Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter

LIU Jun; LIU Yu; HE You; SUN Shun

doi:10.11999/JEIT150849

Volume 38 Issue 6

Jun. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(6): 1438-1445

LIU Jun, LIU Yu, HE You, SUN Shun. Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1438-1445. doi: 10.11999/JEIT150849

Citation:

LIU Jun, LIU Yu, HE You, SUN Shun. Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1438-1445. doi: 10.11999/JEIT150849

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Joint Probabilistic Data Association Algorithm Based on All-neighbor Fuzzy Clustering in Clutter

doi: 10.11999/JEIT150849

1.
(Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001, China)
2.

Funds:

The National Natural Science Foundation of China (61471383)

Received Date: 2015-07-16
Rev Recd Date: 2016-03-08
Publish Date: 2016-06-19

Abstract

Abstract

This paper proposes a new Joint Probabilistic Data Association algorithm based on All-Neighbor Fuzzy Clustering (ANFCJPDA) for mutitarget tracking in the clutter. Firstly, distance measure is established according to measurements distribution in validation area and data correlation rules. Then, the predicted position is set up as a cluster center, and the association probabilities are calculated on the basis of fuzzy clustering, which are used as weights to update targets state and the covariance. Simulation results show that the proposed method reduces highly the computational complexity compared to conventional Joint Probabilistic Data Association (JPDA) technique, and is effective for multiple target tracking in a cluttered environment.
- Multiple target tracking,
- Multisensor,
- Data association,
- Fuzzy clustering

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References(26)

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