A broadband high resolution direction of arrival estimation algorithm based on conditional wavenumber spectral density

A broadband high resolution direction of arrival(DOA) estimation algorithm named the conditional wavenumber spectral density based(CWSD-based) is proposed in this paper aimed at the problem of large error and high complexity of wideband high resolution DOA.The super-resolution DOA estimation,minimal main-lobe width,ultra-low side-lobe and high precision target resolution performance can be obtained after transforming the array signals to f-k space and using the characteristic of wideband signal power distributed as a line on the conditional wavenumber spectral density.Moreover,there are no DOA pre-estimates of the incident signals and the number of sources to be required in this algorithm.Computer simulations indicate that the theoretical resolution of the algorithm is inversely proportional to the highest processing frequency,the estimated mean square error is about 0.1°,and also is robust to array shape distortion and has high computing efficiency.Experimental data on the sea indicates that the proposed algorithm,whose performance is superior to CBF and MVDR in the aspects of resolution,weak target detection,non-target direction noise suppression and robustness et al.,can achieve ultra-low sidelobe high resolution DOA in the actual ocean.

Underwater objects classification method in high-resolution sonar images using deep neural network

To solve the problem of underwater proud object classification using high-resolution sonar image under small sample situation,a classification method using deep neural network is proposed.Firstly,statistical characteristics of acoustic shadow regions are modeled using Gaussian mixture model and acoustic shadow is extracted.Trial and simulated dataset are constructed on this basis.Then,simulated dataset is input into convolutional neural network for training,and the feature extraction part is retained,which is used to extract feature of trial dataset.The classification part is reconstructed and trained by feature vectors of trial dataset.The experimental results show that the average classification accuracy of the proposed method is 88.24%,which is 8.67%,20.47%,19.78%,11.59%,9.01%,11.58% higher than that of other six methods respectively.It verifies that the proposed method achieves better performance on underwater proud object classification problem.The learning curve converges to 96.25%,which is 5.14% higher than validation curve,indicating that the over-fitting problem is alleviated to some extent.Improved convolutional neural network is applied in a fusion classifier,which also combines output of logistic classifier,support vector machine,and finally obtains a fusion result.The classification accuracy is up to 93.33%,indicating that fusion classifier improves robustness and classification performance of algorithm further.The proposed method combines deep learning and transfer learning,which not only utilizes powerful image classification ability of convolutional neural network,but also avoids serious over-fitting problem caused by limited dataset.