The probabilistic data association filter

By Yaakov Bar-Shalom, F. Daum, Jim Huang. 2009

796 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chau Yuen
2. Yuren Zhou
3. Meng Zhang
4. Jinkun Cao
5. Billy Pik Lik Lau
6. Karl Granström
7. U-Xuan Tan
8. Marcus Baum

In Computer Networks and Communications

1. Billy Pik Lik Lau
2. Chau Yuen
3. Yuren Zhou
4. Meng Zhang
5. Karl Granström
6. U-Xuan Tan
7. Marcus Baum
8. Kris M. Kitani

In Control and Systems Engineering

1. Xinshuo Weng
2. Chau Yuen
3. Yuren Zhou
4. Meng Zhang
5. Jinkun Cao
6. Billy Pik Lik Lau
7. U-Xuan Tan
8. Kris M. Kitani

Citations in the corpus

• A survey of data fusion in smart city applications (2019, methodology): “Common techniques for data association include Nearest Neighbors [59], Probabilistic Data Association [60], and Multiple Hypothesis Test [61].”
• Extended Object Tracking: Introduction, Overview and Applications (2016, background): “de • Multiple Hypothesis Tracking (MHT) [24], [105], [152], • Joint Probabilistic Data Association (JPDA) [4], [6], [7], • Probabilistic Multiple Hypothesis Tracking (PMHT) [174], [198], and • Random Finite Sets (RFS) [123], [125].”
• Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking (2022, background): “Multi-object Tracking is traditionally approached from probabilistic perspectives, such as joint probabilistic association [2].”