The electric field is the intrinsic characteristic of a ship. The measurement of the electric field of a ship is greatly
disturbed by noise in shallow sea. The measured signals can be processed using wavelet transform, adaptive filtering or
time-frequency analysis. Adaptive filtering can be used to remove a part of ambient noise. But the filtering effect is not
obvious because a certain type of industrial interference signal is also of an extremely low frequency, When wavelet
transform is used to remove noise, most noises from the electric fields of sea are removed. However, it also has nothing
to do with the periodical noises with an extremely low frequency. As far as time frequency method is concerned, the
original signals are first analyzed within the frequency band, and then they are compared with the measured ambient
signals. After the range of shaft-modulated signals are found, the signals of required frequency band are extracted for
further IFFT transform. The processing results show that the traffic characteristic of a ship is very clear, representing the
signals are extracted out.
As an intrinsic characteristic, the shaft-rate modulated electric field of a ship is an important signal source in underwater domain. A
measurement system is designed to measure the shaft-rate modulated electric field of a ship. Taking into consideration the pressure
bearing, watertightness and other problems, the system is made up of sensors, signal condition circuits, and data acquisition and
processing system. After the measurement system is sunk down to the seabed, it is difficult to regulate orientation, attitude and other
sensor parameters. It is also hard to control the environment around the system. Therefore, the status information of the seabed is
recorded for posterior calibration. Frequency analysis and time-frequency analysis methods are used for signal processing. Experiment
results show that the spatial distribution of shaft-rate modulated electric field has a significant regional property and apparent traffic characteristic.
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