Given the problem of the direction of arrival (DOA) estimation of quasi-stationary signals, a sparse reconstruction algorithm based on the sparse array is proposed in this study to improve the DOA estimation performance. Specifically, the quasi-stationary signal is modelled based on the interleaved array (IA), where the algorithm makes full use of the high degree of freedom (DOF) and large array aperture of the interleaved array in the virtual domain. Then, the angle parameters are estimated depending on sparse reconstruction and the Khatri-Rao transform, which can achieve a higher number of DOF. Compared with the angle measurement algorithm based on uniform linear array and nested linear array, the algorithm proposed in this study not only increases the DOF of angle measurement significantly, but also improves the accuracy of angle measurement. Finally, numerical simulations demonstrate the validity of the proposed method.
Digital instruments are widely used in industrial control, traffic, equipment displays and other fields because of the intuitive characteristic of their test data. Aiming at the character recognition scene of digital display Vernier caliper, this paper creatively proposes an intelligent instrument recognition system based on multi-step convolution neural network (CNN). Firstly, the image smples are collected from the Vernier caliper test site, and their resolution and size are normalized. Then the CNN model was established to train the image smples and extract the features. The digital display region in the image smples were extracted according to the image features, and the numbers in the Vernier caliper were cut out. Finally, using the MINIST datas set of Vernier caliper is established, and the CNN model is used to recognize it. The test results show that the overall recognition rate of the proposed CNN model is more than 95%, and has good robustness and generalization ability.
To solve the problem of the mismatch of the wideband underdetermined direction of arrival (DOA) estimation under the condition of the On‐grid model, this paper extends the narrowband Off‐Grid model to wideband and proposes a new DOA estimation algorithm for Off‐Grid sources based on group sparsity. The proposed algorithm first obtains the preliminary estimation result under the current predefined discrete grid through the group sparsity wideband DOA estimation algorithm. Then, the Off‐Grid optimisation problem is adopted to calculate the Off‐Grid deviation vector. It is also assumed that the off‐grid deviation vectors of each frequency subband are exactly the same, thereby reducing the number of parameters to be estimated. Therefore, the proposed algorithm can not only maintain similar or even better estimation accuracy but also greatly reduce the computational complexity. Finally, simulation is conducted and the results verify the effectiveness and performance of the proposed method.
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