Unified Estimation--Guidance Framework Based on Bayesian Decision Theory

TL;DR

This paper develops a Bayesian decision theory-based guidance law that accounts for estimation errors, using particle filtering for real-time implementation, and demonstrates its effectiveness through simulations.

Contribution

It introduces a stochastic guidance law that incorporates estimation errors and leverages particle filtering, improving upon traditional separation-based guidance methods.

Findings

The proposed guidance law effectively handles estimation errors in simulations.

Particle filtering enables real-time implementation of the guidance scheme.

Simulation results show improved guidance performance with the new approach.

Abstract

Using Bayesian decision theory, we modify the perfect-information, differential game-based guidance law (DGL1) to address the inevitable estimation error occurring when driving this guidance law with a separately-designed state estimator. This yields a stochastic guidance law complying with the generalized separation theorem, as opposed to the common approach, that implicitly, but unjustifiably, assumes the validity of the regular separation theorem. The required posterior probability density function of the game's state is derived from the available noisy measurements using an interacting multiple model particle filter. When the resulting optimal decision turns out to be nonunique, this feature is harnessed to appropriately shape the trajectory of the pursuer so as to enhance its estimator's performance. In addition, certain properties of the particle-based computation of the Bayesian cost are exploited to render the algorithm amenable to real-time implementation. The performance of the entire estimation-decision-guidance scheme is demonstrated using an extensive Monte Carlo simulation study.