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Drones tracking based on robust Cubature Kalman-TBD-Multi-Bernoulli filter.

Mohamed Barbary1, Mohamed H Abd ElAzeem2

  • 1Department of Electrical Engineering, Alexandria University, Alexandria, Egypt; Technical Research and Developing Centre, Elsayeda Aisha, Cairo, Egypt.

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|January 3, 2021
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Summary
This summary is machine-generated.

This study introduces a novel robust filter combining Cubature Kalman-Multi-Bernoulli and variational Bayesian track-before-detect (TBD) methods for enhanced micro-drone tracking. The new approach effectively estimates detection profiles and fluctuated measurement variances, improving micro-drone detection and tracking accuracy.

Keywords:
Approximated variational BayesianCubature Kalman Multi-Bernoulli FilterMicro-drones trackingTBD

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Area of Science:

  • Aerospace Engineering
  • Signal Processing
  • Robotics

Background:

  • Nonlinear tracking and detection of small unmanned aerial vehicles (UAVs) and micro-drones present significant challenges.
  • Existing methods like Cubature Kalman filters and variational Bayesian-Multi-Bernoulli filters struggle with unknown detection probabilities, limiting their effectiveness for micro-drone tracking.
  • Track-before-detect (TBD) schemes are recognized as effective for tracking small objects.

Purpose of the Study:

  • To propose a novel robust Cubature Kalman-Multi-Bernoulli filter integrated with variational Bayesian-TBD for micro-drone tracking.
  • To address the challenge of unknown detection profiles and estimate fluctuated measurement variances in micro-drone detection.
  • To develop an effective method for estimating the hybrid kinematic state of micro-drones.

Main Methods:

  • A novel robust Cubature Kalman-Multi-Bernoulli filter with variational Bayesian-TBD is proposed.
  • The filter jointly estimates fluctuated measurement variances.
  • A new implementation using a non-linear Cubature Kalman Gaussian mixture and Inverse Gamma approximation is presented to estimate the hybrid kinematic state.

Main Results:

  • The proposed filter effectively solves the problem of detection profile estimation for micro-drones.
  • Simulation results demonstrate the effectiveness of the algorithm in tracking and detecting micro-drones.
  • The algorithm shows robustness in handling nonlinear models and fluctuated measurement variances.

Conclusions:

  • The developed filter offers an effective solution for the nonlinear tracking and detection of micro-drones.
  • The integration of Cubature Kalman-Multi-Bernoulli filter with variational Bayesian-TBD improves detection profile estimation.
  • The proposed method is robust and effective for micro-drone tracking applications.