The AGILE on-board Kalman filter
โ Scribed by A. Giuliani; V. Cocco; S. Mereghetti; C. Pittori; M. Tavani
- Publisher
- Elsevier Science
- Year
- 2006
- Tongue
- English
- Weight
- 341 KB
- Volume
- 568
- Category
- Article
- ISSN
- 0168-9002
No coin nor oath required. For personal study only.
โฆ Synopsis
On-board reduction of particle background is one of the main challenges of space instruments dedicated to gamma-ray astrophysics. We present in this paper a discussion of the method and main simulation results of the on-board background filter of the Gamma-Ray Imaging Detector (GRID) of the AGILE mission. The GRID is capable of detecting and imaging with optimal point spread function gamma-ray photons in the range 30 MeV-30 GeV. The AGILE planned orbit is equatorial, with an altitude of 550 km. This is an optimal orbit from the point of view of the expected particle background. For this orbit, electrons and positrons of kinetic energies between 20 MeV and hundreds of MeV dominate the particle background, with significant contributions from high-energy (primary) and lowenergy protons, and gamma-ray albedo-photons. We present here the main results obtained by extensive simulations of the on-board AGILE-GRID particle/photon background rejection algorithms based on a special application of Kalman filter techniques. This filter is applied (Level-2) sequentially after other data processing techniques characterizing the Level-1 processing. We show that, in conjunction with the Level-1 processing, the adopted Kalman filtering is expected to reduce the total particle/albedo-photon background rate to a value (p10-30 Hz) that is compatible with the AGILE telemetry. The AGILE on-board Kalman filter is also effective in reducing the Earth-albedo-photon background rate, and therefore contributes to substantially increase the AGILE exposure for celestial gamma-ray sources.
๐ SIMILAR VOLUMES
In a recent paper Hamilton et al. (1973) evaluated the use of a Kalman filter in a multivariable process industry feedback control system. In addition to analyzing the sensitivity of the Kalman filter to various parameters, they compared its performance to that of an exponential filter commonly used