Estimation-Guided Guidance and Its Implementation via Sequential Monte Carlo Computation

Abstract

Existing missile guidance strategies are traditionally based on the separation theorem, which has never been proven valid in realistic guidance scenarios. In such cases, only the general separation theorem may be applied, implying a separately designed estimator and a guidance law accounting for the conditional probability density function. A new general-separation-theorem-compliant geometry-based approach is proposed to fusion of estimation and guidance. The conventional notion of reachability sets is extended, facilitating the introduction of miss-sets. A stochastic guidance strategy is proposed that aims at maximizing the pursuer's single-shot kill probability by driving its own miss-set to optimally cover the evader's miss-set (in a probabilistic sense). Information-based trajectory shaping is employed, when applicable, to enhance the scenario's observability, thereby reducing the evader's miss-set uncertainty. Computationally efficient sequential Monte Carlo methods are employed to estimate the evader's missset and implement the guidance scheme. A numerical simulation study is used to demonstrate the proposed methodology's robustness and accuracy in a realistic stochastic engagement. The performance of the proposed strategy is compared with that of an advanced perfect-information guidance law, addressing (in particular) real-time implementation issues and constraints. Proof-of-concept simulations demonstrate that the method is real-time amenable using present-day technology.

Gaussian process‐based Bayesian non‐linear filtering for online target tracking

“…The key components in the design of a Bayesian filter are grounded on the formulation of dynamics and observation models, which typically constructs the temporal evolution of the target dynamics and the measurements observed by the sensors in a target tracking scenario. This kind of methodology can be traced to optimal filtering [8], suboptimal filtering [9] and adaptive filtering [10] for linear and non‐linear systems, and it is also widely applied in the area of manoeuvering target interception [11], guidance law design [12] and distributed sensor fusion [13]. Solutions to state estimation in linear dynamic systems are well known, of which the Kalman filter [14] yields the optimal unbiased estimate with respect to the current state variable in the sense of minimum mean square error.…”