Recent trends in platforms for the consumer market increased the need for low-power and reliable classification engines. Spiking Neural Network (SNN) is a new technology that promises to deliver 4 orders of magnitude more performance per watt than competing solutions. Moreover, the adoption of RADAR for gesture detection provides higher reliability compared to image sensors. However, no accepted topology for a temporal SNN classifier focusing on RADAR data exists. In addition, previous research did not account for several design limitations necessary to export the design in analog hardware. In this work, we explore the possible SNN topologies and propose a Liquid State Machine (LSM) with fully-supervised readout, suitable to be exported to a mixed-signal neuron array. A complete parametric model of the architecture and learning rule has been implemented in a simulation environment. Following, the design space was explored in search for the optimal operating region. By analysing the results, we: (i) highlight and explain the effects of several parameters and the trade-off between accuracy and power consumption; (ii) emphasize the need for a good balance between global excitation and inhibition in the LSM; (iii) suggest that the limitations of the proposed design point to the importance of an adequate feature extraction for a stable LSM behaviour and to the unpredictable nature of the SNN backpropagation algorithm, caused by the non differentiability of the spike signals.