Probabilistic data association algorithm based on adaptive robust Kalman filtering

Binhong Ma; Tingwei Zhang; Mingliang Shen; Jun Tang

doi:10.1117/12.2661021

2 February 2023 Probabilistic data association algorithm based on adaptive robust Kalman filtering

Binhong Ma, Tingwei Zhang, Mingliang Shen, Jun Tang

Proceedings Volume 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022); 124622X (2023) https://doi.org/10.1117/12.2661021
Event: International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 2022, Xi'an, China

Abstract

The data association problem in cluttered environments is one of the difficult problems in the field of object tracking. When probabilistic data association algorithms are used in motion model deviation scenarios, incorrect tracking occurs. Combining the adaptive robust Kalman filter (ARKF) with the probabilistic data association (PDA), this paper presents the adaptive robust probabilistic data association (ARPDA) algorithm for estimating the target state in cluttered environments. The results of the experiment indicate that the proposed algorithm has higher tracking accuracy under the condition of motion model deviation compared with the traditional probabilistic data association algorithm.

Citation Download Citation

Binhong Ma, Tingwei Zhang, Mingliang Shen, and Jun Tang "Probabilistic data association algorithm based on adaptive robust Kalman filtering", Proc. SPIE 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 124622X (2 February 2023); https://doi.org/10.1117/12.2661021

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available