Continuous-capture microwave imaging

Archita Hati,Anthony B Kos,Craig W Nelson,Jason B Coder,Grace E Antonucci,Fabio C S Da Silva

doi:10.1038/s41467-021-24219-0

Archita Hati, Anthony B Kos + Show 4 more

Open Access

https://doi.org/10.1038/s41467-021-24219-0

Copy DOI

Abstract

Light-in-flight sensing has emerged as a promising technique in image reconstruction applications at various wavelengths. We report a microwave imaging system that uses an array of transmitters and a single receiver operating in continuous transmit-receive mode. Captures take a few microseconds and the corresponding images cover a spatial range of tens of square meters with spatial resolution of 0.1 meter. The images are the result of a dot product between a reconstruction matrix and the captured signal with no prior knowledge of the scene. The reconstruction matrix uses an engineered electromagnetic field mask to create unique random time patterns at every point in the scene and correlates it with the captured signal to determine the corresponding voxel value. We report the operation of the system through simulations and experiment in a laboratory scene. We demonstrate through-wall real-time imaging, tracking, and observe second-order images from specular reflections.

Highlights

Light-in-flight sensing has emerged as a promising technique in image reconstruction applications at various wavelengths
Time-of-flight imaging focuses on reconstructing a scene by measuring delayed stimulus responses via continuous wave, impulses or pseudo-random binary sequence (PRBS) codes[1]
For first-order reflections, the algorithm reduces to a single dot product between the reconstruction matrix and captured signal, and can be executed in a few milliseconds

Summary

Results

By using different PRBS codes for each transmitter pulse train, this configuration creates a spatiotemporal mask that can probe the whole scene continuously in time (Fig. 2). The calculation of the second-order sampling matrix A2 involves an extra loop accounting for the scene voxels weighted by the first-order image mask where the test condition is based on r1 + r2 + r3. To obtain the remainder of the vertical wall, we used the normalized first-order image in Fig. 4b as an input for the calculation of the second-order reconstruction matrix as described above (Fig. 6). If memory of the order M × N2 is available, all second-order contributions can be calculated in no more than 3.6 s using the above GPU hardware

Discussion

Methods

Code availability