In the last years, substantial attention has been paid to the use of radar systems in health monitoring, due to the availability of both low-cost radar devices and computationally efficient algorithms for processing their measurements. In this article, a tutorial overview of radar-based monitoring of vital signs is provided. More specifically, we first focus on the available radar technologies and the signal processing algorithms developed for the estimation of vital signs. Then, we provide some useful guidelines that should be followed in the selection of radar devices for vital sign monitoring and in their use. Finally, we illustrate various specific applications of radar systems to health monitoring and some relevant research trends in this field.

In this manuscript, an innovative method for the detection and the estimation of multiple targets in a radar system employing orthogonal frequency division multiplexing is illustrated. The core of this method is represented by a novel algorithm for detecting multiple superimposed two-dimensional complex tones in the presence of noise and estimating their parameters. This algorithm is based on a maximum likelihood approach and combines a single tone estimator with a serial cancellation procedure. Our numerical results lead to the conclusion that the developed method can achieve a substantially better accuracy-complexity trade-off than various related techniques in the presence of closely spaced targets.

In this manuscript, the problem of detecting multiple targets and jointly estimating their spatial coordinates (namely, the range, the Doppler and the direction of arrival of their electromagnetic echoes) in a colocated multiple-input multiple-output radar system employing orthogonal frequency division multiplexing is investigated. It is well known its optimal solution, namely the joint maximum likelihood estimator of an unknown number of targets, is unfeasible because of its huge computational complexity. Moreover, until now, sub-optimal solutions have not been proposed in the technical literature. In this manuscript a novel approach to the development of reduced complexity solutions is illustrated. It is based on the idea of separating angle estimation from range-Doppler estimation, and of exploiting known algorithms for solving these two sub-problems. A detailed analysis of the accuracy and complexity of various detection and estimation methods based on this approach is provided. Our numerical results evidence that one of these methods is able to approach optimal performance in the maximum likelihood sense with a limited computational effort in different scenarios.

In this manuscript we investigate the implications of adopting a double cyclic prefix in the orthogonal time-frequency space modulation. Our study first focuses on the analysis of the modulated signal and on the development of a useful model for the received signal in the presence of a doubly selective fading channel. On the one hand, our mathematical results allow us to accurately assess the impact of pulse shaping on the structure of the transmitted waveform and on its power spectral density, and to develop some simple rules for allocating multiple pilot symbols within each orthogonal time-frequency space symbol. On the other hand, they are exploited to develop a novel algorithm for pilot-aided channel estimation, whose output provides a detailed representation of the communication channel; for this reason, it can be used for sensing at the transmit side in integrated sensing and communication applications, or for channel equalization at the receive side in digital communications. Our numerical results evidence that our channel estimation & equalization algorithm outperforms the other related techniques available in the technical literature at the price of a limited increase in computational complexity.

In this manuscript, a general method for deriving filtering algorithms that involve a network of interconnected Bayesian filters is proposed. This method is based on the idea that the processing accomplished inside each of the Bayesian filters and the interactions between them can be represented as message passing algorithms over a proper graphical model. The usefulness of our method is exemplified by developing new filtering techniques, based on the interconnection of a particle filter and an extended Kalman filter, for conditionally linear Gaussian systems. Numerical results for two specific dynamic systems evidence that the devised algorithms can achieve a better complexity-accuracy tradeoff than marginalized particle filtering and multiple particle filtering.

In this manuscript, we analyze the most relevant classes of deterministic signal processing methods currently available for the detection and the estimation of multiple targets in a joint communication and sensing system employing orthogonal frequency division multiplexing. Our objective is offering a fair comparison of the available technical options in terms of required computational complexity and accuracy in both range and Doppler estimation. Our numerical results, obtained in various scenarios, evidence that distinct algorithms can achieve a substantially different accuracy-complexity trade-off.