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Paper Information

Journal:   NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR   SUMMER 2016 , Volume 14 , Number 2; Page(s) 93 To 104.
 
Paper: 

PARTICLE FILTER WITH ADAPTIVE OBSERVATION MODEL

 
 
Author(s):  HAERI H.*, SADOGHI YAZDI H.
 
* DEPT. OF COMP. SCI., EQBALLAHOURI INSTITUTE OF HIGHER EDUCATION, MASHHAD, I.R. IRAN
 
Abstract: 

Particle filter is an effective tool for the object tracking problem. However, obtaining an accurate model for the system state and the observations is an essential requirement. Therefore, one of the areas of interest for the researchers is estimating the observation function according to the learning data. The observation function can be considered linear or nonlinear. The existing methods for estimating the observation function are faced some problems such as: 1) dependency to the initial value of parameters in expectation-maximization based methods and 2) requiring a set of predefined models for the multiple models based methods. In this paper, a new unsupervised method based on the kernel adaptive filters is presented to overcome the above mentioned problems. To do so, least mean squares/ recursive least squares adaptive filters are used to estimate the nonlinear observation function. Here, given the known process function and a sequence of observations, the unknown observation function is estimated. Moreover, to accelerate the algorithm and reduce the computational costs, a sparsification method based on approximate linear dependency is used. The proposed method is evaluated in two applications: time series forecasting and tracking objects in video. Results demonstrate the superiority of the proposed method compared with the existing algorithms.

 
Keyword(s): PARTICLE FILTER, KLMS, KRLS, MODEL ESTIMATION
 
 
References: 
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Citations: 
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+ Click to Cite.
APA: Copy

HAERI, H., & SADOGHI YAZDI, H. (2016). PARTICLE FILTER WITH ADAPTIVE OBSERVATION MODEL. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 14(2), 93-104. https://www.sid.ir/en/journal/ViewPaper.aspx?id=510931



Vancouver: Copy

HAERI H., SADOGHI YAZDI H.. PARTICLE FILTER WITH ADAPTIVE OBSERVATION MODEL. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR. 2016 [cited 2021August02];14(2):93-104. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=510931



IEEE: Copy

HAERI, H., SADOGHI YAZDI, H., 2016. PARTICLE FILTER WITH ADAPTIVE OBSERVATION MODEL. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, [online] 14(2), pp.93-104. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=510931.



 
 
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