Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification

Abstract

Exo-atmospheric infrared (IR) point target discrimination is an important research topic of space surveillance systems. It is difficult to describe the characteristic information of the shape and micro-motion states of the targets and to discriminate different targets effectively by the characteristic information. This paper has constructed the infrared signature model of spatial point targets and obtained the infrared radiation intensity sequences dataset of different types of targets. This paper aims to design an algorithm for the classification problem of infrared radiation intensity sequences of spatial point targets. Recurrent neural networks (RNNs) are widely used in time series classification tasks, but face several problems such as gradient vanishing and explosion, etc. In view of shortcomings of RNNs, this paper proposes an independent random recurrent neural network (IRRNN) model, which combines independent structure RNNs with randomly weighted RNNs. Without increasing the training complexity of network learning, our model solves the problem of gradient vanishing and explosion, improves the ability to process long sequences, and enhances the comprehensive classification performance of the algorithm effectively. Experiments show that the IRRNN algorithm performs well in classification tasks and is robust to noise.

A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

“…However, since the gray value of the target and the radiation can be considered linear within the normal operation of the infrared detector, it is still essentially using the infrared radiation of the target for recognition. These papers [14,15] also only used infrared radiation as input data for target recognition; the difference lies in the recognition algorithms.…”

Special Issue “Emerging Artificial Intelligence (AI) Technologies for Learning”

“…Sun et al [8] adopt a generative adversarial network to enhance classification, while Qu et al [9] use an LSTM neural network model and data mining techniques to predict student achievements. Wu et al [10] employ recurrent neural networks for the infrared spatial point target classification problem. Finally, Do et al [11] propose an ontology model forming the knowledge bases for intelligent problem solvers in many mathematical courses.…”