Signal clustering method

Abstract

The invention relates to the electromagnetic environment signal monitoring field and particularly relates to a signal clustering method. The method comprises steps that firstly, a pulse description word PDWi is read by a receiver, according to the orientation Doai and the carrier frequency Rfi of the pulse descriptor PDWi, the PDWi index address is calculated; secondly, the content stored in the PDWi index address is determined, if the lower-adjacent index address of the orientation Doai has no channel number mark, the upper-adjacent index address of the carrier frequency Rfi of the same orientation Doai is detected; the content stored in the index address is determined, whether the index address has a channel number mark is determined, if the upper-adjacent index address of the carrier frequency Rfi has no channel number mark, the lower-adjacent index address of the carrier frequency Rfi of the same orientation Doai is detected. An index address hash table method is provided, detectedsignals can be timely and rapidly clustered, 100% signal acquisition is guaranteed, moreover, the upper and lower-adjacent relationship of the orientation Doai and the carrier frequency Rfi are further applied, a carrier of the signals is not split due to the motion or the frequency agility of radar signals, and correct signal sorting can be guaranteed for subsequent signal processing.

Description

technical field [0001] The invention relates to a signal clustering method in the field of electromagnetic environment signal monitoring. Background technique [0002] Due to the dense, complex and changeable modern electromagnetic environment, the received signals are very dense. If these signals are not classified and diluted, it is difficult to quickly separate the signals, especially high repetition frequency signals and low repetition frequency signals. When the signals are mixed together, the low-repetition-frequency signals will be submerged and cannot be sorted out; in order to adapt to the external pulse density of 1 million per second, the processing time for each pulse should be less than 1us, so that the pulse will not be lost, ensuring 100% signal acquisition; there are many existing clustering methods, such as: neural network, vector machine, Kalman filter, etc. These methods take a lot of time and are difficult to be widely used in engineering. Contents of t...

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Problems solved by technology

[0002] Due to the dense, complex and changeable modern electromagnetic environment, the received signals are very dense. If these signals are not classified and diluted, it is difficult to quickly separate the signals, especially high repetition frequency signals and low repetition frequency signals. When the signals are mixed together, the low-repetition-frequency signals will be submerged and cannot be sorted out; in order to adapt to the external pulse density of 1 million per second, the processing time for each pulse should be less than 1us, so that the pulse will not be lost, ensuring 100% signal acquisition; there are many existing clustering methods, such as: neural network, vector machine, Kalman filter, etc. These methods take a lot of time and are difficult to be widely used in engineering

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