Analysis of radar dismount signatures via non-parametric and parametric methods

We present novel approaches to the analysis of radar dismount signatures that entail the characterization of the time-frequency (TF) structure of the received radar signal associated with the dismount gait by both non-parametric and parametric methods. We first introduce the concept of Gaussian g-Snakes in order to parametrically characterize the TF distribution of radar signals. In particular, we derive simple steepest descent equations that enable the estimation of the (locally) optimal g-Snake parameters for a given TF distribution. Furthermore the g-Snake modeling methodology gives us an objective unsupervised criterion from which to quantify the quality of the motion curve estimates that have been tracked from the TF data. We then formulate the non-parametric motion estimation for TF signals by a coupling of a simple partial tracking methodology in conjunction with boundary condition enforcement with regularity constraints. Finally we propose a coupling of the above non-parametric approach with g-Snake modeling that result in improved overall modeling of the given real and simulated radar TF data.