Breathing rate monitoring using continuous wave (CW) radar has gained much attention due to its contact-less nature and privacy-friendly characteristic. In this work, using a single-channel CW radar, a breathing rate estimation method is proposed that deals with system nonlinearity of a single-channel CW radar and realizes a reliable breathing rate estimate by including confidence intervals. To this end, time-varying dominant Doppler frequency of radar signal, in the range of breathing rate, is extracted in time-frequency domain. It is shown through simulation and mathematical modeling that the average of the dominant Doppler frequencies over time provides an estimation of breathing rate. However, this frequency is affected by noise components and random body movements over time. To address this issue, the sum of these unwanted components is extracted in time-frequency domain, and from their surrogate versions, bootstrap resamples of the measured signal are obtained. Accordingly, a 95% confidence interval is calculated for breathing rate estimation using the bootstrap approach. The proposed method is validated in three different postures including lying down, sitting, and standing, with or without random body movements. The results show that using the proposed algorithm, estimation of breathing rate is feasible using single-channel CW radar. It is also shown that even in presence of random body movements, average of absolute error of estimation for all three postures is 1.88 breath per minute, which represents 66% improvement as compared to the Fourier transform-based approach.

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Typical radar detectors exploit only a small proportion of the valuable information contained in radar reflections, i.e. magnitude and Doppler. A neural network-based approach for augmenting traditional radar detector structures using machine learning (ML) is proposed in this paper. Specifically, the network is designed to augment target detection in the field of maritime wide area surveillance for noncoherent data. A combination network consisting of a convolutional neural network (CNN) to extract spatial features and a long short-term memory (LSTM) for extracting temporal patterns in the spatial features is proposed. The network augments the detector structure by blanking out regions of the frame which are classified as not containing a target, thus reducing false alarms. The network is tested on data containing four marine targets collected by a ground-based radar. The data set was chosen because it contains strong sea clutter returns. When ML is used, the receiver operating characteristic (ROC) curves are shifted to lower probability of false alarm (PFA). A Kalman filter tracker was applied to the ML-augmented and baseline detections, and it was shown that ML-augmented detections produced similar tracks at lower PFA. The feature discovering capability of the network is analyzed through a series of tests, and the argument is made that the CNN-LSTM network presented in this work demonstrates the ability to improve the detection performance by exploiting spatial and temporal information in the data.

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Radar has been proposed for monitoring the health of elderly patients in long term care because it is safe, non-contact and preserves the privacy of patients. Random body movements (RBM) obscure radar return signals making it difficult if not impossible to accurately estimate vitals. Activity classification is presented in this thesis as a preprocessing step for dealing with RBMs. Posture classification is presented in this thesis for assistance in preventing falls. Two popular radar architectures-continuous wave (CW) Doppler and ultra-wideband (UWB) are investigated in this thesis. Activity classification is performed with 92% average accuracy with CW and 86% with UWB.Posture Classification is performed with 64% average accuracy with CW and 85% with UWB. An occupancy detection algorithm was also developed for UWB and achieved 88% average accuracy. The contribution of this thesis is a proposed hierarchical processing approach for both radar types capable of dealing with moving subjects.iii