Comprehensive Approach to Directly Addressing Estimation Delays in Stochastic Guidance

TL;DR

This paper introduces a comprehensive guidance strategy that explicitly manages time-varying estimation delays in pursuit-evasion scenarios, improving interception robustness.

Contribution

It develops a guidance law incorporating two delays, driven by a particle-based smoother, with real-time delay estimation using semi-Markov models, unifying estimation and guidance.

Findings

Demonstrates superior robustness over existing laws.

Uses Monte Carlo simulations to validate effectiveness.

Adapts guidance inputs based on real-time delay estimates.

Abstract

In realistic pursuit-evasion scenarios, abrupt target maneuvers generate unavoidable periods of elevated uncertainty that result in estimation delays. Such delays can degrade interception performance to the point of causing a miss. Existing delayed-information guidance laws fail to provide a complete remedy, as they typically assume constant and known delays. Moreover, in practice they are fed by filtered estimates, contrary to these laws' foundational assumptions. We present an overarching strategy for tracking and interception that explicitly accounts for time-varying estimation delays. We first devise a guidance law that incorporates two time-varying delays, thereby generalizing prior deterministic formulations. This law is driven by a particle-based fixed-lag smoother that provides it with appropriately delayed state estimates. Furthermore, using semi-Markov modeling of the target's maneuvers, the delays are estimated in real-time, enabling adaptive adjustment of the guidance inputs during engagement. The resulting framework consistently conjoins estimation, delay modeling, and guidance. Its effectiveness and superior robustness over existing delayed-information guidance laws are demonstrated via an extensive Monte Carlo study.