Detection of Apogee with Kalman Filter for Flight Computer of Model Rockets

Abstract

Raw sensor values are often noisy and unreliable in high sampling frequencies. Accuracy is important when it comes to guidance, navigation, and control of vehicles more so spacecraft or ships. We made use of the Kalman filter to get stable values throughout our flight. The purpose of this study is to come up with a proper filtering method that accurately estimates the distance, velocity, and acceleration from the sensor readings, BMP180 altitude readings, and MPU6050 Z-axis acceleration readings. We come up with a physical model that describes our system dynamics to get the most out of our filter. With this data, we made three algorithms to accurately predict our apogee for parachute ejection. The first one checks if the altitude readings start decreasing after launch by 0.1 meters. The second one checks if the velocity value is within an envelope of -1 m/s and +1 m/s after launch while the third one checks if the acceleration value is within an envelope of -1 m/s2 and +1 m/s2 also after a successful launch was detected. With this system, we were able to accurately predict apogee since they worked in tandem. Keywords—Kalman filter, sensor noise, state estimation.