# 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.  
infrared signature; spatial point target; time series; recurrent networks +
Footnote †: journal: Medical  
of the target. The infrared signature is influenced by a range of factors, such as wavelength range, LOS (line of sight) orientation, shape, and temperature of targets [9; 10].  
In the field of machine learning, Artificial Neural Networks (ANNs) have superior feature learning and data representation capabilities [11; 12]. RNNs is an improved structure of feed-forward ANNs which has time-recurrent structures and memory ability of previous information. Moreover, RNNs algorithm has a simple structure, high computational efficiency, and low computational and storage resources [13]. However, the RNNs algorithm only focuses on local information essentially, especially when using the error back-propagation method to train the network, which inevitably limits the RNNs' grasp of the overall information of the sequence, hindering its ability to learn complex decision functions [13; 14]. The widely used LSTM algorithm is capable of selectively remembering and forgetting data, and retaining hidden important data and features for a longer period of time. By using the \"processor\" in the LSTM algorithm to determine whether the information is important, the information of the target data waveform is forced to \"memorize\" and \"forget\" by learning the training method [15; 16; 17]. However, for both RNNs and LSTM, the ability to process long sequences is limited, which is not conducive to the characteristics of the periodicity of the time series data [18].  
In the research of spatial targets classification, it is necessary to propose a more effective classification algorithm for the infrared radiation intensity sequence characteristics based on the actual situation [19]. Based on the recurrent structure of RNNs, this paper proposes an IRRNN model, which adopts an independent structure in the hidden layer, so the unsaturated activation function can be used to solve the problem of gradient vanishing and gradient explosion. At the same time, our model introduces the historical output information into the input layer in the form of random weighting, which is the direction that the data tends to be easy to classify [20].  
The rest of the paper is organized as follows. An infrared signature model is constructed in Section 2 and simulation has been conducted. Discrimination of spatial point targets based on IRRNN is put forward in Section 3, followed by experiments and a discussion in Section 4. Conclusions are presented in the last section.