missile guidance
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Author(s):  
D. Lytovchenko ◽  
V. Kutsenko

In modern conditions of combat use the SA-19 “Grison” anti-aircraft gun missile system fires at small targets (drones) and typical targets (helicopters and attack aircraft), so a number of problems arise. In particular, they include: finding the value of the probabilities of hitting the target with n shots and one shot; assessing the effectiveness of the SA-19 “Grison” platoon‟s concentrated fire on a single target; estimating errors of missile guidance and warhead detonation system; estimating the values of conditional probabilities of hitting a target with a single missile, depending on the value of particular mishit. When calculating the slant range to the far edge of the SA-19 “Grison” weapon's kill zone under different conditions of use, factors that reduce these ranges should be taken into account. An analysis of the main studies and publications presented in [1-9] does not make it possible to determine the performance of missile and artillery weapons in shooting at small-size targets. This literature provides general approaches to solving this problem. The purpose of this article is to develop a model for calculating the values of conditional probabilities of destruction of small targets, to form the best options for repelling an enemy‟s air strike, as well as to justify the general directions of improvement of weapon‟s elements.


Author(s):  
Weifan Li ◽  
Yuanheng Zhu ◽  
Dongbin Zhao

AbstractIn missile guidance, pursuit performance is seriously degraded due to the uncertainty and randomness in target maneuverability, detection delay, and environmental noise. In many methods, accurately estimating the acceleration of the target or the time-to-go is needed to intercept the maneuvering target, which is hard in an environment with uncertainty. In this paper, we propose an assisted deep reinforcement learning (ARL) algorithm to optimize the neural network-based missile guidance controller for head-on interception. Based on the relative velocity, distance, and angle, ARL can control the missile to intercept the maneuvering target and achieve large terminal intercept angle. To reduce the influence of environmental uncertainty, ARL predicts the target’s acceleration as an auxiliary supervised task. The supervised learning task improves the ability of the agent to extract information from observations. To exploit the agent’s good trajectories, ARL presents the Gaussian self-imitation learning to make the mean of action distribution approach the agent’s good actions. Compared with vanilla self-imitation learning, Gaussian self-imitation learning improves the exploration in continuous control. Simulation results validate that ARL outperforms traditional methods and proximal policy optimization algorithm with higher hit rate and larger terminal intercept angle in the simulation environment with noise, delay, and maneuverable target.


Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3977
Author(s):  
Yukuan Liu ◽  
Guanglin He ◽  
Zenghui Qiao ◽  
Zhaoxuan Guo ◽  
Zehu Wang

The time delay of seekers has grown to be a serious issue for tactical missile guidance with the development of flight vehicle technologies. To address the problem, a measurement compensation system for the seeker, with lags and delays based on predictive active disturbance rejection control, is proposed. In addition, to eliminate the effects of target maneuvers to the tactical missile guidance, an adaptive finite-time convergent sliding mode guidance law, based on super-twisting algorithm, is proposed in three-dimensional missile-target engagement kinematics. Specifically, the compensation system consists of a predictive tracking structure and an active disturbance rejection control system, which could follow a virtual measurement without lags and delays. The compensation system has advantages in disturbance rejection and model inaccuracy addressing, compared with existing compensation methods for seeker measurement. As for the sliding mode guidance law design, the proposed approach is based on an improved super-twisting algorithm with fast convergent adaptive gains, which has advantages in addressing unknown but bounded target maneuvers and avoiding chattering of the classical sliding mode control. As a result, the measurement compensation system and the adaptive sliding mode guidance law is verified robust and effective under the proposed constraints by the simulation examples.


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