimensionless form, which are nearly in good agreement with each other for all the arrangements of air exit ports and are basically contained within ±18% of the theoretical curve. The results are important for understanding the mechanism of airlift pumps and enriching multiphase flow theory.

scholarly journals Development of a Low-Cost Data Acquisition System for Very Short-Term Photovoltaic Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6075
Author(s):  
Guilherme Fonseca Bassous ◽  
Rodrigo Flora Calili ◽  
Carlos Hall Barbosa
Keyword(s):  
Low Cost ◽  
Renewable Energy Sources ◽  
Relevant Information ◽  
Acquisition System ◽  
Time Operation ◽  
Detection Point ◽  
Real Time Operation ◽  
Forecast Models ◽  
Input Variables ◽  
Power Forecasting

The rising adoption of renewable energy sources means we must turn our eyes to limitations in traditional energy systems. Intermittency, if left unaddressed, may lead to several power-quality and energy-efficiency issues. The objective of this work is to develop a working tool to support photovoltaic energy forecast models for real-time operation applications. The current paradigm of intra-hour solar-power forecasting is to use image-based approaches to predict the state of cloud composition for short time horizons. Since the objective of intra-minute forecasting is to address high-frequency intermittency, data must provide information on and surrounding these events. For that purpose, acquisition by exception was chosen as the guiding principle. The system performs power measurements at 1 Hz frequency, and whenever it detects variations over a certain threshold, it saves the data 10 s before and 4 s after the detection point. A multilayer perceptron neural network was used to determine its relevance to the forecasting problem. With a thorough selection of attributes and network structures, the results show very low error with R2 greater than 0.93 for both input variables tested with a time horizon of 60 s. In conclusion, the data provided by the acquisition system yielded relevant information for forecasts up to 60 s ahead.

scholarly journals Performance Analysis of Computational Intelligence Correction

2021 ◽  
Author(s):  
Nalini Arasavali ◽  
Sasibhushanarao Gottapu
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Computational Intelligence ◽  
A Priori ◽  
Covariance Matrices ◽  
System Model ◽  
Process Noise ◽  
Navigation Algorithm ◽  
Modified Kalman Filter ◽  
Measurement Noises

Abstract Kalman filter (KF) is a widely used navigation algorithm, especially for precise positioning applications. However, the exact filter parameters must be defined a priori to use standard Kalman filters for coping with low error values. But for the dynamic system model, the covariance of process noise is a priori entirely undefined, which results in difficulties and challenges in the implementation of the conventional Kalman filter. Kalman Filter with recursive covariance estimation applied to solve those complicated functional issues, which can also be used in many other applications involving Kalaman filtering technology, a modified Kalman filter called MKF-RCE. While this is a better approach, KF with SAR tuned covariance has been proposed to resolve the problem of estimation for the dynamic model. The data collected at (x: 706970.9093 m, y: 6035941.0226 m, z: 1930009.5821 m) used to illustrate the performance analysis of KF with recursive covariance and KF with computational intelligence correction by means of SAR (Search and Rescue) tuned covariance, when the covariance matrices of process and measurement noises are completely unknown in advance.

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