Modulation Signal Recognition of Underwater Acoustic Communication Based on Archimedes Optimization Algorithm and Random Forest
Abstract
:1. Introduction
2. Methodology
2.1. Archimedes Optimization Algorithm
2.1.1. Archimedes Principle
2.1.2. Initialization
2.1.3. Transfer Operator
2.1.4. Object’s Acceleration
2.1.5. Object’s Position
2.2. Random Forest
2.2.1. Determine the Number of DT
2.2.2. Determine the Depth of the DT
2.3. Overall Approach of this Study
3. Feature Parameter Extraction and Analysis
3.1. Feature Parameters Based on Instantaneous Information
3.1.1. Absolute Amplitude Standard Deviation
3.1.2. Absolute Frequency Standard Deviation
3.1.3. Absolute Phase Standard Deviation
3.1.4. Direct Phase Standard Deviation
3.2. Feature Parameters Based on Higher-Order Cumulant
3.3. Feature Parameters Based on a Spectral Line
3.4. Feature Parameters Based on Cyclic Spectrum
3.5. Feature Parameters Based on Autocorrelation
4. Simulation Results and Analysis
4.1. Simulation Environment and Results
4.2. Simulation Results under the BELLHOP Channel
4.3. Comparison of Different Classifiers
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Modulation Pattern | ||||
---|---|---|---|---|
2ASK | 1 | 1 | 2 | 32 |
4ASK | 1 | 1 | 1.36 | 27.52 |
2FSK | 0 | 0 | 1 | 16 |
4FSK | 0 | 0 | 1 | 16 |
2PSK | 1 | 1 | 2 | 32 |
4PSK | 1 | 0 | 1 | 16 |
Parameter Name | Numerical Value (First Group) | Numerical Value (Group 2) |
---|---|---|
Signal frequency | 10 khz | 10 khz |
Seawater depth | 345.78 m | 345.78 m |
Sound source depth | 100 m | 100 m |
Receiver depth | 120 m | 120 m |
Number of receivers in the horizontal direction | 50 | 50 |
Maximum transmission distance | 2.5 km | 5 km |
Seawater density (kg/m3) | 1024 | 1024 |
Sound speed of seawater (m/s) | 1518 | 1518 |
Methods | Accuracy Recognition Rate in Different SNR Environments (%) | |||||
---|---|---|---|---|---|---|
−10 | −8 | −6 | −4 | −2 | 0 (dB) | |
RF | 24.57 | 29.57 | 43.29 | 63.14 | 78.14 | 93.04 |
Higher-order cumulant-RF | 36.48 | 64.63 | 84.33 | 94.25 | 98.92 | 99.64 |
Wavelet Transform | 56.00 | 64.63 | 84.33 | 94.25 | 98.92 | 99.64 |
Instantaneous feature-RF | 59.12 | 73.11 | 81.02 | 87.59 | 100 | 100 |
BPNN | 64.57 | 69.57 | 87.07 | 91.14 | 100 | 100 |
SVM | 71.09 | 84.06 | 95.10 | 100 | 100 | 100 |
AOA-RF | 76.50 | 85.21 | 96.23 | 99.71 | 100 | 100 |
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Wang, M.; Zhu, Z.; Qian, G. Modulation Signal Recognition of Underwater Acoustic Communication Based on Archimedes Optimization Algorithm and Random Forest. Sensors 2023, 23, 2764. https://doi.org/10.3390/s23052764
Wang M, Zhu Z, Qian G. Modulation Signal Recognition of Underwater Acoustic Communication Based on Archimedes Optimization Algorithm and Random Forest. Sensors. 2023; 23(5):2764. https://doi.org/10.3390/s23052764
Chicago/Turabian StyleWang, Maofa, Zhenjing Zhu, and Gaofeng Qian. 2023. "Modulation Signal Recognition of Underwater Acoustic Communication Based on Archimedes Optimization Algorithm and Random Forest" Sensors 23, no. 5: 2764. https://doi.org/10.3390/s23052764