To solve the problem that current dynamic intention recognition methods fail to make full use of time domain variation information between multi-temporal group targets, which leads to low accuracy of intention recogni...
To solve the problem that current dynamic intention recognition methods fail to make full use of time domain variation information between multi-temporal group targets, which leads to low accuracy of intention recognition. This paper proposes a bidirectional convolutional long short term memory-attention network for marine formation target intention recognition. The network takes the multi-source trajectory data of the marine ship formation target as the input, extracts and uses the target change rule and time domain characteristics of the Marine formation data, and trains the model to have the ability to independently learn the weight of information in different time periods. The simulation results show that the method has good performance and can meet the needs of practical application.
Oriented towards the requirements for reliable positioning and navigation of aircraft under the condition of rejection of navigation satellite, we proposed cross-domain guide positioning methods based on multi-layer n...
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The simulation of aircraft guidance and controlsystem has characteristics such as multiple model classification, large parameter influence,complex information interaction and so on. The traditional code-level model d...
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Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with backgroun...
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Synthetic aperture radar (SAR) image registration is widely used in integrated navigation system with development of guidance system in sensors and other applications. However, there exists some difficulty because of ...
Synthetic aperture radar (SAR) image registration is widely used in integrated navigation system with development of guidance system in sensors and other applications. However, there exists some difficulty because of the quality and imaginary principle of SAR images. In this article, a brand-new method based on feature points using improved CSP-DenseNet is proposed to solve the problems of SAR image registration with weak and noisy texture. Deep features of interest points in SAR images are extracted using the proposed network from the crops of search image and template image respectively. The method has an advantage of preserving abundant feature information in SAR images under the influence of insufficient image information and noise of SAR images. The CSP-DenseNet architecture has a cross-stage construction optimizing fused by CSP-Net and DenseNet to extract matching features. The network is partially connected in specific convolutional layers for feature reusing and computation resources saving. Then, Brute Force Matcher and RANSAC voting are successively applied for a larger number of matching pairs and high-precise matching vertexes. Experimental results on various SAR image matching methods show that the proposed method provides better performance than other approaches compared.
In this paper, an ultra-wideband conical equiangular spiral antenna has been designed. The presented antenna covers an operating bandwidth of f-{0}-10f-{0}. In the design, by loading absorbing materials, the voltage s...
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This paper studies the output feedback consensus problem of linear multi-agentsystems with Markovian switching topologies. To avoid reconstructing the Markovian switching topologies during designing the observers or ...
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Due to the strong scattering intensity of the apex of the carrier, a scheme of rounding the apex of the carrier is proposed. In this design, it is embodied in face fillet and line fillet. Based on the above designs, t...
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A stable finite-difference time-domain (FDTD) sub-grid ding method using the summation-by-parts simultaneous approximation term (SBP-SAT) technique is proposed. To meet the SBP properties and keep Yee's grids unch...
A stable finite-difference time-domain (FDTD) sub-grid ding method using the summation-by-parts simultaneous approximation term (SBP-SAT) technique is proposed. To meet the SBP properties and keep Yee's grids unchanged, the projection operators are carefully designed to extrapolate fields on the boundaries. Then, the SATs are used to weakly enforce the boundary conditions between multiple mesh blocks with different mesh sizes. Therefore, its long-term stability is theoretically guaranteed. An iris filter is simulated to validate the effectiveness of the proposed method. Results show that the proposed SBP-SAT FDTD subgridding method is stable, accurate and efficient.
In order to solve the problem that the accuracy assessment of a single data source is subject to multiple constraints, data fusion reliability evaluation model based on transfer learning was established. Take the high...
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