Abstract
Sensing the external complex electromagnetic environment is an important function for cognitive radar, and the concept of cognition has attracted wide attention in the field of radar since it was proposed. In this paper, a novel method based on an idea of multidimensional feature map and convolutional neural network (CNN) is proposed to realize the automatic modulation classification of jamming entering the cognitive radar system. The multidimensional feature map consists of two envelope maps before and after the pulse compression processing and a time-frequency map of the receiving beam signal. Drawing the one-dimensional envelope in a 2-dimensional plane and quantizing the time-frequency data to a 2-dimensional plane, we treat the combination of the three planes (multidimensional feature map) as one picture. A CNN-based algorithm with linear kernel sensing the three planes simultaneously is selected to accomplish jamming classification. The classification of jamming, such as noise frequency modulation jamming, noise amplitude modulation jamming, slice jamming, and dense repeat jamming, is validated by computer simulation. A performance comparison study on convolutional kernels in different size demonstrates the advantage of selecting the linear kernel.
Highlights
Cognitive radar proposed in [1] senses the external electromagnetic environment by recognizing the external emitters and accomplishes antijamming, clutter suppression, and target detection based on the environment information and prior knowledge
Multiple signal classification (MUSIC) algorithm [9] is usually used to measure the direction of the interferences, and the Gerschgorin disk algorithm [10] is employed to estimate the number of the interferences
The sensing of jamming is composed of four parts: (1) jamming power measurement using digital beamforming (DBF); (2) jamming bandwidth measurement using fast Fourier transform (FFT); (3) direction of arrival estimation using multiple signal classification (MUSIC); (4) automatic modulation classification of jamming (AMCOJ)
Summary
Cognitive radar proposed in [1] senses the external electromagnetic environment by recognizing the external emitters and accomplishes antijamming, clutter suppression, and target detection based on the environment information and prior knowledge. E perception of jamming is implemented after digital beamforming (DBF) or pulse compression of the array data In this example, the sensing of jamming is composed of four parts: (1) jamming power measurement using DBF; (2) jamming bandwidth measurement using fast Fourier transform (FFT); (3) direction of arrival estimation using MUSIC; (4) automatic modulation classification of jamming (AMCOJ). E study in [25] accomplishes feature extraction using short-time Fourier transform and AMC based on support vector machine of various types signals, such as single frequency, liner frequency modulation, binary frequency shift keying, 4-frequency shift keying, BPSK, and 16QAM. On the basis of the complex jamming environment faced by cognitive radar, this paper utilizes the deep learning technology based on CNN to carry out the classification of jamming modulation automatically, realizing jamming environment perception for cognitive radar. Is paper mainly focuses on the radar environment perception processing consisting of multidimensional feature map construction and CNN model design.
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