A predicate model of process knowledge in detecting and recognizing the burst structure of signals from aircraft in surveillance radars

Abstract

A model of process knowledge in detecting and recognizing the burst structure of air object signals in the intelligent surveillance radar systems is presented. The relevance of this work is to improve the efficiency of radar monitoring systems and moving object control systems by creating an universal algorithm for automating the process of detecting and recognizing weak useful signals under conditions of interfering influences. The developed predicate model of process knowledge includes procedures to form and analyze the geometric signal image of point objects and decision-making the observed objects. Based on this model of process knowledge, a case-based decision-making method has been developed. The process knowledge of converting radar signals into the burst structure symbolic images of the point moving air objects is formalized. It is possible to build various types of links between information units in the proposed model. First of all, these links characterize the relations between information units; the semantics of these relations is both declarative and procedural. On the other hand, the process is described both by functional relations and relations between information cells. The processing knowledge of signal mark models for air objects such as an airplane, a helicopter, an unmanned aerial vehicle (UAV) is generated, taking into account declarative and procedural predicate signs. The proposed model includes a system of predicate equations, as a result of their solution, the burst structure type of the moving object signals and a list of procedural and semantic operations for processing knowledge are determined. It is shown that to detect and recognize the burst structure of signals, the production or combined models are most applicable; this type of model uses some elements of logical and network models.