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Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic

Qinghua Han1, Minghai Pan2, Weijun Long3

  • 1College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China.

Sensors (Basel, Switzerland)
|February 16, 2020
PubMed
Summary

This study introduces a joint adaptive sampling interval and power allocation (JASIPA) scheme for maneuvering target tracking (MTT) in opportunistic array radar (OAR) systems. The proposed method optimizes radar resource allocation for improved tracking performance.

Keywords:
Cramér-Rao lower bound like (CRLB-like)best-fitting Gaussian (BFG)chance-constraint programming (CCP)joint adaptive sampling interval and power allocation (JASIPA)maneuvering target tracking (MTT)

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Area of Science:

  • Radar Systems Engineering
  • Signal Processing
  • Optimization Theory

Background:

  • Maneuvering target tracking (MTT) in opportunistic array radar (OAR) systems presents challenges in resource allocation.
  • Accurate prediction of target states and handling of uncertainties like radar cross section (RCS) variations are critical.

Purpose of the Study:

  • To propose a joint adaptive sampling interval and power allocation (JASIPA) scheme for enhanced MTT in OAR systems.
  • To minimize total transmitted power while ensuring desired tracking accuracy.
  • To address uncertainties in target RCS using chance-constrained programming (CCP).

Main Methods:

  • Utilizing best-fitting Gaussian (BFG) approximation for target state prediction.
  • Employing Riccati-like recursion for resource allocation based on prior and Bayesian Cramér-Rao lower bounds (CRLB-like).
  • Integrating stochastic simulation with a genetic algorithm (GA) to form a hybrid intelligent optimization algorithm (HIOA) for solving the CCP problem.

Main Results:

  • The proposed JASIPA scheme effectively determines adaptive sampling intervals and power allocation.
  • The BFG approximation simplifies multimodal probability density functions (PDFs).
  • The HIOA successfully solves the CCP optimization problem, minimizing transmitted power.

Conclusions:

  • The developed resource allocation scheme significantly improves the global performance of OAR systems for MTT.
  • The combination of CCP and HIOA provides an effective approach for managing radar resources under uncertainty.
  • The method demonstrates robust performance in maneuvering target tracking scenarios.