## 1 Introduction  
There is a significant body of academic research which has attempted to apply radar detection and imaging techniques to scenarios that consist of an obscuring wall barrier, which is summarized effectively within [1; 2; 3]. Information obtained about the structural layout of a room, if a person is present or how many individuals are present is vital for police, military or civilian search and rescue teams [4]. In the domain of defence and security, a key challenge that exists is the ability not only to sense the presence of individuals within a room but to also classify their actions. This can be accomplished using a multitude of different techniques and types of sensors, each with their own advantages and disadvantages. Invasive positioning of optical sensors allows for observations to be made of a room, but requires physically penetrating the walls or the opening of the closed room which may be impractical in a short time frame or cause disruption that would alert the individuals within the room. The advantage of TTW radar is that it can sense targets within a room at a standoff distance without the need of physically placing the sensor within the room. The challenge of TTW radar imagery has been researched using a combination of both innovative signal processing and hardware techniques. Hardware innovations in this area include active MIMOarray systems [5] providing cross range information, ultra-wide band radars [6; 7] for high range resolution, Synthetic Aperture Radar (SAR) imaging techniques [8] and passive WiFi radar [9]. The practical implications of using a MIMO radar for TTW imaging is discussed in [7]. This system was capable of real-time processing and high range and cross range resolution, although it was bulky in its design compared to other man-portable solutions. These hardware innovations have been successful in gathering information within through wall scenarios, but often come with increased complexity and cost. Previously developed signal processing techniques that have also been applied to TTW imaging include the MUSIC algorithm [10], compressed sensing [11], image alignment procedures [12] and spatial filtering [13]. The focus of these techniques is often on the maximization the signal-to-noise ratio (SNR) of the desired target or removal of the significant primary wall reflection. TTW human micro-Doppler signatures have been both simulated and analysed previously within [14; 15; 16; 17]. In comparison with those publications, this article focuses on the application of classification of movements on real data obtained in a through wall configuration using human micro-Doppler signatures. The most significant effect of a TTW scenario on the detected signatures is the attenuation of the signals obtained due to the significant loss of signal when propagating through the wall. Other important effects to take into account include multipath and shadowing effects from other clutter items within the scene. Multipath issues are one of the most challenging problems and can result in missed detections or incorrect estimates of the number of targets. All of these problems contribute to a very challenging situation which is yet to be effectively solved. The hardware system used for this research was a Frequency Modulated Continuous Wave (FMCW) radar which benefits from no minimum blind range, relative low-cost, good resistance to interference, and simple architecture suited to a compact design [18]. This type of radar system has been researched for many decades, with numerous variations on the engineering techniques applied to maximize the information obtained and reduce the effects of clutter and the reflections from the wall. Previous FMCW radar systems that have been used in TTW experiments include work published by ONERA [19; 20] showing a UWB radar (1-5 GHz) measuring a person walking inside a residential building as well as evaluating the measured attenuations with respect to range and frequency.