Ilan Shaviv

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… רוצה לראות יותר

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 miss-set 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. רוצה לראות פחות

This paper addresses the problem of guiding a missile towards an evading agile target in
the presence of noisy measurements. Existing guidance law design methods are all based
on the separation theorem which has never been proven for realistic guidance scenarios.
Moreover, recent works in the field of guidance have suggested that in a realistic guidance
scenarios, the separation theorem is not applicable. In such a case, only the general separation
theorem (GST) may be applied… רוצה לראות יותר

This paper addresses the problem of guiding a missile towards an evading agile target in
the presence of noisy measurements. Existing guidance law design methods are all based
on the separation theorem which has never been proven for realistic guidance scenarios.
Moreover, recent works in the field of guidance have suggested that in a realistic guidance
scenarios, the separation theorem is not applicable. In such a case, only the general separation
theorem (GST) may be applied, implying a separately designed estimator, but a
guidance law that is to consider the conditional probability density function resulting from
this estimator. A new approach to guidance, under the guidelines of the GST, is proposed
herein. The full nonlinear dynamic models are used and the discussion is not limited to
Gaussian noise assumptions. Utilizing a geometric approach, a new entity, termed the miss
set, is identified, which enables the design of a new guidance law that complies with the
GST. From this analysis, a methodology to optimally guide a pursuer towards an evader
is derived. A numerical study is provided which demonstrates the performance
and viability of the proposed methodologv in a realistic 3-D engagement scenario. A comparison
between the proposed methodology and a differential game-based guidance law is carried
out, demonstrating a significant improvement. רוצה לראות פחות

Optimal Kalman filter tuning is discussed and analyzed. It is demonstrated that in a given practical problem, statistical consistency tests cannot be solely used to obtain the unique, optimal filter. Instead. these tests yield an infinite set or consistent filters, of which the optimal filter is a unique member. Based on genetic algorithm minimization, an automated procedure is presented, that can yield the optimally tuned filter (or a set of near optimally tuned filters) using standard… רוצה לראות יותר

Optimal Kalman filter tuning is discussed and analyzed. It is demonstrated that in a given practical problem, statistical consistency tests cannot be solely used to obtain the unique, optimal filter. Instead. these tests yield an infinite set or consistent filters, of which the optimal filter is a unique member. Based on genetic algorithm minimization, an automated procedure is presented, that can yield the optimally tuned filter (or a set of near optimally tuned filters) using standard statistical consistency tests. The performance of the new method is demonstrated via numerical examples, including the tuning of an attitude estimator used in inertial navigation system of UAV. רוצה לראות פחות