Non-contact life detection is a significant component of both civilian and military rescue applications. As a consequence, this interest has resulted in a very active area of research. The primary goal of this research is reliable detection of a human breathing signal. Additional goals of this research are to carry out detection under realistic conditions, to distinguish between two targets, to determine human breathing rate and estimate the posture. Range gating and Singular Value Decomposition (SVD) have been used to remove clutter in order to detect human breathing under realistic conditions. However, the information of the target range or what principal component contains target information may be unknown. DFT and Short Time Fourier Transform (STFT) algorithms have been used to detect the human breathing and discriminate between two targets. However, the algorithms result in many false alarms because they detect breathing when no target exists. The unsatisfactory performance of the DFT-based estimators in human breathing rate estimation is due to the fact that the second harmonic of the breathing signal has higher magnitude than the first harmonic. Human posture estimation has been performed by measuring the distance of the chest displacements from the ground. This requires multiple UWB receivers and a more complex system. In this thesis, monostatic UWB radar is used. Initially, the SVD method was combined with the skewness test to detect targets, discriminate between two targets, and reduce false alarms. Then, a novel human breathing rate estimation algorithm was proposed using zero-crossing method. Subsequently, a novel method was proposed to distinguish between human postures based on the ratios between different human breathing frequency harmonics magnitudes. It was noted that the ratios depend on the abdomen displacements and higher harmonic ratios were observed when the human target was sitting or standing. The theoretical analysis shows that the distribution of the skewness values of the correlator output of the target and the clutter signals in a single range-bin do not overlap. The experimental results on human breathing detection, breathing rate, and human posture estimation show that the proposed methods improve performance in human breathing detection and rate estimation.

Contact-less human detection and monitoring using radar technology has been recently applied in many areas including search-and-rescue for earthquake victims, fall detection, gait analysis and detection of other human activities. Radars can also provide important information about a persons state of health by monitoring the level of activities, heart and breathing rate. Also it can be used to generate warnings if some of the monitored parameters are outside of predefined limits. The major application of this work is for monitoring in-mates and their activities. This thesis deals with characterizing the performance of the radar system used for monitoring a single person in a contained environment. This thesis is experimentally based and during the thesis a large number of experiments were performed in order to monitor subjects in realistic conditions. The thesis explores feasibility of using the radar with a single radio-frequency channel input and two algorithms for breathing and heart rate estimation when the subject is at different relative orientation towards the radar as well as in different postures. Algorithm one is using Fast Fourier Transformation (FFT) and algorithm two is using Empirical Mode Decomposition (EMD) with Minkowski distance. We also detect the zones where the subject is when the subject is moving. Since this exploratory analysis provides initial features for classifications and algorithms for breathing and heart beat estimation, it can represent a foundation for future works on designing systems that track subjects and their breathing in real-time.