Estimation-Guided Guidance

Shaviv, Ilan G.; Oshman, Yaakov

doi:10.2514/6.2006-6217

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…A simplified and intuitive approach to the linearized Gaussian problem was presented in [19]. Expanding upon and consolidating the results of [20][21][22], the present paper presents a novel, unified approach to integrating guidance with estimation in a generalized nonlinear non-Gaussian framework. The new integrated estimation/ guidance methodology is compliant with the GST.…”

Section: Introductionmentioning

confidence: 84%

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

Shaviv

Oshman

2017

Journal of Guidance, Control, and Dynamics

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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.

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Section: Introductionmentioning

confidence: 84%

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

Shaviv

Oshman

2017

Journal of Guidance, Control, and Dynamics

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“…Shaviv & Oshman 18 pointed out the affiliation of the existing missile guidance law designs methods to the separation theorem. They provided a composition of multiple model estimation and guidance in the frame work of general separation theorem.…”

Section: Previous Work On Predictive Guidancementioning

confidence: 99%

Predictive Missile Guidance with Online Trajectory Learning

Akcal

Üre

2017

Def. Sc. Jl.

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INtroductIoNVarious missile guidance laws have been developed and studied since the World War II. The proportional navigation 1 (PN) is quite celebrated among these laws, due to its simplicity and efficiency. First studies on modern guidance laws based on modern control and estimation theory were conducted during 1960's. However at that time, implementation of these methods was not feasible because of the computational restrictions. Yet, with the proceeding technology and increasing capability of the targets, the aforementioned attitude towards modern laws began to change during late 1970's 2-8 . The problem of coping with modern day targets (e.g., intercepting a hostile aircraft) with advanced manoeuverability skills, calls for utilisation of estimation techniques within guidance algorithms. By making use of these estimation methods, it is possible to predict the future trajectory of the target. The basic idea behind predictive guidance is enabling the interceptor to take advantage of the estimated future trajectory of the target and modify the guidance law according to these estimations. Talole & Banavar 9 have shown that the PN law modified with predictive control is superior to the PN law itself in terms of control effort. Prabhakar 10 , et al. demonstrated that predictive guidance is capable of exhibiting a significantly improved performance. However, most of the existing predictive guidance laws assume full or partial knowledge of the target's dynamics. In this paper, estimations are based on a learning process, which utilises the noisy measurement of the target positions by passing them through a recursive least squares (RLS) estimation algorithm. This study presents a predictive guidance scheme for tactical missiles. The modern day targets, with improved manoeuverability, have revealed insufficient performance of the conventional guidance laws. The underlying cause of this poor performance is the reactive nature of the conventional guidance laws such as proportional navigation (PN) and pure pursuit (PP). Predictive guidance offers an alternative approach to the classical methods by taking proactive actions by estimating target's future trajectory. However, most of the existing predictive guidance approaches assume that the interceptor have a model of the target dynamics. A guidance strategy is developed in this study, that can learn the target dynamics iteratively and adapt the interceptor actions accordingly. A recursive least squares (RLS) estimation algorithm is employed for learning and estimating the possible future target positions, and a fixed horizon nonlinear program is employed for selecting the optimal interception action. Monte-Carlo simulations show that the guidance algorithm introduced in this work demonstrates a significantly improved performance compared to the alternatives in terms of interception time and miss distance.

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Stochastic Optimal Guidance of Missiles with Nonlinear Dynamics

Hexner

Shima

2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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Estimation-Guided Guidance

Cited by 4 publications

References 11 publications

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

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

Predictive Missile Guidance with Online Trajectory Learning

Stochastic Optimal Guidance of Missiles with Nonlinear Dynamics

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