Radar signal processing for human identification by means of reservoir computing networks

Along with substantial advances in the area of image processing and, consequently, video-based surveillance systems, concerns about preserving the privacy of people have also deepened. Therefore, replacing conventional video cameras in surveillance systems with less-intrusive and yet effective alternatives, such as micro-wave radars, is of high interest. The aim of this work is to explore the application of Reservoir Computing Networks (RCNs) to the problem of identifying a limited number of people in an indoor environment, leveraging gait information captured by micro-wave radar measurements. These measurements are done using a commercial low-power linear frequency-modulated continuous-wave (FMCW) radar. Besides the low quality of the outputs of such a radar sensor, walking spontaneously as opposed to controlled situations adds another level of complexity to the targeted use case. In this context, RCNs are interesting tools, given that they have shown a high effectiveness in capturing temporal information and handling noise, while at the same time being easy to setup and train. Using Micro-Doppler features as inputs, we follow a structured procedure towards optimizing the parameters of our RCN-based approach, showing that RCNs have a great potential in processing the noisy features provided by a low-power radar.