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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
International Radar Conference (RadarCon) <2014, Cincinnati/Ohio>
Conference Paper
Fraunhofer FKIE ()

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.