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Risk-Aware Resource Allocation Strategy for Target Tracking in a Cognitive Radar Network.

Ji Ye Lee¹, Jongho Park²

¹Department of Military Digital Convergence, Ajou University, Suwon 16499, Republic of Korea.

Sensors (Basel, Switzerland)

|June 12, 2026

Summary

This summary is machine-generated.

Cognitive radar now incorporates operational risk for resource allocation, improving efficiency. This new approach balances tracking accuracy with real-world operational needs, optimizing radar dwell time effectively.

Keywords:

Second-Order Cone Programming (SOCP)cognitive radar convex optimization operational risk assessment resource allocation target tracking

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Area of Science:

Electrical Engineering
Computer Science
Operations Research

Background:

Cognitive radar utilizes environmental feedback for resource allocation via optimization.
Prior research emphasized target tracking accuracy, neglecting operational perspectives.
Real-world cognitive radar applications require balancing tracking performance with operational considerations.

Purpose of the Study:

To propose an operational risk score for cognitive radar resource allocation.
To enhance cognitive radar frameworks by integrating operational risk.
To optimize radar dwell time allocation based on target priority and operational significance.

Main Methods:

Developed a cognitive radar framework incorporating an operational risk score.
Formulated the radar dwell time allocation as an optimization problem.
Solved the dwell time allocation using Second-Order Cone Programming (SOCP).

Main Results:

The proposed SOCP-based algorithm demonstrated comparable operational risk estimation to conventional methods.
The framework achieved improved overall resource efficiency.
Fewer time resources were utilized compared to traditional approaches, particularly in resource-constrained scenarios.

Conclusions:

Integrating operational risk scores enhances cognitive radar resource allocation.
SOCP provides an effective method for optimizing dwell time in cognitive radar.
The proposed framework improves efficiency and operational relevance in practical cognitive radar systems.