Using continuous wave, 94-GHz millimeter-wave interferometry, a signal representing chest wall motion can be obtained that contains both the heart rate and respiration patterns of a human subject. These components have to be separated from each other in the received signal. Our method was to use the quadrature and in-phase components of the signal, after removing the mean of each, to find the phase, unwrap it, and convert it to a displacement measurement. Using this, the power spectrum was examined for peaks, which corresponded to the heart rate and respiration rate. The displacement waveform of the chest was also analyzed for discrete heartbeats using a novel wavelet decomposition technique.

A compact millimeter-wave (MMW) sensor has been developed for remote monitoring of human vital signs (heart and respiration rate). The low-power homodyne transceiver operating at 94 GHz was assembled by using solid-state active and passive block-type components and can be battery operated. A description of the MMW system front end and the back-end acquisition hardware and software is presented. Representative test case results on the application of various signal processing and data analysis algorithms developed to extract faint physiological signals of interest in presence of strong background interference are provided. Although the laboratory experiments so far have been limited to standoff distances of up to 15 m, the upper limit of the detection range is expected to be higher. In comparison with its microwave counterparts, the MMW system described here provides higher directivity, increased sensitivity, and longer detection range for measuring subtle mechanical displacements associated with heart and respiration functions. The system may be adapted for use in a wide range of standoff sensing applications including for patient health care, structural health monitoring, nondestructive testing, biometric sensing, and remote vibrometry in general.

Passive millimeter-wave (PMMW) imagers using a single radiometer, called single pixel imagers, employ raster scanning to produce images. A serious drawback of such a single pixel imaging system is the long acquisition time needed to produce a high-fidelity image, arising from two factors: (a) the time to scan the whole scene pixel by pixel and (b) the integration time for each pixel to achieve adequate signal to noise ratio. Recently, compressive sensing (CS) has been developed for single-pixel optical cameras to significantly reduce the imaging time and at the same time produce high-fidelity images by exploiting the sparsity of the data in some transform domain. While the efficacy of CS has been established for single-pixel optical systems, its application to PMMW imaging is not straightforward due to its (a) longer wavelength by three to four orders of magnitude that suffers high diffraction losses at finite size spatial waveform modulators and (b) weaker radiation intensity, for example, by eight orders of magnitude less than that of infrared. We present the development and implementation of a CS technique for PMMW imagers and shows a factor-of-ten increase in imaging speed.

This paper analyzes heart rate (HR) information from physiological tracings collected with a remote millimeter wave (mmW) I-Q sensor for biometric monitoring applications. A parameter optimization method based on the nonlinear Levenberg-Marquardt algorithm is used. The mmW sensor works at 94 GHz and can detect the vital signs of a human subject from a few to tens of meters away. The reflected mmW signal is typically affected by respiration, body movement, background noise, and electronic system noise. Processing of the mmW radar signal is, thus, necessary to obtain the true HR. The down-converted received signal in this case consists of both the real part (I-branch) and the imaginary part (Q-branch), which can be considered as the cosine and sine of the received phase of the HR signal. Instead of fitting the converted phase angle signal, the method directly fits the real and imaginary parts of the HR signal, which circumvents the need for phase unwrapping. This is particularly useful when the SNR is low. Also, the method identifies both beat-to-beat HR and individual heartbeat magnitude, which is valuable for some medical diagnosis applications. The mean HR here is compared to that obtained using the discrete Fourier transform.

In this paper, we present a novel passive millimeter-wave (PMMW) imaging system designed using compressive sensing principles. We employ randomly encoded masks at the focal plane of the PMMW imager to acquire incoherent measurements of the imaged scene. We develop a Bayesian reconstruction algorithm to estimate the original image from these measurements, where the sparsity inherent to typical PMMW images is efficiently exploited. Comparisons with other existing reconstruction methods show that the proposed reconstruction algorithm provides higher quality image estimates. Finally, we demonstrate with simulations using real PMMW images that the imaging duration can be dramatically reduced by acquiring only a few measurements compared to the size of the image.

We have developed two passive millimeter-wave imagers for terrestrial remote sensing: one is an integrated imaging and spectroscopy system in the 146-154-GHz range with 16 channels of 500-MHz bandwidth each, and the other is a single-channel dual-polarized imaging radiometer in the 70-100-GHz range. The imaging in both systems is implemented through translation of a 15-cm Gaussian dielectric imaging lens. We compared the outdoor images of objects such as car, vegetation, sky, and ground by both the systems under various weather conditions, including clear, cloudy, and rainy times. A ray-tracing simulation with radiative transfer equation was used to quantify the polarization diversity of the acquired images.