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Multiple Targets Tracking with Big Data-Based Measurement for Extended Binary Phase Shift Keying Transceiver.

Yao Yu1, Junhui Zhao1, Wu Lenan2

  • 11 School of Information Engineering, East China Jiaotong University, Nanchang, China.

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Summary
This summary is machine-generated.

This study introduces a big data tracking strategy for high-resolution radar systems using Extended Binary Phase Shift Keying (EBPSK). Incorporating Doppler measurements significantly enhances linear multitarget tracking performance.

Keywords:
Doppler data associationEBPSKlinear multitarget integrated probabilistic data association algorithmradar trackingtracking performance

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Data Science

Background:

  • Extended Binary Phase Shift Keying (EBPSK) systems offer high-resolution radar tracking capabilities.
  • Estimating target kinematic states relies on extensive time-series data of range and velocity measurements.
  • Handling large volumes of measurement data presents significant computational challenges.

Purpose of the Study:

  • To develop and evaluate a big data-based tracking strategy for EBPSK radar systems.
  • To integrate Doppler measurements into the data association process for improved tracking.
  • To enhance linear multitarget (LMs) tracking by utilizing both range and velocity data.

Main Methods:

  • Introduction to the principles of EBPSK transceivers and target impulse response representation.
  • Proposal of an efficient big data association scheme for LMs tracking, incorporating range and velocity measurements.
  • Demonstration that nonlinear filter banks are unnecessary when velocity measurements are excluded from kinematic state estimation.

Main Results:

  • The proposed scheme efficiently associates data using both target range and velocity measurements.
  • Excluding velocity measurements simplifies the estimation process without requiring nonlinear filter banks.
  • Significant performance enhancement in tracking accuracy was observed with the big Doppler data association method.

Conclusions:

  • The big data-based tracking strategy effectively utilizes Doppler measurements for enhanced radar tracking.
  • The proposed scheme offers a more efficient approach to linear multitarget tracking in high-resolution radar systems.
  • Integrating Doppler data into the data association procedure demonstrably improves tracking performance.