New infrared counter-countermeasure technique using an iterative self-organizing data analysis algorithm for the rosette scanning infrared seeker

Abstract

Conventional infrared counter-countermeasure (IRCCM) techniques against flares in the rosette scanning infrared seeker (RSIS) are based on the image-processing algorithms such as the clustering and moment techniques. We analyze the conventional IRCCM techniques and find that they can handle only a limited number of clusters and may falsely recognize multiple targets as single ones. To overcome these limitations, we propose a new IRCCM technique using an iterative self-organizing data analysis algorithm. The proposed IRCCM classifies image clusters and calculates their centroids regardless of their numbers, since the ISODATA technique can handle an arbitrary number of clusters. The nonlinearity of the rosette pattern causes the calculated centroid of the target cluster to lean toward the middle of the pattern, i.e., the optical axis. For a precise determination of the cluster centroid, we also propose a new calculation method based on a weight function. We simulate the RSIS to evaluate the tracking performance in various situations. The simulation results prove that the proposed algorithm is very effective for IRCCM against flares. (C) 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)00609-7].