With the increasing complexity of the electromagnetic environment of electronic countermeasures, the detection of radar pulse signals under low signal-to-noise ratio is becoming more and more demanding. Based on this detection performance requirement, an adaptive dynamic double-threshold signal detection algorithm is proposed based on the digital channelization and autocorrelation algorithms. The high threshold is used to detect signals with higher signal-to-noise ratios, ensuring better real-time signal detection. The low threshold is used to detect possible low signal-to-noise signals, ensuring better detection of weak signals. Simulation experiments are carried out on the algorithm, and the results show that the algorithm can effectively improve the real-time and weak signal detection ability of signal detection.
KEYWORDS: Radar signal processing, Pulse signals, Signal processing, Windows, Radar sensor technology, Signal detection, Signal attenuation, Electromagnetism, Tolerancing, Process modeling
Radar signal sorting is a key technology in electronic warfare. The quality of signal sorting directly determines the outcome of electronic warfare. The accuracy and real-time performance of signal sorting are two important indicators to evaluate the quality of signal sorting. With the increasingly complex electronic countermeasure environment, there are many radar radiation sources in the battlefield, and the signal aliasing is serious. At the same time, there may be pulse loss and interference pulse in the electromagnetic environment. In such a complex electromagnetic environment, in order to improve the accuracy and real-time of radar signal sorting and master the battlefield initiative, this paper proposes a method of radar signal batch processing sorting based on sliding window. Firstly, the radar signal sorting transient library and the sorted radar signal library are set up, and the transient library is used to store radar signals in the sliding window. Move the sliding window continuously from the starting point. When the number of signals in the transient library is higher than the specified threshold, sort the radar signals in the transient library, and then store the sorted radar signals in the sorted radar signal library and delete them from the transient library. When the sliding window moves to the end point, the signals in the sorted radar signal library are merged. Simulation results show that compared with traditional methods, the proposed method has higher sorting accuracy and better real-time performance when there are more radar pulses and pulse loss.
KEYWORDS: Radar signal processing, Education and training, Machine learning, Deep learning, Signal to noise ratio, Signal attenuation, Time-frequency analysis, Detection and tracking algorithms, Convolutional neural networks, Convolution
In view of the fact that the current radar signal modulation recognition method based on depth learning needs enough samples to achieve good recognition effect, this paper designs a depth metric learning recognition method based on Cosine Softmax (CS Softmax), which has strong intra class aggregation and inter class separation, and can recognize unknown signals. First, the Wigner Ville distribution (WVD), Pseudo Wigner Ville distribution (PWVD) and Smooth Pseudo Wigner Ville distribution (SPWVD) of known signals are taken as the input, the vectors obtained from the network output are learned into the embedded space to identify radar signals, and several unknown signals are combined with the initial training set in the verification phase. The simulation results show that under the signal to noise ratio of 10dB, the recognition accuracy of the known signal of this method can reach about 94%, can distinguish the unknown signal from the known signal, and can distinguish the unknown signal separately, with an accuracy of about 85%.
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