According to the receiving array size, the underwater acoustic
positioning system can be divided into three positioning systems: long
baseline (LBL), short baseline (SBL) and ultra-short baseline (USBL).
Acoustic detection and positioning technology based on ultra-short
baseline is the most effective means to collect and transmit
information, and is an effective way to solve the positioning accuracy
of underwater vehicles. In this paper, the short baseline positioning
and navigation system is studied deeply, and an underwater acoustic
positioning algorithm based on extended Kalman filter is proposed. By
fusing the information of pressure gauge, digital compass and
accelerometer of the robot, the positioning accuracy is improved and the
influence of underwater channel multipath effect on the system is
reduced.
With the economic and military development of today’s era, more and more
fields have higher and higher requirements for the positioning accuracy
of underwater vehicles. In order to solve the optimization of long
baseline trajectory smoothing, the intersection algorithm of simplified
model and the optimization algorithm based on redundant
measurement-Gaussian Newton method are studied, the comparison between
Kalman prediction and alpha prediction and smoothing optimization
algorithm in the Ca and CV states, and the Kalman filtering, alpha
filtering and interactive multi-model (IMM) filtering algorithm in the
maneuvering state, simulation experiments show that the IMM algorithm
smoothing effect is better, and the Kalman filter algorithm of the CV
model has a small error.
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