scholarly journals Markov Chain Realization of Joint Integrated Probabilistic Data Association

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2865 ◽  
Author(s):  
Eui Lee ◽  
Qian Zhang ◽  
Taek Song
Keyword(s):  
Markov Chain ◽  
Data Association ◽  
Probabilistic Data ◽  
Probabilistic Data Association

scholarly journals Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 112 ◽  
Author(s):  
Yuan Huang ◽  
Taek Song ◽  
Dae Cheagal
Keyword(s):  
Markov Chain ◽  
Computational Complexity ◽  
Data Association ◽  
Computational Cost ◽  
Chain Process ◽  
Random Data ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Multiple Detection ◽  
Partition Method

In multiple detection target tracking environments, PDA-based algorithms such as multiple detection joint integrated probabilistic data association (MD-JIPDA) utilize the measurement partition method to generate measurement cells. Thus, one-to-many track-to-measurements associations can be realized. However, in this structure, the number of joint data association events grows exponentially with the number of measurement cells and the number of tracks. MD-JIPDA is plagued by large increases in computational complexity when targets are closely spaced or move cross each other, especially in multiple detection scenarios. Here, the multiple detection Markov chain joint integrated probabilistic data association (MD-MC-JIPDA) is proposed, in which a Markov chain is used to generate random data association sequences. These sequences are substitutes for the association events. The Markov chain process significantly reduces the computational cost since only a few association sequences are generated while keeping preferable tracking performance. Finally, MD-MC-JIPDA is experimentally validated to demonstrate its effectiveness compared with some of the existing multiple detection data association algorithms.

scholarly journals A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2180 ◽  
Author(s):  
Xiao Chen ◽  
Yaan Li ◽  
Yuxing Li ◽  
Jing Yu ◽  
Xiaohua Li
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Cluttered Environment

Tracking Position of Airborne Target on SPx-Radar-Simulator Using Probabilistic Data Association Filter

2021 ◽  
Author(s):  
Mochammad Sahal ◽  
Zaidan Adenin Said ◽  
Rusdhianto Effendi Abdul Kadir ◽  
Zulkifli Hidayat ◽  
Yusuf Bilfaqih ◽  
...  
Keyword(s):  
Data Association ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Radar Simulator ◽  
Airborne Target
Sign in / Sign up

Export Citation Format

Share Document