3D map generation for biomimetic applications using a network of multi-static radar sensors

Shubha Kadambe

doi:10.1117/12.724244

9 April 2007 3D map generation for biomimetic applications using a network of multi-static radar sensors

Shubha Kadambe

Proceedings Volume 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V; 65760V (2007) https://doi.org/10.1117/12.724244
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Shubha Kadambe "3D map generation for biomimetic applications using a network of multi-static radar sensors", Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65760V (9 April 2007); https://doi.org/10.1117/12.724244

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