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Robust Measurement-Driven Cardinality Balance Multi-Target Multi-Bernoulli Filter.

Biao Yang1, Shengqi Zhu1, Xiongpeng He1

  • 1National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)
|September 10, 2021
PubMed
Summary

This study introduces a new robust filter for multi-target tracking, even with unknown detection and clutter densities and limited prior target information. The novel filter effectively estimates these unknown parameters for improved tracking accuracy.

Keywords:
cardinality balance multiple targets multi-Bernoulli filtercorrelation functionmultiple targets trackingrandom finite set

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Area of Science:

  • Multi-target tracking
  • Bayesian inference
  • Signal processing

Background:

  • Bayesian multi-target tracking filters require accurate prior information (detection/clutter density, initial position).
  • Unknown or lacking prior information poses significant challenges for existing tracking filters.

Purpose of the Study:

  • To propose a novel robust measurement-driven cardinality balance multi-target multi-Bernoulli filter (RMD-CBMeMBer).
  • To address multi-target tracking problems with unknown detection probability density, unknown background clutter density, and lacking prior target position information.

Main Methods:

  • Extended target state to include detection probability, kernel state, and target/clutter indicators.
  • Modeled detection probability using Beta distribution and clutter using independent Poisson distributions.
  • Jointly estimated detection probability, kernel state, and clutter density via filtering.
  • Introduced a correlation function (CF) for creating new Bernoulli components (BCs) using prior measurement data.

Main Results:

  • The RMD-CBMeMBer filter successfully handles multi-target tracking under conditions of unknown detection probability, unknown clutter density, and inadequate prior target information.
  • The filter effectively estimates target detection probability and clutter density.
  • Numerical experiments validated the filter's performance and robustness.

Conclusions:

  • The RMD-CBMeMBer filter provides a robust solution for multi-target tracking in scenarios with uncertain environmental and target parameters.
  • This approach enhances tracking accuracy by adaptively estimating key unknown variables.
  • The proposed method offers a significant advancement in handling complex, real-world tracking scenarios.