scholarly journals Implementasi Algoritma 2 Step Kalman Filter Untuk Mengurangi Noise Pada Estimasi Data Accelerometer

2019 ◽  
Vol 3 (1) ◽  
pp. 142
Author(s):  
Wahyu Sukestyastama Putra
Keyword(s):  
Kalman Filter ◽  
Technological Development ◽  
Estimation Method ◽  
Stable Condition ◽  
Sensor Data ◽  
Average Error ◽  
Sensor Output ◽  
Step Method ◽  
Accelerometer Sensor ◽  
Step Algorithm

An accelerometer is a useful sensor in technological development. Currently, the accelerometer is found on smartphone devices, navigation devices, and wearable devices. However, processing the sensor output signal into data that can be interpreted is not easy. This is because the output of an accelerometer sensor has significant noise. In this study, the authors are interested in developing an estimation method using a Kalman Filter. Kalman filter is an estimator so it is expected that the sensor data are more resistant to noise interference. In this study, the author innovated the 2 step Kalman filter. The study was conducted because the use of 1 step still has noise on the estimation results. Based on the analysis of the algorithm simulation results, it can be concluded that the Kalman filter 2-step algorithm has good performance in estimating the accelerometer sensor output. When compared with the Kalman filter 1 step algorithm, the Kalman filter 2 step algorithm has a smaller average error estimation and is able to achieve a constant/stable condition faster than the Kalman filter 1 step method

A Kalman Filter-Based Queue Length Estimation Method with Low-Penetration Mobile Sensor Data at Signalized Intersections

2018 ◽  
Vol 2672 (45) ◽  
pp. 253-264 ◽  
Author(s):  
Juyuan Yin ◽  
Jian Sun ◽  
Keshuang Tang
Keyword(s):  
Kalman Filter ◽  
Queue Length ◽  
Estimation Method ◽  
Mobile Internet ◽  
Internet Technology ◽  
Sensor Data ◽  
Linear Regression Method ◽  
Mobile Sensor ◽  
Length Estimation ◽  
Queue Lengths

Queue length estimation is of great importance for signal performance measures and signal optimization. With the development of connected vehicle technology and mobile internet technology, using mobile sensor data instead of fixed detector data to estimate queue length has become a significant research topic. This study proposes a queue length estimation method using low-penetration mobile sensor data as the only input. The proposed method is based on the combination of Kalman Filtering and shockwave theory. The critical points are identified from raw spatiotemporal points and allocated to different cycles for subsequent estimation. To apply the Kalman Filter, a state-space model with two state variables and the system noise determined by queue-forming acceleration is established, which can characterize the stochastic property of queue forming. The Kalman Filter with joining points as measurement input recursively estimates real-time queue lengths; on the other hand, queue-discharging waves are estimated with a line fitted to leaving points. By calculating the crossing point of the queue-forming wave and the queue-discharging wave of a cycle, the maximum queue length is also estimated. A case study with DiDi mobile sensor data and ground truth maximum queue lengths at Huanggang-Fuzhong intersection, Shenzhen, China, shows that the mean absolute percentage error is only 11.2%. Moreover, the sensitivity analysis shows that the proposed estimation method achieves much better performance than the classical linear regression method, especially in extremely low penetration rates.

scholarly journals Design of a Fuel Sensor Noise Reduction System Using Kalman Filter

ELKHA ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Rico Bernando Putra ◽  
Suhartati Agoes
Keyword(s):  
Kalman Filter ◽  
Noise Reduction ◽  
Web Application ◽  
Sensor Data ◽  
Average Error ◽  
Sensor Noise ◽  
Reduction System ◽  
Level Data ◽  
Vehicle Information ◽  
Fuel Level

In the field of transportation, telematics is used to obtain vehicle information using Global Positioning System (GPS) technology which is integrated with sensors so that vehicle information can be monitored. One of them is fuel monitoring. The fuel sensor has good accuracy in stationary conditions, but the tability of the data is disturbed when the vehicle is running on an uneven road and causes the tank to shake. This study discusses a fuel sensor noise reduction system using a Kalman filter to overcome the problem of data instability due to shocks. This research aims to reduce noise so that the filter results are closer to the actual result. Filtering is done by changing the process error covariance (Q) and measurement error (R) in the Kalman filter. The fuel sensor noise is simulated using a simulator tank driven by an actuator that can tilt towards the x-axis and the y-axis to resemble the behavior of a vehicle. The fuel level data from the sensor readings are sent by GPS via the cellular network to a server which is then filtered using a web application. From the test results obtained the best filter with (Q) equals 0.1^3 and (R) equals 0.1^3. The average error of the best filter results is 4.73% where this value is 1.92% smaller than the average error of sensor data before filtering, which is 6.65%. Therefore, this proves that the system can reduce noise that occurs in the fuel sensor with the Kalman filter.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

scholarly journals Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1231
Author(s):  
Yunbo Shi ◽  
Juanjuan Zhang ◽  
Jingjing Jiao ◽  
Rui Zhao ◽  
Huiliang Cao
Keyword(s):  
Wavelet Packet ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Parameter Extraction ◽  
Accelerometer Sensor ◽  
Oscillation Phase ◽  
Mems Accelerometer ◽  
Optimal Tree ◽  
Adaptive Decomposition ◽  
Entropy Criterion

High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.

scholarly journals An Initial Value Estimation Method for the Kalman and Extended Kalman Filters in Underground Metal Detection

2019 ◽  
Vol 9 (19) ◽  
pp. 4113 ◽  
Author(s):  
Yadong Wan ◽  
Zhen Wang ◽  
Peng Wang ◽  
Zhiyang Liu ◽  
Na Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Detection Methods ◽  
Polarizability Tensor ◽  
Filter Method ◽  
Excellent Performance ◽  
Initial Value ◽  
Initial State ◽  
Metal Detection ◽  
Value Estimation

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

2021 ◽  
pp. 1-29
Author(s):  
Xiongbin Peng ◽  
Yuwu Li ◽  
Wei Yang ◽  
Akhil Garg
Keyword(s):  
Kalman Filter ◽  
Working Conditions ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Influential Factor ◽  
Recursive Least Squares ◽  
Maximum Deviation ◽  
Soc Estimation ◽  
Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

scholarly journals A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

scholarly journals Practical Endurance Estimation for Minimizing Energy Consumption of Multirotor Unmanned Aerial Vehicles

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2221 ◽  
Author(s):  
Myeong-hwan Hwang ◽  
Hyun-Rok Cha ◽  
Sung Yong Jung
Keyword(s):  
Drag Coefficient ◽  
Unmanned Aerial Vehicles ◽  
Estimation Method ◽  
Flight Test ◽  
Flight Time ◽  
Average Error ◽  
Practical Applications ◽  
Aerial Vehicles ◽  
Vehicle Shape ◽  
Battery Discharge

The practically applicable endurance estimation method for multirotor unmanned aerial vehicles (UAVs) using a battery as a power source is proposed. The method considers both hovering and steady-level flights. The endurance, thrust, efficiency, and battery discharge are determined with generally available data from the manufacturer. The effects of the drag coefficient related to vehicle shape and payload weight are examined at various forward flight speeds. As the drag coefficient increases, the optimum speed at the minimum required power and the maximum endurance are reduced. However, the payload weight causes an opposite effect, and the optimal flying speed increases with an increase in the payload weight. For more practical applications for common users, the value of S × Cd is determined from a preliminary flight test. Given this value, the endurance is numerically estimated and validated with the measured flight time. The proposed method can successfully estimate the flight time with an average error of 2.3%. This method would be useful for designers who plan various missions and select UAVs.

Localization and Map Building of Laser Sensor AGV Based on Kalman Filter

2013 ◽  
Vol 712-715 ◽  
pp. 1938-1943
Author(s):  
Li Xiao Guo ◽  
Fan Kun ◽  
Wen Jun Yan
Keyword(s):  
Kalman Filter ◽  
Sensor Data ◽  
Laser Sensor ◽  
Map Building ◽  
Localization Accuracy ◽  
Key Technology ◽  
Navigation Algorithm ◽  
Automatic Guided Vehicle ◽  
Kalman Filter Algorithm ◽  
Navigation Method

Localization and navigation algorithm is the key technology to determine whether or not an AGV (automatic guided vehicle) can run normally. In this paper, we summarize the popular navigation technologies first and then focus on the positioning principle of Nav200 which is adopted in our AGV system. Besides that, the map building method and the layout of the reflective board is also introduced briefly. This paper introduced two navigation methods. The traditional navigation method only uses the sensor data and the electronic map to guide AGV. To improve positioning accuracy, we use the Kalman filter to minimize the error of localization sensor. At last some simulation work was done, the results shows that the localization accuracy was improved by adopting Kalman filter algorithm.

scholarly journals Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments

2011 ◽  
Vol 15 (8) ◽  
pp. 2437-2457 ◽  
Author(s):  
S. Nie ◽  
J. Zhu ◽  
Y. Luo
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Estimation Method ◽  
State Parameter ◽  
Hydraulic Parameters ◽  
Constrained Estimation ◽  
Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

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