A planar-array multiple-input-multiple-output (MIMO) radar system possess the ability to gain a three-dimensional (3-D) image in single snapshot due to the wide-band signal and two-dimensional (2-D) virtual aperture. And the conventional inverse synthetic aperture radar (ISAR) obtains the cross-range resolution thanks to the relative rotational movement during the observation. Naturally, the planar-array MIMO radar 3-D images in multiple snapshots also include slow-time domain Doppler information. In order to take advantage of the Doppler shift along the slow-time domain for a better 3-D imaging result, we investigate the method of MIMO radar 3-D imaging via jointly utilizing the time-space observation. By coherent processing along the velocity direction, inverse aperture caused by target movement is incorporated into the 3-D image focusing and therefore the resolution can be increased. Simulation results validate the effectiveness of the proposed method. Comparing to ISAR, the longtime observation as well as the complicated motion compensation in the proposed 3-D imaging method is not necessary. Besides, comparing to the 3-D image in single snapshot, the proposed method can improve the resolution along the target trajectory efficiently.
The efficient scene management of virtual environment is an important research content of computer real-time visualization, which has a decisive influence on the efficiency of drawing. However, Traditional scene management methods do not suitable for complex virtual battlefield environments, this paper combines the advantages of traditional scene graph technology and spatial data structure method, using the idea of management and rendering separation, a loose object-oriented scene graph structure is established to manage the entity model data in the scene, and the performance-based quad-tree structure is created for traversing and rendering. In addition, the collaborative update relationship between the above two structural trees is designed to achieve efficient scene management. Compared with the previous scene management method, this method is more efficient and meets the needs of real-time visualization.
In order to solve the problem of in-shore ship extraction from remote sensing image, a novel method for in-shore ship extraction from high resolution (HR) optical remote sensing image is proposed via salience structure feature and GIS information. Firstly, the berth ROI is located in the image with the aid of the prior GIS auxiliary information. Secondly, the salient corner features at ship bow are extracted from the berth ROI precisely. Finally, a recursive algorithm concerning the symmetric geometry of the ship target is conducted to discriminate the multi docked in-shore targets into mono in-shore ships. The results of the experiments show that the method proposed in this paper can detect the majority of large and medium scale in-shore ships from the optical remote sensing image, including both the mono and the multi adjacent docked in-shore ship cases.
Clustering is an effective mean for of marine environment data analysis. This paper proposes a clustering algorithm
based on the “Velocity-Direction” histogram. First of all, the “Velocity-Direction” histogram is constructed based on the
characteristics of marine environment vector field data. Secondly, the exact surface of histogram is reconstructed by the
Gaussian kernel function to eliminate the contaminated data points in “Velocity-Direction” histogram. Finally, the FCM
algorithm is introduced and modified for the “Velocity-Direction” histogram clustering. The initial number and
clustering centers for the FCM algorithm are set as the local extremum in the constructed histogram surfaces. The
experiment results based on the simulation and the NOAA marine environment vector field data verifies the
effectiveness of the proposed algorithm.
Filter window selection is one of the key issues in SAR image despeckling. This paper proposes an adaptive windowing
method for robust estimation of the local statistics for despeckling filters, which is based on the combination of
confidence interval and morphological reconstruction. A preliminary homogeneous window of each pixel is firstly
shaped from an initial window according to the confidence interval inferred by a given confidence probability. The
confidence probability is chosen adaptively according to the homogeneity facts of the initial window. Subsequently, this
preliminary window is refined by morphological reconstruction under the region adjacency constraints. As a result, a
homogeneous window for filtering with arbitrary shape is obtained, which is continuous in both radiometric and spatial
domain. The experimental results show that the proposed adaptive windowing method performs better in the term of the
window accuracy and gets better balance between speckle reduction and structure preservation with two other commonly
used windowing method.