16 research outputs found
Doppler Sensing Using WiFi Round-Trip Channel State Information
This paper presents a wireless sensing system using WiFi round-trip channel state information (RTCSI). It is implemented using the channel state information (CSI) from the Raspberry Pi CM4 onboard WiFi chip and a customized WiFi protocol. Utilizing the CSI phase in WiFi sensing is challenging as hardware imperfections and asynchronization introduce significant phase errors. Similar to WiFi round-trip time (RTT) ranging, RTCSI cancels the adverse effect of asynchronization through two-way communication. Since the phase of RTCSI is reliable and useful, a Doppler sensing prototype is built to detect a moving target in the wireless channel. Our findings show that the additional phase information utilised in RTCSI significantly enhances CSI-based WiFi sensing. Moreover, it may be integrated with other techniques to further improve the performance in joint communications and sensing
A High-Speed Multi-Purpose Software Defined Radar for Near-Field Applications
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
People counting using multistatic passive WiFi radar with a multi-input deep convolutional neural network
Accurately counting numbers people is useful in many applications. Currently, camera-based systems assisted by computer vision and machine learning algorithms represent the state-of-the-art. However, they have limited coverage areas and are prone to blind spots, obscuration by walls, shadowing of individuals in crowds, and rely on optimal positioning and lighting conditions. Moreover, their ability to image people raises ethical and privacy concerns. In this paper we propose a distributed multistatic passive WiFi radar (PWR) consisting of 1 reference and 3 surveillance receivers, that can accurately count up to six test subjects using Doppler frequency shifts and intensity data from measured micro-Doppler (µ-Doppler) spectrograms. To build the person-counting processing model, we employ a multi-input convolutional neural network (MI-CNN). The results demonstrate a 96% counting accuracy for six subjects when data from all three surveillance channels are utilised
FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram
Micro-Doppler signatures contain considerable information about target
dynamics. However, the radar sensing systems are easily affected by noisy
surroundings, resulting in uninterpretable motion patterns on the micro-Doppler
spectrogram. Meanwhile, radar returns often suffer from multipath, clutter and
interference. These issues lead to difficulty in, for example motion feature
extraction, activity classification using micro Doppler signatures (-DS),
etc. In this paper, we propose a latent feature-wise mapping strategy, called
Feature Mapping Network (FMNet), to transform measured spectrograms so that
they more closely resemble the output from a simulation under the same
conditions. Based on measured spectrogram and the matched simulated data, our
framework contains three parts: an Encoder which is used to extract latent
representations/features, a Decoder outputs reconstructed spectrogram according
to the latent features, and a Discriminator minimizes the distance of latent
features of measured and simulated data. We demonstrate the FMNet with six
activities data and two experimental scenarios, and final results show strong
enhanced patterns and can keep actual motion information to the greatest
extent. On the other hand, we also propose a novel idea which trains a
classifier with only simulated data and predicts new measured samples after
cleaning them up with the FMNet. From final classification results, we can see
significant improvements
Decimeter-Level Indoor Localization Using WiFi Round-Trip Phase and Factor Graph Optimization
Indoor localization using WiFi signals has been
studied since the emergence of WiFi communication. This paper
presents a novel training-free approach to indoor localization
using a customized WiFi protocol for data collection and a factor
graph-based back-end for localization. The protocol measures
the round-trip phase, which is very sensitive to small changes in
displacement. This is because the sub-wavelength displacements
introduce significant phase changes in WiFi signal. However, the
phase cannot provide absolute range information due to angle
wrap. Consequently, it can only be used for relative distance
(displacement) measurement. By tracking the round-trip phase
over time and unwrapping it, a relative distance measurement can
be realized and achieve a mean absolute error (MAE) of 0.06m.
For 2-D localization, factor graph optimization is applied to the
round-trip phase measurements between the STA (station) and
four APs (access points). Experiments show the proposed concept
can offer a decimeter-level (0.26m MAE and 0.24m 50%CDF)
performance for real-world indoor localization
MDPose:Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures
Motion tracking systems based on optical sensors typically often suffer from
issues, such as poor lighting conditions, occlusion, limited coverage, and may
raise privacy concerns. More recently, radio frequency (RF)-based approaches
using commercial WiFi devices have emerged which offer low-cost ubiquitous
sensing whilst preserving privacy. However, the output of an RF sensing system,
such as Range-Doppler spectrograms, cannot represent human motion intuitively
and usually requires further processing. In this study, MDPose, a novel
framework for human skeletal motion reconstruction based on WiFi micro-Doppler
signatures, is proposed. It provides an effective solution to track human
activities by reconstructing a skeleton model with 17 key points, which can
assist with the interpretation of conventional RF sensing outputs in a more
understandable way. Specifically, MDPose has various incremental stages to
gradually address a series of challenges: First, a denoising algorithm is
implemented to remove any unwanted noise that may affect the feature extraction
and enhance weak Doppler signatures. Secondly, the convolutional neural network
(CNN)-recurrent neural network (RNN) architecture is applied to learn
temporal-spatial dependency from clean micro-Doppler signatures and restore key
points' velocity information. Finally, a pose optimising mechanism is employed
to estimate the initial state of the skeleton and to limit the increase of
error. We have conducted comprehensive tests in a variety of environments using
numerous subjects with a single receiver radar system to demonstrate the
performance of MDPose, and report 29.4mm mean absolute error over all key
points positions, which outperforms state-of-the-art RF-based pose estimation
systems