A sequential ESM track association algorithm based on the use of information theoretic criteria

In this paper, a sequential track association algorithm for multiple electronic support measures (ESM) sensors is proposed based on the application of clustering techniques. Association metrics are developed based on the use of the information theoretic criteria, in particular, the Akaike Information Criterion (AIC). The proposed association algorithm is able to handle track components of differing numbers of subcomponents including incomplete and erroneous ones. It has the advantage of not requiring any subjective threshold setting in deciding a non-association. Computer simulation and actual ESM processors are used to demonstrate the effectiveness and performance of the proposed association approach.