18th International Conference on Innovative Computing, Information and Control (ICICIC2024)
Onsite (Qinhuangdao, China) and Online, September 10-13, 2024

PS-27: ICICIC2024-SS02-02
Specific Emitter Identification of Burst Signals Based on Transient Fingerprint Feature
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2

Haiyan Luan *, Lin Wang and Chuanyu Xia
1

2Yantai Institute of Technology, Yantai 264003, P. R. China
Yishang Electronic Technology Co., Ltd., Yantai 264003, P. R. China*Corresponding author: abaoliu@126.com

Introduction

Mathematical Formulas

Specific emitter identification (SEI) is thetechnology only using the external features of thereceived signals to identify the emitters. The SEI
methods based on transient features usually havethree steps, which are detection of transient process,feature extraction and features classification. Capturing
the transient process of burst signals with channel
noise accurately is one of the key issues for SEI
algorithms based on transient process, which directlyaffects the recognition accuracy. Modeling andanalyzing the non-ideality features during the processof turning on-off the transmitter, and extracting thedifferent features, is the core issue of SEI algorithms.

L(r ) =

p1 (r )
p0 (r )
2==
∑i 1 =∑ k i rk*ck rk −i ck*−i =∑i 1=∑ k i rk rk −i
PL −1

PL −1

PL −1

PL −1

2

θˆ = (ΦT Φ ) −1 ΦT RFigures
1Research Questions

0.9

0.8

0.7

identication accuracy

The following two questions are focused on:1) The detection of the transient process of theburst signals;
2) The fingerprint feature estimation of theemitters from one manufacture with the samemodel.

0.6

0.5

0.4

0.3
02

4

6

8

1012141618

deviation of transient estimation

Figure 1. Relationship between estimation accuracy anddeviation of transient

Methodologies
The paper provides a transient process detection
algorithm based on maximum likelihood estimation, andthe transient distortion model for burst signals isestablished. The instantaneous amplitude distortion
feature extraction algorithm and support vector machine
based feature classification method are presented.

TablesTable 1. The identification of the test samples of fourradio devices
ResultRatio1

Ratio 2

Ratio 3

Ratio 4

Recognition
accuracy

Ratio 1

1970030.985

Ratio 24196000.98

Ratio 30020001Ratio 4300197

0.985

IDConclusion
The turn on-off feature of the transmitter is an
important component of fingerprint features and plays avery important role in SEI. This paper studies thedetection algorithm for transient processes of sudden
signals, and uses actual data to analyze the impact oftransient process detection accuracy on recognition
accuracy; By establishing a transient process distortion
model, a transient distortion feature extraction algorithm
is derived, and transient process instantaneous amplitude
distortion features are extracted. The effectiveness of thealgorithm is verified through experiments on four actual
collected UHF radio, with an average recognition
accuracy of 98.75%. This algorithm is suitable forindividual identification of burst signal, and has highrecognition accuracy.
This paper develops the transmitter as a linear
constant coefficient mathematical model. The next
research work will focus on building of the nonlinearity
mathematical model and proposing the corresponding
method to estimate the character parameters.