The Vulnerability of Laser Warning Systems Against Guided Weapons Based on Low-Power Lasers—Part II
The Vulnerability of Laser Warning Systems Against Guided Weapons Based on Low-Power Lasers—Part II
Author(s): Mubarak Al-Jaberi; John A. Coath; Robin B. Jenkin; Mark A. Richardson
No pages: 5
Year: 2006
Article ID: 9-2-4
Keywords: guided weapons, lasers, surveillance and target acquisition
Format: Electronic (PDF)
Abstract: Laser-assisted weapons, such as laser-guided bombs, laser-guided missiles and laser-beamriding missiles pose a significant threat to military assets in the modern battlefield. Laser-beamriding missiles are particularly hard to detect because they use low-power lasers. They are even harder to defeat because current countermeasures are not designed to work against this threat [1]. The aim of this project is to examine the vulnerability of laser-warning systems to guided weapons, to build an evaluation tool for laser-warning receivers (LWRs) and seekers, and to identify suitable countermeasures for laser-beamriding missiles that use low-power lasers in their guidance systems. The project arose because of the unexpected results obtained from extensive field trials carried out on various LWRs in the United Arab Emirates desert, where severe weather conditions may be experienced. In order to approach the subject, a computer model has been developed to enable the assessment of all phases of a laser-warning receiver and missile seeker. MATLAB & SIMULINK software have been used to build the model. During this process experimentation and field trials have been carried out to verify the reliability of the model. This project enables both the evaluation and design of any generic laser-warning receiver or missile seeker and specific systems if various parameters are known. Moreover, this model will be used as a guide to the development of reliable countermeasures for laser-beamriding missiles. Part I of this series outlined the theory required to construct a computer model for a laser-beamriding missile engagement. This second part presents the implementation of the model using MATLAB and Simulink, the inputs required of the model, and the outputs generated. These results are analysed to determine the correct functionality of the model prior to its verification with laboratory-based experimental results and full-scale field trials (as presented in Part III).