Awireless passive radar system for real-time through-wall movement detection

In this paper, a reconfigurable real-time passive wireless detection system is described. The system is based on software-defined radio (SDR) architecture. The signal processing method and processing flow that enable through-wall target detection are introduced. The high-speed noise and interference mitigation methods implemented in the system for through-wall target detection are also described. A series of experimental results are presented for both large and small human body movements in through-wall scenarios. It is shown that the high-resolution Doppler event history implemented in the system enables the system to recognize and distinguish a range of body movements. The results demonstrate that this real-time SDR-based wireless detection system is a low-cost solution for human movement and recognition, with a range of applications

A New Taxonomy for Symbiotic EM Sensors

It is clear that the EM spectrum is now rapidly reaching saturation, especially for frequencies below 10~GHz. Governments, who influence the regulatory authorities around the world, have resorted to auctioning the use of spectrum, in a sense to gauge the importance of a particular user. Billions of USD are being paid for modest bandwidths. The earth observation, astronomy and similar science driven communities cannot compete financially with such a pressure system, so this is where governments have to step in and assess /regulate the situation. It has been a pleasure to see a situation where the communications and broadcast communities have come together to formulate sharing of an important part of the spectrum (roughly, 50 MHz to 800 MHz) in an IEEE standard, IEEE802.22. This standard (known as the "TV White Space Network" (built on lower level standards) shows a way that fixed and mobile users can collaborate in geographically widespread regions, using cognitive radio and geographic databases of users. This White Space (WS) standard is well described in the literature and is not the major topic of this short paper. We wish to extend the idea of the WS concept to include the idea of EM sensors (such as Radar) adopting this approach to spectrum sharing, providing a quantum leap in access to spectrum. We postulate that networks of sensors, using the tools developed by the WS community, can replace and enhance our present set of EM sensors. We first define what Networks of Sensors entail (with some history), and then go on to define, based on a Taxonomy of Symbiosis defined by de Bary\cite{symb}, how these sensors and other users (especially communications) can co-exist. This new taxonomy is important for understanding, and should replace somewhat outdated terminologies from the radar world.Comment: 4 pages, 1 Figur

Passive Radar for Opportunistic Monitoring in e-Health Applications

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

Respiration and Activity Detection based on Passive Radio Sensing in Home Environments

The pervasive deployment of connected devices in modern society has significantly changed the nature of the wireless landscape, especially in the license free industrial, scientific and medical (ISM) bands. This paper introduces a deep learning enabled passive radio sensing method that can monitor human respiration and daily activities through leveraging unplanned and ever-present wireless bursts in the ISM frequency band, and can be employed as an additional data input within healthcare informatics. Wireless connected biomedical sensors (Medical Things) rely on coding and modulating of the sensor data onto wireless (radio) bursts which comply with specific physical layer standards like 802.11, 802.15.1 or 802.15.4. The increasing use of these unplanned connected sensors has led to a pell-mell of radio bursts which limit the capacity and robustness of communication channels to deliver data, whilst also increasing inter-system interference. This paper presents a novel methodology to disentangle the chaotic bursts in congested radio environments in order to provide healthcare informatics. The radio bursts are treated as pseudo noise waveforms which eliminate the requirement to extract embedded information through signal demodulation or decoding. Instead, we leverage the phase and frequency components of these radio bursts in conjunction with cross ambiguity function (CAF) processing and a Deep Transfer Network (DTN). We use 2.4GHz 802.11 (WiFi) signals to demonstrate experimentally the capability of this technique for human respiration detection (including through-the-wall), and classifying everyday but complex human motions such as standing, sitting and falling

SoK: Inference Attacks and Defenses in Human-Centered Wireless Sensing

Human-centered wireless sensing aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around her. The wireless sensing community has demonstrated the superiority of such techniques in many applications such as smart homes, human-computer interactions, and smart cities. Like many other technologies, wireless sensing is also a double-edged sword. While the sensed information about a human can be used for many good purposes such as enhancing life quality, an adversary can also abuse it to steal private information about the human (e.g., location, living habits, and behavioral biometric characteristics). However, the literature lacks a systematic understanding of the privacy vulnerabilities of wireless sensing and the defenses against them. In this work, we aim to bridge this gap. First, we propose a framework to systematize wireless sensing-based inference attacks. Our framework consists of three key steps: deploying a sniffing device, sniffing wireless signals, and inferring private information. Our framework can be used to guide the design of new inference attacks since different attacks can instantiate these three steps differently. Second, we propose a defense-in-depth framework to systematize defenses against such inference attacks. The prevention component of our framework aims to prevent inference attacks via obfuscating the wireless signals around a human, while the detection component aims to detect and respond to attacks. Third, based on our attack and defense frameworks, we identify gaps in the existing literature and discuss future research directions

Doppler based detection of multiple targets in passive WiFi radar using undetermined blind source separation

Passive approaches for detecting and localizing people in wireless environments have attracted significant attention because of its diverse application in healthcare, security and robotics in recent years. However, within indoor environments multiple people moving in close proximity to each other often impedes the utility of such approaches. In this paper we present a new method for identifying multiple human targets in Wi-Fi passive radar systems using only a single receive channel to detect Doppler returns. The technique is based on tree-structure sparse underdetermined blind source separation and utilizes proximal alternating methods in a convex optimization field. Firstly, we show proof-of-principle simulation results for two targets moving within a typical indoor scenario and compare the results with those from the well-known independent component analysis (ICA). Secondly, we validate the simulation outputs using real-world experimental data. The results demonstrate the effectiveness of the proposed technique for device-free detection of multiple targets in the indoor wireless landscape

Log-Likelihood Clustering-Enabled Passive RF Sensing for Residential Activity Recognition

Physical activity recognition is an important research area in pervasive computing because of its importance for e-healthcare, security, and human-machine interaction. Among various approaches, passive radio frequency sensing is a well-tried radar principle that has potential to provide the unique solution for non-invasive activity detection and recognition. However, this technology is still far from mature. This paper presents a novel hidden Markov model-based log-likelihood matrix for characterizing the Doppler shifts to break the fixed sliding window limitation in traditional feature extraction approaches. We prove the effectiveness of the proposed feature extraction method by K-means K-medoids clustering algorithms with experimental Doppler data gathered from a passive radar system. The results show that the time adaptive log-likelihood matrix outperforms the traditional singular value decomposition, principal component analysis, and physical feature-based approaches, and reaches 80% in recognizing rate

An inclusive survey of contactless wireless sensing: a technology used for remotely monitoring vital signs has the potential to combating COVID-19

With the Coronavirus pandemic showing no signs of abating, companies and governments around the world are spending millions of dollars to develop contactless sensor technologies that minimize the need for physical interactions between the patient and healthcare providers. As a result, healthcare research studies are rapidly progressing towards discovering innovative contactless technologies, especially for infants and elderly people who are suffering from chronic diseases that require continuous, real-time control, and monitoring. The fusion between sensing technology and wireless communication has emerged as a strong research candidate choice because wearing sensor devices is not desirable by patients as they cause anxiety and discomfort. Furthermore, physical contact exacerbates the spread of contagious diseases which may lead to catastrophic consequences. For this reason, research has gone towards sensor-less or contactless technology, through sending wireless signals, then analyzing and processing the reflected signals using special techniques such as frequency modulated continuous wave (FMCW) or channel state information (CSI). Therefore, it becomes easy to monitor and measure the subject’s vital signs remotely without physical contact or asking them to wear sensor devices. In this paper, we overview and explore state-of-the-art research in the field of contactless sensor technology in medicine, where we explain, summarize, and classify a plethora of contactless sensor technologies and techniques with the highest impact on contactless healthcare. Moreover, we overview the enabling hardware technologies as well as discuss the main challenges faced by these systems.This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under grand 119E39