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3D Map Generation For Biomimetic Applications Using A Net Work Of Multi-Static Radar Sensors

机译:使用多静态雷达传感器网络为仿生应用生成3D地图

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摘要

In this paper, we describe a novel algorithm based on diffraction tomography for 3D map generation using the received backscattered radar Electro-Magnetic (EM) field from different spatially distributed multi-static radar sensors. Each sensor at a given time will transmit a radar waveform and the other sensors including the one transmitted will receive the waveform that is backscattered from the objects. A data cube of received data will be created at each sensor by changing the location of sensors. This data cube is used in generating the 3D object profiles at each sensor and then the fused 3D map will be outputted which will contain the fused 3D object profiles or structure obtained from each sensor. If there are more than one object in the field of interest there would be inter object backscattering. This would result in receiving mixed signals. This mixed signal might cause problems in the generation of the 3D map/structure. So to reduce the effect of inter object backscattering we use the probabilistic based blind source separation (BSS) technique for convolutive mixture separation. Before applying the mixture separation technique, we estimate the number of sources. For this we have developed a technique. In this paper, all these techniques are described and also results using real radar backscattered data are provided. A description of how this 3D maps can be used for biomimetics is also provided.
机译:在本文中,我们描述了一种基于衍射层析成像的新颖算法,该算法使用从不同空间分布的多静态雷达传感器接收到的反向散射雷达电磁场来生成3D地图。在给定时间,每个传感器将发送雷达波形,而其他传感器(包括已发送的传感器)将接收从对象反向散射的波形。通过更改传感器的位置,将在每个传感器上创建接收到的数据的数据立方体。该数据立方体用于在每个传感器上生成3D对象轮廓,然后将输出融合的3D地图,其中将包含从每个传感器获得的融合3D对象轮廓或结构。如果感兴趣的领域中有多个物体,则物体之间将发生反向散射。这将导致接收到混合信号。此混合信号可能会在3D地图/结构的生成中引起问题。因此,为了减少对象间反向散射的影响,我们使用基于概率的盲源分离(BSS)技术进行卷积混合物分离。在应用混合物分离技术之前,我们先估算来源的数量。为此,我们开发了一种技术。本文描述了所有这些技术,并提供了使用真实雷达后向散射数据的结果。还提供了如何将此3D地图用于仿生的描述。

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