Moufa Hu

1. Introduction Multi-target tracking is used to estimate the number of unknown time-varying targets and corresponding target states in environments that are subject to noise, false detection, and missed detection. Tracking all targets in a region of interest using a traditional single-sensor-based multi-target tracking approach is challenging because of single sensors’ limited sensing capabilities.

Abstract Object discrimination plays an important role in infrared (IR) imaging systems. However, at long observing distance, the presence of detector noise and absence of robust features make exo-atmospheric object classification difficult to tackle. In this paper, a recurrence-plots-based convolutional neural network (RP-CNN) is proposed for feature learning and classification. First, it uses recurrence plots (RPs) to transform time sequences of IR radiation into two-dimensional texture images.