Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

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Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Sensor-based and vision-based human activity recognition: A comprehensive survey

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

With the advancement of wireless technologies and sensing methodologies, many studies have shown the success of re-using wireless signals (e.g., WiFi) to sense human activities and thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, gesture recognition to vital signs monitoring and user identification involving even finer-grained motion sensing. These applications arguably can brace various domains for smart home and office environments, including safety protection, well-being monitoring/management, smart healthcare and smart-appliance interaction. The movements of the human body impact the wireless signal propagation (e.g., reflection, diffraction and scattering), which provide great opportunities to capture human motions by analyzing the received wireless signals. Researchers take the advantage of the existing wireless links among mobile/smart devices (e.g., laptops, smartphones, smart thermostats, smart refrigerators and virtual assistance systems) by either extracting the ready-to-use signal measurements or adopting frequency modulated signals to detect the frequency shift. Due to the low-cost and non-intrusive sensing nature, wireless-based human activity sensing has drawn considerable attention and become a prominent research field over the past decade. In this paper, we survey the existing wireless sensing systems in terms of their basic principles, techniques and system structures. Particularly, we describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization. The future research directions and limitations of using wireless signals for human activity sensing are also discussed.

Wireless Sensing for Human Activity: A Survey

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Statistical Signal Processing Detection Estimation And Time Series Analysis

Detection and interpretation of human activities have emerged as a challenging healthcare problem in areas such as assisted living and remote monitoring. Besides traditional approaches that rely on wearable devices and camera systems, WiFi-based technologies are evolving as a promising solution for indoor monitoring and activity recognition. This is, in part, due to the pervasive nature of WiFi in residential settings such as homes and care facilities, and the unobtrusive nature of WiFi-based sensing. Advanced signal processing techniques can accurately extract WiFi channel status information (CSI) using commercial off-the-shelf devices or bespoke hardware. This includes phase variations, frequency shifts, and signal levels. In this article, we describe the healthcare application of Doppler shifts in the WiFi CSI caused by human activities that take place in the signal coverage area. The technique is shown to recognize different types of human activities and behavior and be very suitable for applications in healthcare. Three experimental case studies are presented to illustrate the capabilities of WiFi CSI Doppler sensing in assisted living and residential care environments. We also discuss the potential opportunities and practical challenges for realworld scenarios.

/pdf/exploiting-wifi-channel-state-information-for-residential-2e8as6qu2z.pdf

Exploiting WiFi Channel State Information for Residential Healthcare Informatics

This paper proposes a passive Doppler radar as a non-contact sensing method to capture human body movements, recognize respiration, and physical activities in e-Health applications. The system uses existing in-home wireless signal as the source to interpret human activity. This paper shows that passive radar is a novel solution for multiple healthcare applications which complements traditional smart home sensor systems. An innovative two-stage signal processing framework is outlined to enable the multi-purpose monitoring function. The first stage is to obtain premier Doppler information by using the high speed passive radar signal processing. The second stage is the functional signal processing including micro Doppler extraction for breathing detection and support vector machine classifier for physical activity recognition. The experimental results show that the proposed system provides adequate performance for both purposes, and prove that non-contact passive Doppler radar is a complementary technology to meet the challenges of future healthcare applications.

Passive Radar for Opportunistic Monitoring in E-Health Applications

Detection and interpretation of human activities have emerged as a challenging healthcare problem in areas such as assisted living and remote monitoring. Besides traditional approaches that rely on wearable devices and camera systems, WiFi based technologies are evolving as a promising solution for indoor monitoring and activity recognition. This is, in part, due to the pervasive nature of WiFi in residential settings such as homes and care facilities, and unobtrusive nature of WiFi based sensing. Advanced signal processing techniques can accurately extract WiFi channel status information (CSI) using commercial off-the-shelf (COTS) devices or bespoke hardware. This includes phase variations, frequency shifts and signal levels. In this paper, we describe the healthcare application of Doppler shifts in the WiFi CSI, caused by human activities which take place in the signal coverage area. The technique is shown to recognize different types of human activities and behaviour and be very suitable for applications in healthcare. Three experimental case studies are presented to illustrate the capabilities of WiFi CSI Doppler sensing in assisted living and residential care environments. We also discuss the potential opportunities and practical challenges for real-world scenarios.

Non-contact breathing monitoring systems are very attractive for a range of e-Healthcare applications. This paper proposes a passive radar based system for measuring human breathing rate. A novel signal processing method is introduced to extract breathing rate based on micro Doppler derived from cross ambiguity function (CAF). The passive radar system is built within a software defined radio (SDR) platform. The proposed system uses opportunistically energy harvesting transmitter as an illumination signal. Passive detection is compared and verified using the ground truth from clinical chest belt respiration detector. Two experiments have been conducted to show the feasibility of passive detection system in the line of sight and also through-wall conditions. We conclude that a low frequency narrow band signal with non-contact passive detection can offer a realistic alternative to UWB based radars for future e-Healthcare passive sensing applications.

/pdf/non-contact-breathing-detection-using-passive-radar-5dhdg2eu2u.pdf

Non-contact breathing detection using passive radar

Detection of human presence and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security, and low impact building. However, existing radio frequency identification tags, wearables, and passive infrared approaches require the user to carry dedicated electronic devices that suffer from problems of low detection accuracy and false alarms. This study proposes a novel system for non-invasive human sensing by analysing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to stationary objects, thus there is no need for any scenario-specific calibration which makes it ideal for human sensing. We also introduce the time-frequency domain feature vectors of WiFi Doppler information for the support vector machine classifier towards activity event recognition. The proposed methodology is evaluated on a software defined radio system together with the experiment of five different events. The results indicate that the proposed system is sufficient for indoor context awareness, with 95.3% overall accuracy for event classification and 93.3% accuracy for human presence detection, which outperforms the traditional received signal strength approach where accuracy is 69.3% for event classification and 83.3% for human presence detection.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-wss.2018.5113

Wenda Li

Papers

Exploiting WiFi Channel State Information for Residential Healthcare Informatics

Passive Radar for Opportunistic Monitoring in E-Health Applications

Exploiting WiFi Channel State Information for Residential Healthcare Informatics

Non-contact breathing detection using passive radar

WiFi-based passive sensing system for human presence and activity event classification