Recognition of Pulse Repetition Interval of Multilayer Percetron Network Based on Multi-parameter TDOA Sorting

CHEN Tao; WANG Tianhang; GUO Limin

doi:10.11999/JEIT170913

Volume 40 Issue 7

Jul. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(7): 1567-1574

CHEN Tao, WANG Tianhang, GUO Limin. Recognition of Pulse Repetition Interval of Multilayer Percetron Network Based on Multi-parameter TDOA Sorting[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1567-1574. doi: 10.11999/JEIT170913

Citation:

CHEN Tao, WANG Tianhang, GUO Limin. Recognition of Pulse Repetition Interval of Multilayer Percetron Network Based on Multi-parameter TDOA Sorting[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1567-1574. doi: 10.11999/JEIT170913

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Recognition of Pulse Repetition Interval of Multilayer Percetron Network Based on Multi-parameter TDOA Sorting

doi: 10.11999/JEIT170913

CHEN Tao WANG Tianhang GUO Limin

Funds:

The National Natural Science Foundation of China (61571146), The Fundamental Research Funds for the Central Universities (HEUCFP201769), Weapon Equipment Pre- Research Foundation of China

Received Date: 2017-09-28
Rev Recd Date: 2018-01-10
Publish Date: 2018-07-19

Abstract

Abstract

In modern warfare, the radar system is developing rapidly. To recognize complex modulation mode of radar signal and hybrid pulse repetition interval radar, this paper proposes a sorting method based on multi station acquired pulse time-difference parameter combined with other pulse description words, taking advantage of the multi-station TDOA from the same emitter is similar to sort emitter signal pulse, and finally got the recognition result with the Multi-Layer Percetron (MLP) neural network. Traditional Pulse Repetition Interval (PRI) estimation algorithms estimate complex pulse interval modulation invalidly. In this paper, to solve this problem pulse time-difference parameter and other pulse description words are used. The feature vector of de-interlace pulse sequence is acquired and the result of pulse interval modulation type recognition is obtained with the trained MLP neutral network. Through experimental simulation, the correct recognition probability of the complex pulse interval modulation method is more than 90% in the case of the pulse loss rate is not more than 20%.
- Pulse repetition interval recognition、Time Difference Of Arrival (TDOA) sorting、Multi-Layer Perception (MLP)、Signal sorting,

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References

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