This paper presents a method of fusion of identification (attribute) information provided by ELINT – ESM sensors (Electronic Intelligence – Electronic Support Measures). In the first section the basic taxonomy of attribute identification in accordance with the standards of STANAG 1241 ed. 5 and STANAG 1241 ed. 6 (draft) is adopted. These standards provide the following basic values of the attributes of identification: FRIEND, HOSTILE, NEUTRAL, UNKNOWN and additional values: ASSUMED FRIEND and SUSPECT. The last values can be interpreted as a conjunction of basic values. The basis of theoretical considerations is the Dezert-Smarandache theory (DSmT) of inference. This paper presents and practically uses combining identification information from different ELINT – ESM sensors one of the information fusion rules proposed by the DSmT - the Proportional Conflict Redistribution #5 rule (PCR5). In the next section rules of determining attribute information by ESM sensor equipped with the data base of radar emitters are presented. It was proposed that each signal vector sent by the ELINT-ESM sensor contained an extension specifying a randomized identification declaration (hypothesis). This declaration specifies the reliability of the identification information - basic belief assignment (bba) for the identification information set. This paper presents a method of determining this belief assignment based on the distance between recognized signal features, vectors and centers of clusters grouping emitter patterns in the pattern data base. Results of the PCR5 rule of sensor information combining for two scenarios are presented in the final part of this paper. Conclusions are given at the end of this paper. They confirm the legitimacy of the use of the Dezert-Smarandache theory into information fusion for ESM sensors.
This paper presents a method of recognition of maritime objects based on their images made by infrared sensors (FLIR – forward looking infra-red) using the time series comparison DTW method (DTW - Dynamic Time Warping). The DTW method allows to find the smallest distance between two time series when the run of time one of the series has been deformed (stretched or compressed). In the presented classifier of maritime objects images the DTW method is used to compare the combined horizontal and vertical brightness histograms for a recognized object and pattern objects. The DTW method allows to compare the histograms of objects whose FLIR images were taken at different angles. To determine the silhouette of a maritime object the Otsu segmentation algorithm is used in this paper. The paper describes the Otsu threshold method, the method of comparing time series DTW and the method of constructing combined histograms of maritime objects silhouettes. The final part of the paper presents the results of research on the developed method of maritime objects classification using a set of FLIR images registered in the Baltic Sea.