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Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion

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dc.contributor.author Subramaniam, A
dc.contributor.author Sindhu, N
dc.contributor.author Tandon, S. N
dc.contributor.author Kameswara Rao, N
dc.contributor.author Postma, J
dc.contributor.author Cote, Patrick
dc.contributor.author Hutchings, J. B
dc.contributor.author Ghosh, S. K
dc.contributor.author George, K
dc.contributor.author Girish, V
dc.contributor.author Mohan, Rekhesh
dc.contributor.author Murthy, J
dc.contributor.author Sankarasubramanian, K
dc.contributor.author Stalin, C. S
dc.contributor.author Sutaria, F. K
dc.contributor.author Mondal, Chayan
dc.contributor.author Snehalata Sahu
dc.date.accessioned 2020-11-20T13:29:10Z
dc.date.available 2020-11-20T13:29:10Z
dc.date.issued 2016-07
dc.identifier.citation Proceedings of the SPIE, Vol. 9912, pp. 99126W-1 - 99126W-10 en_US
dc.identifier.issn 0277-786X
dc.identifier.uri http://prints.iiap.res.in/handle/2248/7339
dc.description Restricted Access © SPIE--The International Society for Optical Engineering http://dx.doi.org/10.1117/12.2234255 en_US
dc.description.abstract We describe the characterization and removal of noises present in the Inertial Measurement Unit (IMU) MPU- 6050, which was initially used in an attitude sensor, and later used in the development of a pointing system for small balloon-borne astronomical payloads. We found that the performance of the IMU degraded with time because of the accumulation of different errors. Using Allan variance analysis method, we identified the different components of noise present in the IMU, and verified the results by the power spectral density analysis (PSD). We tried to remove the high-frequency noise using smooth filters such as moving average filter and then Savitzky Golay (SG) filter. Even though we managed to filter some high-frequency noise, these filters performance wasn't satisfactory for our application. We found the distribution of the random noise present in IMU using probability density analysis and identified that the noise in our IMU was white Gaussian in nature. Hence, we used a Kalman filter to remove the noise and which gave us good performance real time. en_US
dc.language.iso en en_US
dc.publisher SPIE-The International Society for Optical Engineering en_US
dc.subject Balloon experiment en_US
dc.subject Attitude sensor en_US
dc.subject Pointing system en_US
dc.subject MEMS sensors en_US
dc.title Noise modeling and analysis of an IMU-based attitude sensor: improvement of performance by filtering and sensor fusion en_US
dc.type Article en_US

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