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Adaptive Estimation of Spatial Clutter Measurement Density Using Clutter Measurement Probability for Enhanced

Seung Hyo Park1, Sa Yong Chong1, Hyung June Kim1

  • 1Department of Electronic Systems Engineering, Hanyang University, Ansan 15588, Korea.

Sensors (Basel, Switzerland)
|December 28, 2019
PubMed
Summary

This study introduces a new method to improve multi-target tracking by addressing biases in clutter density estimation. The proposed MTT-SCMDE enhances accuracy in radar and sonar surveillance systems.

Keywords:
clutter measurement densitydata associationmulti-target trackingspatial clutter measurement density estimator

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Area of Science:

  • Signal Processing
  • Surveillance Systems
  • Data Association

Background:

  • Radar and sonar systems generate point detections including both target and clutter measurements.
  • Accurate target tracking relies on data association to differentiate targets from clutter.
  • Existing clutter density estimation methods, like SCMDE, exhibit biases in multi-target scenarios, degrading tracking performance.

Purpose of the Study:

  • To analyze the source of performance degradation in existing SCMDE for multi-target tracking.
  • To propose an improved clutter measurement density estimation method for enhanced multi-target tracking.
  • To validate the effectiveness of the proposed method through simulations and real-world data.

Main Methods:

  • Analysis of spatial clutter measurement density estimator (SCMDE) biases in multi-target tracking.
  • Introduction of clutter measurement probability as a corrective measure.
  • Development of a novel adaptive clutter measurement density estimation method for multi-target tracking (MTT-SCMDE).
  • Expansion of hyper-sphere volume for sparsity orders to reduce estimation bias.

Main Results:

  • Identified a key source of tracking performance degradation in SCMDE for multi-target tracking.
  • Demonstrated that MTT-SCMDE significantly improves multi-target tracking performance.
  • Validated performance improvements through Monte Carlo simulations and real radar data analysis.
  • Showcased enhanced clutter measurement density estimation and target tracking performance across various sparsity orders.

Conclusions:

  • The proposed MTT-SCMDE effectively mitigates biases in clutter density estimation for multi-target tracking.
  • The new method leads to more robust and accurate target tracking in surveillance environments.
  • MTT-SCMDE offers a significant advancement for applications relying on radar and sonar data association.