A Newborn Track Detection and State Estimation Algorithm Using Bernoulli Random Finite Sets

Baehoon Choi, Seongkeun Park, Euntai Kim

Research output: Contribution to journalArticle

6 Citations (Scopus)


In multi-target tracking (MTT) problems, there are many important issues that affect performance, including statistical filtering, measurement-target association, and estimating the number of targets. While newborn target detection and state estimation should also be considered as important factors in MTT, only a few studies have addressed these topics. In this paper, a novel newborn track detection and state estimation method is proposed using the concept of Bernoulli random finite sets. The posterior finite set statistical probability density function (FISST PDF) of a newborn target is analytically derived, and a tractable implementation scheme is proposed using importance sampling. Finally, the validity of the proposed method is demonstrated via integration with a Gaussian mixture probability hypothesis density (GM-PHD) filter and subsequent application to MTT problems.

Original languageEnglish
Article number7412755
Pages (from-to)2660-2674
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number10
Publication statusPublished - 2016 May 15


All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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