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Adaptive Target Birth Intensity Multi-Bernoulli Filter with Noise-Based Threshold.

Xiaolong Hu1, Hongbing Ji2, Long Liu3

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Sensors (Basel, Switzerland)
|March 8, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new filter for multi-target tracking (MTT) that better models new targets appearing. It improves accuracy and efficiency by adaptively adjusting target birth probability and using a measurement noise threshold.

Keywords:
measurement likelihoodmulti-Bernoullimulti-target trackingrandom finite setstarget birth modelthreshold

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Multi-target tracking (MTT) faces challenges in adaptively modeling target birth intensity while maintaining filtering efficiency.
  • Existing adaptive birth models often use constant target birth probability and only consider density, leading to reduced accuracy, especially when new targets appear.
  • Current models also suffer from decreased operational efficiency due to additional birth modeling calculations.

Purpose of the Study:

  • To propose a novel fast sequential Monte Carlo (SMC) adaptive target birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter.
  • To improve multi-target tracking performance by accurately adapting to variations in the number of newborn targets.
  • To enhance both tracking accuracy and operational efficiency.

Main Methods:

  • An adaptive target birth probability is conducted in a pre-processing step, incorporating current measurement information to correct pre-set probabilities.
  • A measurement noise-based threshold is employed in the likelihood calculations of multi-target updating to improve implementation efficiency.
  • The proposed filter integrates adaptive birth probability with the CBMeMBer framework for robust MTT.

Main Results:

  • The proposed filter demonstrates true adaptation to target birth cases, significantly improving tracking accuracy compared to existing methods.
  • Implementation efficiency is substantially enhanced through the use of a measurement noise-based threshold.
  • Simulation results validate the effectiveness and superior performance of the novel filter.

Conclusions:

  • The novel SMC adaptive target birth intensity CBMeMBer filter effectively addresses the limitations of existing MTT models.
  • The adaptive birth probability and efficient updating mechanism lead to improved accuracy and speed in multi-target tracking.
  • This approach offers a promising solution for real-world applications requiring robust and efficient multi-target tracking.