Single vector hydrophone target direction estimation based on densenet
Consider the problem of bearing estimation as a multi label classification problem,apply dense connected networks to single vector hydrophone target bearing estimation,use second-order statistics widely concerned in classical methods as input to the neural network,and train the neural network using the method of continuously generating training sets.The simulation and lake trial results show that using DenseNet has narrower main lobes and higher azimuth resolution compared to classical methods;When the signal-to-noise ratio of two targets differs by 6 dB or more,the DenseNet has the ability to simultaneously detect two targets that classical methods do not have,and still has excellent azimuth resolution;When the signal-to-noise ratio difference between two targets exceeds 18 dB,the DenseNet gradually losses its ability to de-tect weak targets.
densenetsingle-vector hydrophonedirection of arrivalazimuth resolutionsignal-to-noise ratio
NETL
NSTL
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