KEYWORDS: Reconstruction algorithms, Time-frequency analysis, Radar, Reconnaissance, Digital filtering, Detection and tracking algorithms, Signal processing, Signal to noise ratio, Radar signal processing
In order to solve the problem of MIMO radar underdetermined blind separation under the condition of overlapping in time domain, frequency domain and time-frequency domain, a blind separation algorithm based on sparse smooth reconstruction and ridge estimation is proposed. Firstly, underdetermined blind signal separation is simplified to signal reconstruction with known mixed matrix. Secondly, using the time-frequency sparsity of MIMO radar signal, a time frequency smooth sparse reconstruction algorithm is proposed, and the time-frequency information is modified by the ridge estimation method. Finally, the time-frequency source signal is recovered by inverse transformation. The algorithm can reconstruct the coding information of source signal and radar signal at the same time, which provides a new way to solve the problem of radar signal separation in complex electromagnetic environment.
In the field of cognitive electronic warfare, automatic feature learning and recognition of radar signal is an important technology to ensure intelligence reconnaissance. This paper analyses the basic structure of convolutional neural network (CNN) and proposes an automatic recognition algorithm for radar signal. Firstly, the radar signal is transformed into time-frequency image, and the principal component information of the image is extracted by image processing method. Then, the designed network CNN-LeNet-5 is used to realize self-learning and recognition of features. The simulation results show that the algorithm can effectively identify eight kinds of radar signals in low signal-to-noise ratio.
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