Direction-of-Arrival (DOA) estimation with sparse non-uniform linear arrays (SNLAs) has recently attracted a growing concern for the reason that more sources than the number of physical sensors can be resolved as compared with the conventional uniform linear arrays (ULAs). In this paper, we develop an underdetermined DOA estimation method using unfold coprime array (unfold CPA) from the perspective of sum-difference co-array (SDCA). By jointly using the temporal and spatial information of the impinging sources and the Khatri-Rao (KR) product operation, the SDCA of the unfold CPA are obtained, which can provide more consecutive DOF and larger array aperture than the SNLAs based on the difference co-array (DCA). Additionally, benefitting from the array configuration of the unfold CPA, the weight functions of the physical sensor pairs with small separations are equal to zero, thus the mutual coupling effects between sensors are reduced significantly. The theoretical propositions and numerical simulations are provided to confirm the effectiveness and superior performance of the proposed method.

Design of the intelligent making system (iMAS) poses a big technical challenge to recognize both printed and handwriting characters from the scanned images. In this paper, we propose an iMAS method based on the attention generative adversarial network (GAN), which innovatively applies the idea of removing raindrops through attention GAN to remove the printed text first, and then generates the image of handwriting text. The proposed method introduces a classical attention model in the visual field and generates the attention maps which focus only on the important areas through recurrent neural network (RNN), and adopts you only look once at object detection (YOLOv3) method to recognize the characters. Experimental results show that the structural similarity (SSIM) of the image generated by attention GAN is 0.89 and the accuracy of recognition is 91.34%.

An advanced source number estimation (SNE) algorithm based on both fuzzy C‐means clustering (FCM) and data local density (DLD) is proposed in this paper. The DLD of an eigenvalue refers to the number of eigenvalues within a specific neighborhood of this eigenvalue belonging to the data covariance matrix. This local density essentially as the one‐dimensional sample feature of the FCM is extracted into the SNE algorithm based on FCM and can enable to improve the probability of correct detection (PCD) of the SNE algorithm based on the FCM especially for low signal‐to‐noise ratio (SNR) environment. Comparison experiment results demonstrate that compared to the SNE algorithm based on the FCM and other similar algorithms, our proposed algorithm can achieve highest PCD of the incident source number in both cases of spatial white noise and spatial correlation noise.

In this paper, we propose a parameter estimation method named improved Lv distribution (ImLVD) for linear frequency modulated (LFM) signals. In this method, based on the scaling principle, we present a two-scale estimation strategy to acquire low computational cost. The two-scale estimation strategy includes coarse estimation operation and fine estimation operation. To implement the strategy, improved scaled Fourier transform (ISFT) and Chirp-Z transform (CZT) operation are used, where the ISFT can be implemented by fast Fourier transform (FFT) and complex multiplications. By changing the frequency searching range of ISFT, the ISFT is used in different frequency ranges. To improve the anti-noise, we present an improved parameter selection criterion which can reduce the noise correlation effectively. In this paper, the implementation, anti-noise performance, and computational cost are analyzed. Through simulations and analyses, we demonstrate that the ImLVD outperforms the compared algorithms.

This paper presents an effective 3D digitalization technique to reconstruct an accurate and reliable 3D environment model from multi-view stereo for an environment-learning mobile robot. The novelty of this paper lies in the introduction of nonrigid motion analysis to stereo reconstruction routine. In our proposed scheme, reconstruction task is decoupled into two stages. Firstly, range depth of feature points is recovered and in turn is used for building a polygonal mesh and secondly, projection feedback on comparison views, which is generated on assumption of the established coarse mesh model, is carefully introduced to deform the primitive mesh model so as to improve its quality dramatically. The discrepancy of observation on comparison views and the corresponding predictive feedback is quantitatively evaluated by optical flow field and is employed to derive the corresponding scene flow vector field subsequently, which is then used for surface deformation. As optical flow vector field estimation outperforms traditional dense disparity for its inherent advantage of being robust to illumination change and being optimized and smoothed in global sense, the deformed surface can be improved in accuracy, which is validated by experimental results.