Prediction Performance of Low Error Rate Adaptive Fading Kalman Filter Due to Temperature Change

Abstract

Global Navigation Satellite System (GNSS) is a system which provides very accurate positioning information. The performance of GNSS depends on several factors such as propagation, interference, denial of full service etc. On the other side, inertial navigation system (INS) can work as a standalone system which does not require any external source support. The main problem in INS is the accumulation of error as time evolves. Apart from that , some inertial measurement units may be succeptible to noise and uncertainty in their output. When GNSS is not functional, it is necessary to have measures to increase the robustness of navigation algorithms and compensate for sensor errors when only INS is used. Additionally , temperature is another important factor that should be taken into account. The INS sensors' response to temperature changes may change and therefore adversely effect the estimation results. Otherwise, we can encounter problems in prediction algorithms to predict states accurately due to the accumulation of errors over time . In this study, we attempted to minimize errors due to measurements with different sensors by using a low-error-rate adaptive fading Kalman filter (LERAFKF). The simulation studies were carried out by using two different IMU’s. One IMU is a temperature-sensitive SDI33 model inertial measurement unit (IMU). The second IMU is Honeywell HG9900C1A IMU sensor with 9 degrees of freedom and resistant to temperature change. The measurement set up has a 2-axis rotating head and a temperature control feature We have proved that LERAFKF provides a robust prediction against temperature changes with two different sensors.

Keywords

• Adaptive fading Kalman Filter
• IMU sensor temperature
• Kalman Filter
• State-Space model
• Inertial measurement unit

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