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

Thank you for reading statistical signal processing detection estimation and time series analysis. Maybe you have knowledge that, people have look hundreds times for their chosen novels like this statistical signal processing detection estimation and time series analysis, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop.

Statistical Signal Processing Detection Estimation And Time Series Analysis

The next generation of mobile communication aims to extend the capabilities of traditional communication by reshaping the environment with wireless signals. The channel state information can describe the propagation characteristics in wireless communications, which is beneficial in developing wireless communication networks towards intelligent communication and wireless sensing networks. Raspberry PI with Nexmon firmware patched can extract the channel state information from WiFi signals and realize human activity recognition. However, the phase values on some carriers are susceptible to noise, resulting in phase errors after singular value decomposition. To solve this problem, a method is proposed in this paper to find the optimal phase value by dynamic time warping algorithm utilizing the property of orthogonality between amplitude and phase. In contrast to the conventional recognition strategies, the proposed optimal phase extraction method with commercial WiFi signals can further improve the accuracy of the recognition strategy under different complicated scenarios.

Human Activity Recognition With Commercial WiFi Signals

Software Defined Radar (SDRadar) is a unique radar system, where most of its processing, like filtering, correlation, signal generation etc. is performed by software. This means SDRadar can be flexibly deployed for different purposes and with a relative short development process. In this paper, we present a generic SDRadar system that can operate in different setups for near-field monitoring applications. Practical solutions for traditional limitations in SDRadar and high sampling rates are introduced, and its performance is demonstrated using a commercial universal software radio peripheral (USRP) device with four synchronized receiving channels and a maximum sampling rate of 100MHz. Additionally, a GPU accelerator has been implemented to deal with the high sampling rate. Three different setups have been tested to demonstrate the feasibility of the propose SDRadar system with distributed nodes, vertically positioned nodes and a miniature scenario. Recorded Doppler signatures have shown the proposed SDRadar can effectively capture the body and hand gestures. Such results can be used in a range of applications such as eHealth, human-machine interaction and indoor tracking.

A High-Speed Multi-Purpose Software Defined Radar for Near-Field Applications

This work introduces an attention mechanism that can be integrated into any standard convolution neural network to improve model sensitivity and prediction accuracy with minimal computational overhead. The attention mechanism is introduced in a lightweight network – Alexnet and its classification performance for human micro-Doppler signatures is evaluated. The Alexnet model trained with an attention module can implicitly highlight the salient regions in the radar signatures while suppressing the irrelevant background regions and consistently improving network predictions. Network visualizations are provided through class activation mapping, providing better insights into how the predictions are made. The visualizations demonstrate how the attention mechanism focusses on the region of interest in the radar signatures. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/rsn2.12369

Wenda Li

Papers

Human Activity Recognition With Commercial WiFi Signals

A High-Speed Multi-Purpose Software Defined Radar for Near-Field Applications

Attention‐enhanced Alexnet for improved radar micro‐Doppler signature classification