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Improved Multitarget Tracking in Clutter Using Bearings-Only Measurements.

Yifang Shi1, Mengfan Xue2, Yuemin Ding3

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Summary
This summary is machine-generated.

This study introduces the Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter for multitarget tracking with bearings-only measurements in clutter. The new filter improves performance by addressing measurement origin uncertainty and nonlinearity.

Keywords:
Gaussian mixture measurements-cardinality probability hypothesis densitybearings-onlymeasurement nonlinearitymeasurement-origin-uncertaintymultitarget tracking

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Area of Science:

  • Signal Processing
  • Estimation Theory
  • Data Fusion

Background:

  • Multitarget tracking in clutter using bearings-only measurements presents significant challenges.
  • Key issues include measurement-origin-uncertainty and measurement nonlinearity.
  • Existing methods like the extended Kalman filter (EKF) have limitations in handling these complexities.

Purpose of the Study:

  • To propose a novel nonlinear filter, the Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter.
  • To enhance performance in bearings-only multitarget tracking within cluttered environments.
  • To effectively address measurement-origin-uncertainty and measurement nonlinearity.

Main Methods:

  • The proposed GMMbCPHD filter is based on Random Finite Set (RFS) theory.
  • It estimates the intensity of RFS of multiple targets and propagates the posterior cardinality distribution to handle measurement-origin-uncertainty.
  • It approximates the measurement likelihood function using a Gaussian mixture to address measurement nonlinearity, outperforming the single Gaussian approximation used in EKF.

Main Results:

  • The GMMbCPHD filter demonstrates improved performance in multitarget tracking scenarios with bearings-only measurements.
  • The filter effectively manages both measurement-origin-uncertainty and measurement nonlinearity.
  • Simulation studies confirm the superiority of the GMMbCPHD filter compared to state-of-the-art algorithms.

Conclusions:

  • The GMMbCPHD filter offers a robust solution for bearings-only multitarget tracking in clutter.
  • It provides a significant advancement by accurately handling complex measurement uncertainties and nonlinearities.
  • The proposed filter represents a valuable contribution to the field of target tracking.