One approach to the evaluation of the effectiveness of the boomerang system for acoustic source localization and identification

Abstract

One approach to the evaluation of the effectiveness of a system for acoustic source localization and identification has been shown in this article. The system for acoustic source localization and identification has been presented as a model of mass servicing system. The states of the system as well as its features have been described while the formula for service probability determination has been derived as a criterion for effectiveness evaluation. The introductory part of the article describes the system for acoustic source localization and identification and shows a model for quantity estimation of the function of technical system effectiveness E (t). The system effectiveness represents a probability of system initiation as well as a probability of its successful mission accomplishment on the basis of designed capabilities within the given time period and operational conditions. The basic characteristics of the Boomerang system for acoustic source localization and identification show the system elements and its designed capability to detect, analyse and distribute acoustic source location data. The analytical model for the evaluation of the effectiveness of the Boomerang system for acoustic source localization and identification shows that 'n' stations for acoustic source localization can exchange information with each other using the network link. The system conditions are described and the expression for the determination of service probability as a criterion for system effectiveness evaluation is derived. The evaluation of the effectiveness of the Boomerang system for acoustic source localization and identification is presented with a formula for determining mass servicing system probability from the Matlab 6.5R13 program. The following graphs have been obtained: - correlation between mass servicing probability and processing acoustic signal intensity for m=1,2,3,i; - correlation between mass servicing probability and the number of stations for acoustic source localization and identification. The obtained graphs help in the optimization of the system for acoustic source localization and identification. The conclusion gives some guidelines in applying this analysis and achieving optimal resources employment in combat environment.