A method for frequency identification of underwater acoustic keying frequency shift signal

Abstract

The invention discloses a method for frequency identification of underwater acoustic keying frequency shifting signals. The present invention adopts the method of combining a dual-channel ultra-narrow band band-pass filter and a limiting comparator to separate the underwater acoustic remote control signal and environmental interference noise, and reduce the influence of the multipath effect; by capturing the time width of the pulse signal and The number of rising or falling edges directly calculates the period and frequency of the signal, avoids the discrete Fourier transform (DFT) operation, avoids the convolution operation in the conventional signal frequency identification method, and avoids large-scale floating-point operations, making The operation is realized on a low-power single-chip microcomputer hardware circuit platform, so that the scale and power consumption of the signal processing system are controlled, and the real-time performance of the processing results is improved.

Description

technical field [0001] The invention relates to the technical field of frequency identification, in particular to a method for frequency identification of underwater acoustic keying frequency shift signals. Background technique [0002] Underwater acoustic remote control and underwater acoustic communication are a method of transmitting information using underwater acoustic channels, and underwater acoustic channels are random fading channels that are uneven, time-varying, and space-varying, with multi-path effects and reverberation effects The effect of the ocean environment will cause distortion and fluctuations in the sending and receiving signals of the underwater acoustic transducer. At the same time, the noise of the ocean environment is also a kind of interference to the underwater acoustic signal. From the perspective of communication theory, the frequency shift keying (FSK for short) technology with small frequency shift is beneficial to resist channel fluctuation a...

AI Technical Summary

Problems solved by technology

However, the discrete Fourier transform must use the convolution operation. When implementing the algorithm on the embedded system platform, a large number of floating-point operations are involved, which takes up more hardware resources and affects the real-time performance of signal processing.

At the same time, the above method has higher requirements on the hardware circuit platform, and the system scale and power consumption requirements are also increased accordingly.

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