Channel (broadcasting)

Geology

Pattern recognition (psychology)

Demodulation

Computer science

Gene

Random forest

Acoustics

Feature extraction

Oceanography

Speech recognition

Biochemistry

Telecommunications

Modulation (music)

Artificial intelligence

Robustness (evolution)

Underwater

Frequency-shift keying

Chemistry

Keying

Orthogonal frequency-division multiplexing

Physics

Remote Sensing

The modulation mode recognition of non-cooperative underwater acoustic (UWA) communication signal faces great challenges due to the influence of the UWA channel and the demand for efficient recognition. This work proposes a recognition method for UWA orthogonal frequency division multiplexing (OFDM), binary frequency shift keying (2FSK), four-frequency shift keying (4FSK), and eight-frequency shift keying (8FSK) by using spectral peak feature extraction combined with random forest (RF). First, a new spectral peak feature extraction method is proposed. In this method, pre-processing, waveform optimization, and feature extraction are used to ensure that the extracted feature maintains high robustness in the UWA channel. Then, we designed an RF classifier that can meet the demand for high-efficiency recognition and good performance. Finally, simulation and experimental results verified the feasibility of the recognition method.

Modulation Mode Recognition Method of Non-Cooperative Underwater Acoustic Communication Signal Based on Spectral Peak Feature Extraction and Random Forest