Autonomous Orbit Determination System of Navigation Satellite Based on Spaceborne GPS Technology

In recent years, many low-orbit satellites have been widely used in the field of scientific research and national defense in China. In order to meet the demand of high-precision satellite orbit in China’s space, surveying and mapping, and other related fields, navigation satellites are of great significance. The UKF (unscented Kalman filter) method is applied to space targets’ spaceborne GPS autonomous orbit determination. In this paper, the UKF algorithm based on UT transformation is mainly introduced. In view of the situation that the system noise variance matrix is unknown or the dynamic model is not accurate, an adaptive UKF filtering algorithm is proposed. Simulation experiments are carried out with CHAMP satellite GPS data, and the results show that the filtering accuracy and stability are improved, which proves the algorithm’s effectiveness. The experimental results show that the Helmert variance component estimation considering the dynamics model can solve the problem of reasonable weight determination of BDS/GPS observations and effectively weaken the influence of coarse error and improve the accuracy of orbit determination. The accuracy of autonomous orbit determination by spaceborne BDS/GPS is 1.19 m and 2.35 mm/s, respectively.

Designing a Mouse for Disabilities Using the MPU-6050 Sensor with the Kalman Filter Method as a Noise Reducer

Problems that are often faced by people with physical disabilities are those who have limited hands, one of which is when they will use the computer. His inability to grip and use the mouse is often a barrier in using the computer. The purpose of the design of the tool is to provide facilities for people with disabilities to be able to use a mouse that will be moved based on head movements without noise interference caused by the MPU-6050 sensor. The results of the tests carried out show that designing a mouse with the MPU-6050 sensor has been successfully carried out, the MPU-6050 sensor by implementing a kalman filter as a noise reducer on the X axis has an accuracy value with an average error percentage of 0.09% and at Y angle is 0.12%. Data transmission from the mouse to the computer is done wirelessly using bluetooth HC-05 can receive data well as far as 12.5 meters with an error percentage of 0%. The button on the mouse that functions to perform the left click function when the button is bitten 1x, right click when the button is bitten 2x and click and hold to do a left click 2x or double click can run according to the command, has a 100% success rate.

Drought analysis over northern Thailand

In this research, the Kalman filter method was applied for correcting precipitations simulated by a high-resolution regional climate model named Non-hydrostatic Regional Climate Model (NHRCM) during the period of 1980-1999. The improved average monthly precipitations were close to the stational observations. To reduce systematic error, the Kalman filter method was also applied to simulated monthly precipitations during the future period of 2080-2099. They were analysed to evaluate drought conditions during March-April (out rainy season) and June-July (in rainy season) by using Standardized Precipitation Index, SPI. Preliminary Analysis shows that drought conditions during both periods slightly mitigate. Furthermore, the drought over upper northern Thailand was found in the wettest month during the southwest monsoon period, September. The other months during the monsoon active are wetter than the period of 1980-1999.

Fuzzy logic approach for detecting drivers’ drowsiness based on image processing and Video streaming

<p>The driver's face is detected and extracted extra features of the eyes and mouth areas. Start the analysis process to determine the status of these parameters. Second, the Kalman filter method is used to track and manipulate the difference in the size and orientation of the captured features. The technique checks all image states such as brightness, shadows, and clarity. Third, the blur control system provides different alert sounds based on the information tracked from the face, eyes, and mouth. The proposed method uses real-time data recorded by a webcam tool in the MatlabR2016a environment. The data sample contains videos of different users of different races, whether they wear glasses, gender, and various lighting backgrounds. The proposed system achieved an accuracy of up to 94.5% in the detection driver status.</p>

Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia

Many forecasting methods have been used for forecasting rainfall data. Kalman Filter is one of the forecasting methods that could give better forecasts. To our knowledge, the Kalman Filter method has not been used to forecast rainfall data in Makassar, Indonesia. This study aims to provide more precise forecasts for rainfall data in Makassar, Indonesia by using Autoregressive Integrated Moving Average (ARIMA) and Kalman Filter methods. Rainfall data from January 2010 to December 2020 were used. The best model selection is based on the smallest Mean Absolute Percentage Error (MAPE) value. The results showed that the best ARIMA model is ARIMA(0,1,1)(0,1,1)12 with MAPE is 111.48, while MAPE value by using the Kalman Filter algorithm is 47.00 indicating that Kalman Filter has better prediction than ARIMA model.

Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).

Fuzzy logic approach for detecting drivers’ drowsiness based on image processing and Video streaming

<p>The driver's face is detected and extracted extra features of the eyes and mouth areas. Start the analysis process to determine the status of these parameters. Second, the Kalman filter method is used to track and manipulate the difference in the size and orientation of the captured features. The technique checks all image states such as brightness, shadows, and clarity. Third, the blur control system provides different alert sounds based on the information tracked from the face, eyes, and mouth. The proposed method uses real-time data recorded by a webcam tool in the MatlabR2016a environment. The data sample contains videos of different users of different races, whether they wear glasses, gender, and various lighting backgrounds. The proposed system achieved an accuracy of up to 94.5% in the detection driver status.</p>