scholarly journals Novel Kalman Filter Algorithm for Statistical Monitoring of Extensive Landscapes with Synoptic Sensor Data

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 23589-23617 ◽  
Author(s):  
Raymond Czaplewski
Keyword(s):  
Kalman Filter ◽  
Sensor Data ◽  
Statistical Monitoring ◽  
Kalman Filter Algorithm

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.

Kalman Filter Algorithm Design for HC-SR04 Ultrasonic Sensor Data Acquisition System

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Adnan Rafi Al Tahtawi
Keyword(s):  
Kalman Filter ◽  
Algorithm Design ◽  
Good Control ◽  
Ultrasonic Sensor ◽  
Noise Measurement ◽  
Sensor Data ◽  
External Signal ◽  
Process Noise ◽  
Range Sensor ◽  
Kalman Filter Algorithm

In the control system application, the existence of noise measurement may impact on the performance degradation. The noise measurement of the sensor is produced due to several reasons, such as the low specification, external signal disturbances, and the complexity of measured state. Therefore, it should be avoided to achieve the good control performance. One of the solutions is by designing a signal filter. In this paper, the design of Kalman Filter (KF) algorithm for ultrasonic range sensor is presented. KF algorithm is designed to overcome the existence of noise measurement on the sensor. The type of ultrasonic range sensor used is HC-SR04 which is capable to detect the distance from 2 cm to 400 cm. The discrete KF algorithm is implemented using ATMega 328p microcontroller on Arduino Uno board. The algorithm is then tested with different three covariance values of process noise. The test result shows that the KF algorithm is able to reduce the measurement noise of the ultrasonic sensor. The analysis of variance conducted shows that the smaller value of covariance matrix of the process and measured noises, the better filtering process performed. However, this results in a longer generated response time. Thus, an optimization is required to obtain the best filtering performance.

Design of Two-Wheeled Self-Balanced Robot Based on Kalman Filter

2014 ◽  
Vol 971-973 ◽  
pp. 444-449
Author(s):  
Ting Gui Li
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
High Efficiency ◽  
Inertial Sensor ◽  
Optimal Estimation ◽  
Sensor Data ◽  
Wheeled Robot ◽  
Kalman Filter Algorithm ◽  
Balancing Robot ◽  
Balancing Control

Using the chip MC9S12XS128, two-wheeled self-balancing robot control system is designed. Its posture information is detected by accelerometer MMA7260 and gyro NEC-03, multi inertial sensor data fusion is realized by Kalman filter, posture data optimal estimation is gotten, and the accuracy of posture sensing system is improved. Using integral separation PID control algorithm, controlling the left and right motors are accelerated or decelerated, self-balancing control of two-wheeled robot is achieved. The experimental results show that, using the hardware platform MC9S12XS128, Kalman filter algorithm has high efficiency and posture data fusion is accurate and reliable, requirements which are posture optimal estimation and inclination data real-time feedback are met, and the system is stable and can accurately and quickly realize self-balancing control of two-wheeled robot.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

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