A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

Abstract

Target recognition mainly focuses on three approaches: optical-image-based, echo-detection-based, and passive signal-analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an “end-to-end” model that cascades a noise estimation network with a recognition network to identify working modes in noisy environments. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signals, the recognition network is realized based on the multi-scale convolutional attention network (MSCANet). Firstly, deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, the finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem of the conventional method being seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environment, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, the proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

Approach to processing radio signals with amplitude modulation of many components using one-dimensional convolutional neural network

“…In comparison with the models described in [4,5], the constructed model demonstrates the ability to work with pseudorandom signals. Compared to models in [8,10], it…”

“…The limitation of such a model is that although one-dimensional convolutional net-works have a good ability to detect both local and global features, the uncertainty of noise can significantly impair the effectiveness of the model. In [5], a cascade model is proposed, in which, for greater efficiency of the analysis of radar pulses, noise parameters are also analyzed, which makes it possible to determine the operating modes of radars under more unstable transmission conditions. Work [6] proposes a model that also estimates the data transmission channel but is able to detect signals in non-orthogonal OFDM multiple access systems.…”

Improved Main Lobe Cancellation Method for Suppression Directional Noise in HFSWR Systems

“…In [31], the wideband linear array white noise is reduced by a judiciously designed spatial transformation followed by a bank of highpass filters. J. Xiong et al [32] presented a cascade model consisting of a noise estimation network based on U-Net and a recognition network based on MSCANet. It can recognize the working mode of the radar in a harsh noise environment.…”