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Iteratively distributed instrumental variable-based pseudo-linear information filter for angle-only tracking.

Yanbo Yang1, Zhunga Liu1, Yuemei Qin2

  • 1School of Automation, Northwestern Polytechnical University, Xi'an, PR China; Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an, PR China.

ISA Transactions
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel distributed filtering method for angle-only target tracking (AOTT) in sensor networks. The proposed approach enhances tracking precision and consistency across multiple observers using iterative refinement.

Keywords:
Angle-only target trackingFinite-time average consensusInstrumental variablesIteratively distributed filteringPseudo-linear estimation

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Area of Science:

  • Robotics and Control Systems
  • Signal Processing
  • Networked Systems

Background:

  • Distributed filtering is crucial for target tracking in sensor networks.
  • Angle-only target tracking (AOTT) presents unique challenges due to limited measurement information.
  • Existing methods often struggle with achieving high precision in distributed AOTT scenarios.

Purpose of the Study:

  • To develop a precise distributed filtering algorithm for three-dimensional angle-only target tracking (AOTT).
  • To ensure consistency of filtering results across all observers in a network.
  • To improve upon the filtering precision of existing pseudo-linear Kalman filter variants.

Main Methods:

  • Derivation of an instrumental variable-based pseudo-linear information filter (IVIF) using bias compensation.
  • Development of a distributed IVIF (DIVIF) employing finite-time average consensus for information quantities and bias.
  • Proposal of an iteratively DIVIF for enhanced accuracy in parameter estimation and filtering precision.

Main Results:

  • The DIVIF ensures that each observer's filtering result aligns with a centralized fusion approach.
  • The iterative DIVIF progressively refines relative distance and angle estimates for superior filtering parameters.
  • Demonstrated superior filtering precision compared to existing pseudo-linear Kalman filters in AOTT examples.

Conclusions:

  • The proposed iterative DIVIF offers enhanced filtering precision for 3D AOTT in observer networks.
  • The method effectively addresses distributed filtering challenges by leveraging consensus mechanisms.
  • Computational complexity is analyzed, and performance advantages are validated through simulations.