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

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.