19 research outputs found
Enhancing microwave imaging by exploiting diversity
No full textMicrowaves can penetrate optically opaque materials without harmful ionizing effects, and thus provide distinct advantages over x-ray imaging in various fields, including security-screening, remote sensing, medical imaging, through-wall imaging, civil and industrial applications. However, conventional microwave imaging methods, such as synthetic aperture radar (SAR) or phased array methods, generally rely on mechanical or electrical scanning to collect the spatial data and the resulting imaging speed and complexity remain challenges for constructing microwave imaging systems. In this thesis, I investigate the use of diversity techniques to develop high-speed, low-profile and low-cost microwave imaging systems. Firstly, I propose a single-shot frequency-diverse near-field imaging system by exploiting a frequency diversity technique based on high-scanning-rate leaky-wave antennas (LWAs). Frequency diversity is an all-electronic technique and can achieve data collection by sweeping frequency without mechanical moving parts or active switching circuit component. In addition, the potential performance of the LWA design is analyzed by introducing a figure of merit based on sensing capacity. It is revealed that a transceiver LWA with a high scanning rate is very effective for providing independent measurement modes. Analytical, simulation and experimental results are provided to characterize and demonstrate the proposed system and show that it can provide image reconstruction across narrow bandwidths. Building on frequency diversity techniques, I investigate the use of high scanning-rate leaky-wave antennas in a multiple-input multiple-output (MIMO) configuration to achieve both frequency and spatial diversity in an imaging system. Compared with frequency-diversity only and spatial-diversity only imaging systems, the proposed system can provide enhanced imaging performance by leveraging both spatial and frequency diversity simultaneously. In addition, an extended Rytov approximation (xRA), recently shown to provide accurate reconstructions for high permittivity and electrically large sized low-loss objects, is also included in this approach. Numerical and experimental examples demonstrate that the proposed system with xRA can accurately estimate the contrast function amplitude and the positions of dielectric scatterers in an imaging region. Apart from frequency and spatial diversity techniques, I investigate the use of pattern diversity for reducing the number of required measurement nodes in utilizing xRA for radio frequency (RF) imaging. For indoor RF imaging using radio tomographic imaging (RTI), 20-40 WiFi nodes are usually utilized around the imaging region. This implies that a conventional WiFi network must be supplemented with additional WiFi nodes specifically dedicated to the imaging application. The proposed approach is to exploit antenna pattern diversity so that each node can collect multiple independent measurements from the same measurement location, thereby decreasing the number of measurement nodes required. Simulation results are provided to verify the RF imaging approach with reduced measurement nodes, which demonstrates the potential of using pattern diversity. In all the research contributions described, analyses, simulations and/or experimental results are utilized to demonstrate the effectiveness of my new and novel approaches to microwave imaging.</p
Received Signal Strength Estimation in Indoor Environment Using High Frequency Rytov Approximation
No full textA Low-Profile Folded Transmitarray for Single-Shot Random Transmit-Receive Sensing
No full textA low-profile folded transmitarray for dual-random-field sensing is presented. The system relies on a randomly arranged folded array and a single feed source to generate random transmit-receive fields by switching the feed polarization. The folded design with a transmitarray surface (TAS) and a polarization-conversion reflective surface (PCRS) operating over a 34% bandwidth reduces the overall height while maintaining desired sensing functionality. The system has an aperture size of 12λ×12 λ×2 λ with a low height-to-diameter ratio (H/D) of 0.16. The random transmit-receive fields generated by the system allow for more independent measurements and enhanced sensing capabilities and accuracy, which is validated by the sensing results. The proposed compact transceiver has the potential of high-resolution real-time sensing applications.</p
Physics Assisted Deep Learning for Indoor Imaging using Phaseless Wi-Fi Measurements
No full textA physics assisted deep learning framework to perform accurate indoor imaging
using phaseless Wi-Fi measurements is proposed. It is able to image objects
that are large (compared to wavelength) and have high permittivity values, that
existing radio frequency (RF) inverse scattering techniques find very
challenging, making it suitable for indoor RF imaging. The technique utilizes a
Rytov based inverse scattering model with a deep learning framework. The
inverse scattering model is based on an extended Rytov approximation (xRA) that
pre-reconstructs the RF measurements. Under strong scattering conditions, this
pre-reconstruction is related to the actual permittivity profile by a
non-linear function, which is learned by a modified U-Net model to obtain the
permittivity profile of the object. Thus, our proposed approach not only
reconstructs the shape of objects, but also estimates their permittivity values
accurately. We demonstrate its imaging performance using simulations as well as
experimental results in an actual indoor environment using 2.4 GHz Wi-Fi
phaseless measurements. For incident wavelength , the proposed
framework can reconstruct objects with relative permittivity as high as 77 and
electrical size as large as , where .
This is in contrast to existing phaseless imaging techniques which cannot
reconstruct permittivity values beyond 3 or 4. Thus, our proposed method is the
first inverse scattering-based deep learning framework which can image large
scatterers with high permittivity and achieve accurate indoor RF imaging using
phaseless Wi-Fi measurements.Comment: 14 pages, 10 figures. This work has been submitted to IEEE for
possible publicatio
Physics Assisted Deep Learning for Indoor Imaging Using Phaseless Wi-Fi Measurements
No full textA physics-assisted deep learning framework to perform accurate indoor imaging using phaseless Wi-Fi measurements is proposed. It can image objects that are larger than wavelength and have high permittivity that existing radio frequency (RF) inverse scattering techniques find very challenging. The technique utilizes a Rytov-based inverse scattering model and deep learning. The inverse scattering model is based on an extended Rytov approximation (xRA) that prereconstructs RF measurements. Under strong scattering conditions, this prereconstruction is related to the actual permittivity profile by a nonlinear function, which is learned by a modified U-Net model to obtain the permittivity profile. Thus, our proposed approach both reconstructs the shape of objects and estimates their permittivity values accurately. We demonstrate its performance using simulations and experiment results in actual indoor environments using 2.4 GHz Wi-Fi phaseless measurements. For incident wavelength λ0, the framework can reconstruct objects with permittivity as high as 77 and electrical size up to 40λ, where λ =λ0√77 as opposed to existing phaseless techniques, which cannot reconstruct permittivity beyond 3 or 4. Thus, our proposed method is the first inverse scattering-based deep learning framework, which can image large scatterers with high permittivity and achieve accurate indoor RF imaging using phaseless Wi-Fi measurements.</p
Received Signal Strength Estimation in Indoor Environment Using High Frequency Rytov Approximation
No full textWe present a new and improved formulation of Rytov approximation (RA) using the theory of ray tracing in low loss media for solving the direct scattering problem. The resultant model provides an accurate prediction of wave scattering in the presence of electrically large scatterers with high permittivity and small loss tangent. The high validity range and the straightforward linear formulation of the proposed model make it suitable for accurately predicting the received signal strength (RSS) in indoor environments. We provide experimental results from a real indoor environment to show that the proposed method closely matches the experimental RSS values and achieves up to ten times higher validity range than any other existing linear approximate models. Also, since the proposed method provides an accurate solution to the direct scattering problem, it can be an ideal candidate for inverse scattering applications.</p
Reducing the Number of Measurement Nodes in RF Imaging Using Antenna-Pattern Diversity With an Extended Rytov Approximation
No full textRadio frequency (RF) imaging that leverages existing wireless communication infrastructure, such as radio tomographic imaging (RTI) and joint communication and sensing (JCAS) is becoming increasingly important. A challenge of RF imaging is that it requires large measurement datasets containing independent measurements. In this article, we investigate a method to reduce the number of measurement nodes in RF imaging so that it is more suitable for integration with wireless communication. The approach is to exploit antenna-pattern diversity so that each node can collect multiple independent measurements from the same measurement location, thereby decreasing the number of measurement nodes required. Furthermore, we formulate pattern diversity for RF imaging using the recently developed extended Rytov approximation (xRA), which has been demonstrated to provide remarkable RF reconstruction accuracy. The advantage of utilizing xRA is that it allows us to utilize the metric of sensing capacity to straightforwardly quantify the potential of various pattern diversity configurations. Using the sensing capacity metric, we are able to identify configurations where the number of measurement nodes can be reduced by at least a factor of 2. Simulation results are provided to verify the RF imaging approach with reduced measurement nodes, which demonstrates the potential of using pattern diversity
