An Improved Deep Canonical Correlation Fusion Method for Underwater Multisource Data

In complex underwater environments, the single mode of a single sensor cannot meet the precision requirement of object identification, and multisource fusion is currently the mainstream research approach. Deep canonical correlation analysis is an efficient feature fusion method but suffers from problems such as not strong scalability and low efficiency. Therefore, an improved deep canonical correlation analysis fusion method is proposed for underwater multisource sensor data containing noise. First, a denoising autoencoder is used for denoising and to reduce the data dimension to extract new feature expressions of raw data. Second, given that underwater acoustic data can be characterized as 1-dimensional time series, a 1-dimensional convolutional neural network is used to improve the deep canonical correlation analysis model, and multilayer convolution and pooling are implemented to decrease the number of parameters and increase the efficiency. To improve the scalability and robustness of the model, a stochastic decorrelation loss function is used to optimize the objective function, which reduces the algorithm complexity from O(
$n^{3}$
) to O(
$n^{2}$
). The comparison experiment of the proposed algorithm and other typical algorithms on MNIST containing noise and underwater multisource data in different scenes shows that the proposed algorithm is superior to others regardless of the efficiency or precision of target classification.