Vital sign monitoring systems are essential in the care of hospitalized neonates. Due to the immaturity of their organs and immune system, premature infants require continuous monitoring of their vital parameters and sensors need to be directly attached to their fragile skin. Besides mobility restrictions and stress, these sensors often cause skin irritation and may lead to pressure necrosis. In this work, we show that a contactless radar-based approach is viable for breathing monitoring in the Neonatal intensive care unit (NICU). For the first time, different scenarios common to the NICU daily routine are investigated, and the challenges of monitoring in a real clinical setup are addressed through different contributions in the signal processing framework. Rather than just discarding measurements under strong interference, we present a novel random body movement mitigation technique based on the time-frequency decomposition of the recovered signal. In addition, we propose a simple and accurate frequency estimator which explores the harmonic structure of the breathing signal. As a result, the proposed radar-based solution is able to provide reliable breathing frequency estimation, which is close to the reference cabled device values most of the time. Our findings shed light on the strengths and limitations of this technology and lay the foundation for future studies toward a completely contactless solution for vital signs monitoring.

The respiratory and heart rates are critical physiological parameters, and conventional contact-based monitoring techniques may cause discomfort and epidermal damage, being therefore inadequate for long-term monitoring. Despite recent advances, accurate contactless vital sign monitoring is still challenging in practical scenarios, especially in relation to heart rate estimation. In this work, we propose a comprehensive framework for vital sign processing in frequency-modulated continuous-wave radar systems and evaluate its performance with real data imitating common working conditions in an office environment. First, to improve the signal-to-noise ratio before estimation, we propose a novel slow-time phase correlation processing, which allows early integration of the vital sign energy at nearby range bins. Subsequently, we present an adaptive nonlinear least squares framework that explores the harmonic structure existing in the recovered displacement signal. An additional Kalman filter stage is designed to select among multiple estimates from different search regions, thus conferring adaptivity and robustness against harmonic interference and noise. This approach largely provides estimates within the predefined error intervals, being capable of tracking the true breathing and heart rate values even during continuous small body movements. The final accuracy and root mean square error values have shown enhanced estimation, outperforming conventional spectral estimation and other recently proposed methods in almost all scenarios.

Noise radar is an emerging technology which employs random signals as their transmit waveforms. Relying on its stochastic properties, with the proper choice of system s parameters, high performance with respect to the suppression of range ambiguity and low range sidelobes can be achieved. Many works have been published in the literature addressing systems that transmit carriers modulated in amplitude by a given stochastic process, generally generated by a hardware noise source. Nevertheless, the characterization of random frequency modulation (more suitable for several applications) is still an on going subject matter. Within this context, in this paper closed form expressions of the narrowband ambiguity function of random frequency modulated signals are presented, and, as a consequence, its Doppler tolerance is better addressed.

Noise Radar technology is the general term used to describe radar systems that employ realizations of a given stochastic process as transmit waveforms. Originally, carriers modulated in amplitude by a Gaussian random signal, derived from a hardware noise source, were taken into consideration, justifying the adopted nomenclature. With the advances made in hardware as well as the rise of the software defined noise radar concept, waveform design emerges as an important research area related to such systems. The possibility of generating signals with varied stochastic properties increased the potential in achieving systems with enhanced performances. The characterization of random phase and frequency modulated waveforms (more suitable for several applications) has then gained considerable notoriety within the radar community as well. Several optimization algorithms have been proposed in order to conveniently shape both the autocorrelation function of the random samples that comprise the transmit signal, as well as their power spectrum density. Nevertheless, little attention has been driven to properly characterize the stochastic properties of those signals through closed form expressions, jeopardizing the effectiveness of the aforementioned algorithms as well as their reproducibility. Within this context, this paper investigates the performance of several random phase and frequency modulated waveforms, varying the stochastic properties of their modulating signals.

Noise radar is the general term used to describe pulse compression radar systems that employ realizations of a given stochastic process as transmit waveforms. With the proper choice of systems parameters, high performance with respect to the suppression of range ambiguity and low range sidelobes can be achieved, indicating their high suitability for several applications. Furthermore, the random nature that underpins such technology evidences their inherent low probability of interception property, specially necessary in military systems. Despite such features, their usage in surveillance related applications, where targets velocity are unknown, is still limited, because they often present a near thumbtack ambiguity function, and, as a consequence, low Doppler tolerance. The latter becomes more critical in long range applications, where the transmission of long duration pulses are often desired to meet the system's power requirements. Within this context, relying on the referred waveforms stochastic properties, a subpulse processing technique is proposed, allowing the detection of fast targets in long range surveillance Noise Radars.