The main disadvantage of an Inertial Navigation System is a low accuracy due to noise, bias, and drift error in the inertial sensor. This research aims to develop the accelerometer and gyroscope sensor for quadrotor navigation system, bias compensation, and Zero Velocity Compensation (ZVC). Kalman Filter is designed to reduce the noise on the sensor while bias compensation and ZVC are designed to eliminate the bias and drift error in the sensor data. Test results showed the Kalman Filter design is acceptable to reduce the noise in the sensor data. Moreover, the bias compensation and ZVC can reduce the drift error due to integration process as well as improve the position estimation accuracy of the quadrotor. At the time of testing, the system provided the accuracy above 90 % when it tested indoor.
This study aims to develop a Kalman filter algorithm in order to reduce the accelerometer sensor noise as effectively as possible. The accelerometer sensor is one part of the Inertial Measurement Unit (IMU) used to find the displacement distance of an object. The method used is modeling the system to model the accelerometer system to form mathematical equations. Then the state space method is used to change the system modeling to the form of matrix operations so that the process of the data calculating to the Kalman Filter algorithm is not too difficult. It also uses the threshold algorithm to detect the sensor's condition at rest. The present study had good results, which of the four experiments obtained with an average accuracy of 93%. The threshold algorithm successfully reduces measurement errors when the sensor is at rest or static so that the measurement results more accurate. The developed algorithm can also detect the sensor to move forward or backward.
In an unmanned aircraft vehicle, a navigation system is needed to calculate its orientation and translation. The navigation system can utilize data from the accelerometer, gyroscope, magnetometer, and GPS. The orientation can be precisely calculated from the accelerometer and magnetometer data when the sensor is in a static state. Meanwhile, under dynamic conditions, the orientation can be more precisely calculated from the gyroscope data. In order to obtain the robust navigation system, a data fusion based on Kalman filter is built to calculate the orientation from the accelerometer, gyroscope, and magnetometer. The Kalman filter trusts more in the data from the accelerometer and magnetometer when the UAV is static and trusts more in to the gyroscope data when the UAV is in dynamic conditions. Meanwhile, the UAV translation is obtained by performing data fusion of the accelerometer data with location data from the GPS sensor. The Kalman filter combines data from the accelerometer and GPS when available, otherwise trusts in data from the accelerometer only. The trust level shifting is done by changing the measurement noise covariance. The data fusion based on Kalman filter provides more accurately the orientation and translation data. The orientation as a result of the calculation from the gyroscope has an average error of 18.12%, while the orientation as a result of the accelerometer and magnetometer has an error of 1.3%. By using Kalman filter-based data fusion, the error of the orientation decreases to 0.87%
Rotation angle estimates are often required and applied to the dynamics of spacecraft, UAVs, robots, underwater vehicles, and other systems before control. IMU is an electronic module that is used as an angle estimation tool but has noise that can reduce the accuracy of the estimation. This study aims to develop an estimation model for the angle of rotation of a rigid body based on the IMU-gyroscope sensor on a smartphone using a Kalman filter. The estimation model is developed in a simple dynamic equation of motion in state-space. Kalman filters are designed based on system dynamics models to reduce noise in sensor data and improve measurement estimation results. Simulations are carried out with software to investigate the accuracy of the developed estimation algorithm. Experiments were carried out on several smartphone rotations during the roll, pitch, and yaw. Then, the experimental data obtained is analyzed for accuracy by comparing the built-in algorithms on smartphones. Based on the experimental results, the accuracy rate of estimation angle is 94% before going through the Kalman filter and an accuracy level of above 98% after going through the Kalman filter for every rotation on the x-axis, y-axis, and z-axis.
The navigation system on quadrotor is important to maintain stability and determine its own position when flying autonomously. The GPS can provide the position measurement, but it has limitations in the specific environments and cannot provide the orientation information. This study aims to design the navigation system for quadrotor based on IMU sensor with Kalman filters using the state space model. The system model was developed using Matlab software. Kalman filter is designed to estimate the navigation data and eliminate noise on the sensor so that it can improve the measurement accuracy. The test results showed that the system model can provide orientation estimation and translation estimation of the quadrotor, while the Kalman filter model is acceptable to reduce noise on the sensor's raw data. When tested indoors, the system can provide the measurement accuracy above 90%.
Radar technology at several airports is still using flightradar24 as a source of information, and building an ADS-B station is expensive. However, the flightradar24 has several weaknesses, among which is that if the user wants to display more information, the user is required to pay periodically or subscriptions, and there is delay due to the process of data that requires the Internet connection. With a concept of receiver ads-b based RTL-SDR R820T2, a low cost receiver ads-b with the results can receive an ads-b signal without delay and can receive data from an airplane. But there is a weakness in rtl-b receivers based RTL-SDR R820T2, because it doesn't explain and can't know how far the receiver can receive signals and target parameters data from the aircraft. Thus on this research a receiver ads-b using RTL-SDR R820T2, with a low-noise amplification and an ads-b antenna 1090 MHZ in the hopes of knowing how far the aircraft's target range is from the receiver and knowing how far the receiver's range of data signals the target parameters. By performing some step-by-step testing of the design. The designed receiver ads-b USES low noise amplification with an ads-b antenna 1090 MHZ capable of receiving data and target parameters ads-b for 284 km on adsbSCOP software range and 287.63 km mathematically.
Improved a quadrotor technology that is capable of fast maneuvering requires accurate attitude estimation or navigation to maintain a quadrotor stability. The GPS can provide position measurements with an accuracy of several meters, but cannot provide orientation information directly. This study aims to design a quadrotor attitude navigation system based on IMU sensors on AR Drone 2.0 with a Kalman filter using the equation of state space model. The system model was developed using the Matlab software. The Kalman filter is designed as an estimator to reduce noise on the sensor so that it can improve measurement accuracy. The test results showed that the system model can be used to estimated the orientation angle and shift of the quadrotor, while the Kalman filter that designed can reduce noise in the sensor data. At the time of tested, the system provided the measurement accuracy of above 90% when tested indoor.
The fire hazard due to an LPG cylinder explosion can be triggered from a gas cylinder leak. Not infrequently these incidents lead to loss of life and property loss. This study proposes the design of an LPG gas leak monitoring system and early warning of fire hazards. The system is designed to be integrated with the internet network through the Internet of Things (IoT) platform. The MQ-6 sensor module is used to detect LPG gas leaks and a fire sensor to detect fire. The sensor is installed close to the gas outlet source to ensure gas leakage and fire. The data from the sensor is read by the microprocessor and sent to the web server via the interface device. When the concentration of LPG gas in the air exceeds a certain level or a fire is detected, the microprocessor gives a command to turn on the alarm and sends a hazard notification to the smartphone. Hazard notifications, gas concentrations and status of hazard conditions can be accessed in real-time on Android-based smartphones. The results of the tests that have been carried out show that this system can provide information on hazard notification messages and gas concentration values in the range of 1000 - 10000 ppm. The fire detector in this system can detect fires up to a distance of 110 cm from the source of the fire. Then, hazard notifications and gas concentration levels can be accessed via a smartphone with the Kodular web application in the form of a real-time graphic display.
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