Performance Improvement of Time-Balance Radar Schedulers Through Decision Policies (Extended Version)

TL;DR

This paper enhances time-balance radar schedulers by applying a stochastic control analogy, enabling adaptive target tracking that improves overall performance without sacrificing practicality.

Contribution

It introduces a novel decision policy based on the machine replacement problem analogy to improve time-balance scheduling in radar systems.

Findings

Improved tracking performance with adaptive scheduling.

Successful trade-off between unnecessary updates and deteriorating tracks.

Numerical experiments validate the effectiveness of the proposed policy.

Abstract

The resource management of a phase array system capable of multiple target tracking and surveillance is critical for the realization of its full potential. Present work aims to improve the performance of an existing method, time-balance scheduling, by establishing an analogy with a well-known stochastic control problem, the machine replacement problem. With the suggested policy, the scheduler can adapt to the operational scenario without a significant sacrifice from the practicality of the time-balance schedulers. More specifically, the numerical experiments indicate that the schedulers directed with the suggested policy can successfully trade the unnecessary track updates, say of non-maneuvering targets, with the updates of targets with deteriorating tracks, say of rapidly maneuvering targets, yielding an overall improvement in the tracking performance.