Abstract-Automatic detection of human motion is important for security and surveillance applications. Compared to other sensors, radar sensors present advantages for human motion detection and identification because of their all-weather and day-and-night capabilities, as well as the fact that they detect targets at a long range. This is particularly advantageous in the case of remote and highly cluttered radar scenes. The objective of this paper is to investigate human motion in highly cluttered forest medium to observe the characteristics of the received Doppler signature from the scene. For this purpose we attempt to develop an accurate model accounting for the key contributions to the Doppler signature for the human motion in a forest environment. Analytical techniques are combined with full wave numerical methods such as Method of Moments (MoM) enhanced with Fast Multipole Method (FMM) to achieve a realistic representation of the signature from the scene. Mutual interactions between the forest and the human as well as the attenuation due to the vegetation are accounted for. Due to the large problem size, parallel programming techniques that utilize a Graphics Processing Unit (GPU) based cluster are used.

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