To get the most use out of scarce spectrum, technologies have emerged that permit single systems to accommodate both radar and communications functions. Dual-function radar communication (DFRC) systems, where the two systems use the same platform and share the same hardware and spectral resources, form a specific class of radio-frequency (RF) technology. These systems support applications where communication data, whether as target and waveform parameter information or as information independent of the radar operation, are efficiently transmitted using the same radar aperture and frequency bandwidth. This is achieved by embedding communication signals into radar pulses. In this article, we review the principles of DFRC systems and describe the progress made to date in devising different forms of signal embedding. Various approaches to DFRC system design, including downlink and uplink signaling schemes, are discussed along with their respective benefits and limitations. We present tangible applications of DFRC systems and delineate their design requirements and challenges. Future trends and open research problems are also highlighted.

Deep learning (DL) has shown tremendous promise in radar applications that involve target classification and imaging. In the field of indoor monitoring, researchers have shown an interest in DL for classifying daily human activities, detecting falls, and monitoring gait abnormalities. Driving this interest are emerging applications related to smart and secure homes, assisted living, and medical diagnosis. The success of DL in providing an accurate real-time accounting of observed humanmotion articulations fundamentally depends on the neural network structure, input data representation, and proper training. This article puts DL in the context of data-driven approaches for motion classification and compares its performance with other approaches employing handcrafted features. We discuss recent proposed enhancements of DL classification performance and report on important challenges and possible future research to realize its full potential.

Spectrum congestion and competition over frequency bandwidth could be alleviated by deploying dualfunction radar-communications systems, where the radar platform presents itself as a system of opportunity to secondary communication functions. In this paper, we propose a new technique for communication information embedding into the emission of multiple-input multiple-output (MIMO) radar using sparse antenna array configurations. The phases induced by antenna displacements in a sensor array are unique, which makes array configuration feasible for symbol embedding. We also exploit the fact that in a MIMO radar system, the association of independent waveforms with the transmit antennas can change over different pulse repetition periods without impacting the radar functionality. We show that by reconfiguring sparse transmit array through antenna selection and reordering waveform-antenna paring, a data rate of megabits per second can be achieved for a moderate number of transmit antennas. To counteract practical implementation issues, we propose a regularized antenna selection based signaling scheme. The possible data rate is analyzed and the symbol/bit error rates are derived. Simulation examples are provided for performance evaluations and to demonstrate the effectiveness of proposed DFRC techniques.

In this paper, we demonstrate the applicability of radar for gait classification with application to home security, medical diagnosis, rehabilitation, and assisted living. Aiming at identifying changes in gait patterns based on radar micro-Doppler signatures, this paper is concerned with solving the intra motion category classification problem of gait recognition. New gait classification approaches utilizing physical features, subspace features, and sum-of-harmonics modeling are presented and their performances are evaluated using experimental K-band radar data of four test subjects. Five different gait classes are considered for each person, including normal, pathological, and assisted walks. The proposed approaches are shown to outperform existing methods for radar-based gait recognition, which utilize physical features from the cadence-velocity data representation domain as in this paper. The analyzed gait classes are correctly identified with an average accuracy of 93.8%, where a classification rate of 98.5% is achieved for a single gait class. When applied to new data of another individual, a classification accuracy on the order of 80% can be expected. Radar micro-Doppler signatures and their Fourier transforms are well suited to capture changes in gait. Five different walking styles are recognized with high accuracy. Radar-based sensing of gait is an emerging technology with multi-faceted applications in security and health care industries. We show that radar, as a contact-less sensing technology, can supplement existing gait diagnostic tools with respect to long-term monitoring and reproducibility of the examinations.

This paper utilizes sparse array motion to increase the numbers of achievable both degrees of freedom (DOFs) and consecutive lags in direction-of-arrival (DOA) estimation problems. We use commonly employed environment-independent sparse array configurations. The design of these arrays is not dependent on the sources in the field of view, but rather aims at achieving desirable difference co-arrays. They include structured coprime and nested arrays, minimum redundancy array (MRA), minimum hole array (MHA), and sparse uniform linear array (SULA). Array motion can fill the holes in the spatial autocorrelation lags associated with a fixed platform and, therefore, increases the number of sources detectable by the same number of array sensors. Quasi-stationarity of the environment is assumed where the source locations and waveforms are considered invariant over array motion of half wavelength. Closed-form expressions of the number of DOFs and consecutive spatial correlation lags for coprime and nested arrays as well as SULA, due to array translation motion, are derived. The number of DOFs and consecutive lags for the specific cases of MRA an 5 avaluated. We show the respective DOA estimation performance based on sparse reconstruction techniques.

In this paper, we analyze the effect of non-uniform array configurations on adaptive beamforming for enhanced signal-to-interference-plus-noise ratio (SINR). The array is configured using a given number of antennas or through a selection of subset of antennas from a larger available set, leading to a sparse array in both cases. The bounds on the highest achievable SINR for a given number of antennas are formulated and used to offer new insights into open-loop adaptive beamforming. The upper and lower bounds underscore the role of array configurations in optimizing performance for interference-free and interference-active environments, respectively. This paper considers the general case of multiple sources and interferences in the field of view. We formulate three angles, namely, eigenspace angle, conventional angle, and minimum canonical angle for characterizing spatial separation between the source and interference subspaces, and as such, represents performance loss incurred by interference nulling. Three sparse array design methods, incorporating these angles, are proposed. Simulation examples confirm the role of subspace angles in optimum beamforming and validate the utility of antenna selection algorithms for sparse array design.