A Fast and Automatic Extraction Method of Sequential Pulse Signal

Abstract

The invention discloses a method for rapidly and automatically extracting train pulse signals. By the adoption of the method, a single pulse can be accurately extracted from radar signals, frequency hopping signals and other communication signals in a continuous pulse train. The basic process for extracting pulses includes the steps that firstly, with the combination of various methods for analyzing the single pulse, smooth processing is conducted on signals; then, the width and the turn-off time of the pulses are detected, the downsampling scale is set, and secondary smooth filtering is conducted; the filtering scale is adjusted by detecting transition point segmentation pulses according to the right and wrong segmentation result, and secondary segmentation is conducted until segmentation is correct. By the adoption of the method, the single pulse can be quickly extracted from the continuous pulse train, threshold values are not needed for conducting threshold judgment, pulse position misjudgment caused by improper threshold value selection is avoided, the method for extracting the signal pulse is simple, and the amount of calculation is reduced.

Description

technical field [0001] The invention relates to a method for quickly and automatically extracting a single pulse sequence pulse signal from communication signals of continuous pulse sequences such as radar signals and frequency hopping signals. Background technique [0002] Pulse signal is a type of signal with rich content, the most vivid performance, and the most representative signal, which is used in many fields such as communication, radar, navigation, television, biomedicine, and instrumentation. Because the waveform parameters of the pulse signal are often used to describe its specific characteristic indicators, the waveform description method is generally used. In order to extract and describe the effective features of the signal from various perspectives such as time domain, frequency domain, and modulation domain, it is necessary to first extract a single pulse from a continuous pulse sequence. For the radar pulse signal, it is also an essential link to segment an...

AI Technical Summary

Problems solved by technology

This method has great limitations: there will be a large error in the calculation of the threshold. If the decision threshold is too high, there will be missed detection. If the decision threshold is too low, the noise peak will often exceed the recognition threshold, resulting in false alarms. , this limitation can seriously affect the accurate extraction of pulse

At the same time, the K-means algorithm uses fixed lower approximation weight wl and upper approximation weight wb. The parameter wl controls the influence of the samples in the lower approximation on the cluster center, and the parameter wb controls the influence of the samples in the upper approximation on the cluster center. , clustering is a dynamic process, with the change of the upper approximation and the lower approximation from the early stage to the late stage of the clustering process, the fixed empirical weight cannot well adapt to the characteristics of the early and late stages of clustering, and the algorithm is easily disturbed by abnormal noise points , a small amount of such data can have a large impact on the average

[0007] The above limitations can seriously affect the accuracy of the final determined threshold value, which in turn affects the determination of pulse and noise, thereby affecting the determination of pulse start and end positions

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