To achieve good on-board SAR processing results a precise knowledge of the position of the concerned satellites is essential. For this reason we develop real-time orbit estimation and calibration algorithms which in spite of the small time frame improve the position estimates in an efficient way. Considering future satellite cluster missions (e.g. Cartwheel, Pendulum, Techsat 21, ...), it will be possible to reduce the computing time by splitting of the processing load onto several algorithms that can be implemented in the individual satellites' hardware. The satellites can be considered as a distributed sensor network, which individual (sensor-) nodes have local processors that are used to calculate state estimates using all available data (we want to combine GPS derived and intersatellite distance measurements). A node to node communication is necessary to ensure that no information is lost in order to yield the best possible estimates. The paper specifies advantages and disadvantages of decentralized estimation and compares computational burden and estimation accuracy of decentralized and standard Kalman filter approaches and also analyzes the communication overhead
[1]
Philip Ferguson,et al.
Decentralized Estimation Algorithms for Formation Flying Spacecraft
,
2003
.
[2]
Arthur G. O. Mutambara,et al.
Decentralized Estimation and Control for Multisensor Systems
,
2019
.
[3]
THE INTERFEROMETRIC CARTWHEEL
,
2002
.
[4]
E. Gill.
A Formation Flying Concept for an Along-track Interferometry SAR Mission
,
2003
.
[5]
D. Massonnet,et al.
The interferometric cartwheel: A constellation of passive satellites to produce radar images to be coherently combined
,
2001
.
[6]
Otmar Loffeld,et al.
Orbit estimation of the interferometric cartwheel using an extended linearized Kalman filter
,
2003,
IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).