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Distributed radar tracking using the double debiased distributed Kalman filter

 
: Charlish, A.; Govaers, F.; Koch, W.

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Aerospace and Electronic Systems Society -AESS-:
IEEE Radar Conference, RadarCon 2014. Proceedings : Cincinnati, Ohio, USA, 19 - 23 May 2014
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-2036-5
ISBN: 978-1-4799-2034-1
ISBN: 978-1-4799-2035-8
pp.1124-1129
International Radar Conference (RadarCon) <2014, Cincinnati/Ohio>
English
Conference Paper
Fraunhofer FKIE ()

Abstract
The distributed Kalman filter requires the measurement covariances of remote radar nodes to be known at all radar nodes. This is not possible for a radar network, as the true measurement covariances depend on the radar-target geometry and the fluctuating signal-to-noise ratio. This paper tackles this problem using the double debiased distributed Kalman filter (D3KF) which utilizes a radar model to form a hypothesis on the global covariance. The scheme also transmits debiasing matrices, that account for the mismatch between the assumed and encountered measurement covariance. The scheme is evaluated in a radar network scenario, where it is demonstrated to achieve close to the optimal performance of a centralized Kalman filter (CKF). In contrast to a CKF, the D3KF does not transmit the complete measurement data and is not dependent on the transmission rate of the communication channels to the fusion center.

: http://publica.fraunhofer.de/documents/N-324374.html