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Non-linear and non-Gaussian state estimation using log-homotopy based particle flow filters

: Khan, M.A.; Ulmke, M.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Aerospace and Electronic Systems Society -AESS-; International Society of Information Fusion -ISIF-:
9th Workshop on Sensor Data Fusion: Trends, Solutions, and Applications, SDF 2014 : 8-10 October 2014, Bonn, Germany
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-7388-0
ISBN: 978‐1-­4799‐7387‐3
6 pp.
Workshop on Sensor Data Fusion (SDF) <9, 2014, Bonn>
Conference Paper
Fraunhofer FKIE ()

Non-linear filtering is a challenging task and generally no analytical solution is available. Sub-optimal methods like particle filters are employed to approximate the conditional probability densities. These methods are expensive in terms of the processing requirements. Recently proposed log homotopy based particle flow filter, also known as Daum-Huang filter (DHF) provides an alternative way of non-linear state estimation. Based on different assumptions, several versions of DHF have been derived. Superior performance has been reported for their use in several non-linear but Gaussian filtering problems. In this paper we compare the performance of different versions of DHF for a coupled, non-linear and non-Gaussian system model. Results show that recently proposed non zero diffusion DHF perform better than previous versions of DHF.