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This paper investigates an implementation of an array of distributed neural networks, operating together to classify between unarmed and potentially armed personnel in areas under surveillance using ground based radar. Experimental data collected by the University College London (UCL) multistatic radar system NetRAD is analysed. Neural networks are applied to the extracted micro-Doppler data in order...
This paper focuses on the classification of human gaits based on micro-Doppler signatures. The micro-Doppler signatures can represent detailed information about the human gaits, which helps in judging the threat of a personnel target. The proposed method consists of three major steps. Firstly, the micro-Doppler signatures are obtained by performing time-frequency analysis on the radar data. Then two...
This paper analyses the experimental results from recent monostatic and bistatic radar measurements of multiple birds as well as a quadcopter micro-drone. The radar system deployed for these measurements was the UCL developed NetRAD system. The aim of this work is to evaluate the key differences observed by a radar system between different birds and a micro-drone. Measurements are presented from simultaneous...
Human micro-Doppler radar signatures have been investigated to classify different types of activities and to identify potential armed personnel in the context of security and surveillance applications. In this paper the use of multistatic micro-Doppler signatures to distinguish between unarmed and armed personnel moving is described. The effect of polarimetry on the classification accuracy is evaluated...
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