## 1 Introduction
A specific application of an FMCW system was in the measurement of a human breathing through wall [21; 22] achieved using an S-Band Self Injection Locked (SIL) architecture. The wall used for this case was, however, only a plywood barrier in comparison to a standard brick wall with air gaps. The micro-Doppler effect is generated by the rotation or vibration of components of a target and presents itself as modulations on top of the main Doppler shift generated by the bulk translational motion of a target [23]. In the case of human micro-Doppler, the motion of the arms, legs and torso all contribute to a unique signature modulated on the main Doppler velocity generated by an individual moving forward. The study of human micro-Doppler is an active area of interest in radar research, with the main objective often being the extraction of additional information within the micro-Doppler signature for the radars' advantage. Well defined models for human motion have been developed by [PERSON] and applied to real data [23; 24; 25; 26], these can accurately produce expected micro-Doppler contributions from different types of motions. In recent work it has been shown that optical sensors can also be used to develop human micro-Doppler modelling capabilities further through the use of a simple low cost optical sensor [27]. The additional information provided by the human micro-Doppler can be used to classify the types of motions individuals are making, from walking to boxing, or to classify different targets, for example animals, humans or vehicles [28], or for Automatic Target Recognition (ATR) [29]. [PERSON] and [PERSON] [28], showed seven different human motions could be successfully classified by applying a Support Vector Machine technique, although this was completed in a non-TTW scenario. If the information is maintained within the signal after propagating through a wall, then the same processing should, in theory, be possible, although with potential reductions in performance due to lower SNR and the additional multipath components. The analysis of human micro-Doppler has also been performed using multistatic radar sensors, where a comparison was made between a simple model for human motion and the real multistatic micro-Doppler data [30]. In a multistatic scenario, classification methodologies have been applied in order to differentiate individuals walking free handed or while carrying a rifle-like object [31; 32]. This analysis used either empirical manually extracted features from the micro-Doppler signatures or an automated Singular Value Decomposition (SVD) feature extraction. The techniques applied within this work follow on from the SVD processing methodologies applied to the multistatic data [31], but are utilized in a TTW scenario, using a different radar sensor, with a different sensor architecture and alternative classification techniques. In the non-TTW configuration, classification results achieved by SVD processing were shown to have success rates of up to 95% with 25% training samples. SVD processing has previously been applied to through wall radar analysis but the focus has not been on using this information as part of classification processing. It was shown in [33] that SVD can be used as part of the detection process when using a TTW radar, although this work focused solely on the detection of a target using the SVD information, not the classification of the actions of a human target. SVD processing was also used for TTW radar in [34], but this was to mitigate clutter, not for detection or classification. The rest of this paper is organized as follows. Section 2 presents the radar system and the experimental setup used for the data collection process. Section 3 explains the theory behind the SVD processing and the classifiers that have been applied to the data. Section 4 shows the results of these signal processing algorithms and compares the classification accuracy from the different cases measured.