CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Adaptive Kernel Background Intensity Estimation Based on Local 2D Orientation

Johannes Wintenby ; Daniel Svensson (Institutionen för signaler och system, Signalbehandling)
Proceedings of the 18th International Conference on Information Fusion. Fusion 2015; Washington; United States (2015)
[Konferensbidrag, refereegranskat]

Many target tracking algorithms for radar systems assume homogeneous backgrounds of clutter. However, real backgrounds are rarely homogeneous. By estimating background intensity, and using the estimate in the likelihood measure, the tracking algorithm is given the ability to adapt to the background. In this work, a method for estimating the clutter intensity is introduced. The method is based on locally adaptive Kernel Density Estimation (KDE), where local 2D structure of the background in terms of energy and orientation controls the smoothing properties of the filter kernels. In regions with low clutter intensity, the kernel adopts low-pass characteristics, and the intensity estimate is based on observations from a larger volume. In regions where there are ridges in the clutter intensity, kernels are selected such that smoothing is carried out along ridges instead of across them. Peaks in the clutter intensity are left unsmoothed. The proposed method is compared to other methods on synthetic data. Additionally, a demonstration is given on recorded radar data.



Den här publikationen ingår i följande styrkeområden:

Läs mer om Chalmers styrkeområden  

Denna post skapades 2015-07-25. Senast ändrad 2016-03-23.
CPL Pubid: 219965