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Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking.

Yun Zhu1, Jun Wang2, Shuang Liang3

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This study introduces a new sensor selection method for multi-target tracking using multi-objective optimization. It improves tracking accuracy by optimizing sensor choices for reliable cardinality estimation in sensor networks.

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Area of Science:

  • Signal Processing
  • Estimation Theory
  • Networked Systems

Background:

  • Multi-target tracking in sensor networks is crucial for situational awareness.
  • Accurate cardinality estimation is vital for reliable multi-target state estimation.
  • Existing sensor selection methods may not adequately address conflicting objectives.

Purpose of the Study:

  • To develop a novel sensor selection method for multi-target tracking.
  • To optimize sensor selection based on reliable cardinality estimation.
  • To address conflicting objectives in sensor management within multi-Bernoulli filtering.

Main Methods:

  • Modeling multi-target states using multi-Bernoulli random finite sets.
  • Utilizing the multi-Bernoulli filter for posterior density propagation.
  • Applying multi-objective optimization to balance cardinality estimation objectives.

Main Results:

  • The proposed method effectively selects sensors for improved multi-target tracking.
  • It balances maximizing measurement-updated track cardinality and minimizing legacy track cardinality variance.
  • Theoretical analysis and simulations demonstrate the method's effectiveness and directness.

Conclusions:

  • The novel multi-objective optimization approach enhances sensor selection for multi-target tracking.
  • Reliable cardinality estimation is key to accurate target state determination.
  • The method shows strong performance in scenarios with varying target observability.