Confocal microwave imaging for breast cancer detection relies on accurate knowledge of the average dielectric properties of the patient-specific breast. When accurately estimated, coherent addition will occur at the tumor site, producing a clear and sharp image thereof. Conversely, if the average dielectric properties are poorly estimated, a blurred, unfocused image will be reconstructed, potentially obscuring cancerous lesions. Several methods have been proposed to estimate the patient-specific average dielectric properties, for example, time-of-flight estimation. However, such methods are specific to the individual imaging hardware, can be susceptible to multipath propagation and assume the chosen paths are representative of the whole volume. In this paper, a novel method to estimate the patient-specific average dielectric properties is presented, based on focal quality metrics (FQMs); used historically to measure the clarity and focus of microscopic or digital photographic images. These FQMs are applied to confocal microwave breast images to assess their focus, and hence estimate the patient-specific average dielectric properties. In this way, FQMs can be used to generate the optimum microwave image of the breast. The performance and robustness of these FQMs for microwave breast imaging applications is examined in this paper and preliminary results are presented and discussed.

This paper compares the noise performance of two different types of time-domain microwave detection systems: a pulsed system and a pseudo-random noise sequence system. System-level simulations and laboratory-based measurements are carried out in the study. Results show that the effect of timing jitter is more significant on the measurement accuracy of the pseudo-random noise sequence system than that of the pulsed system. Although the signal power density of the pseudo-random sequence system is tens of dBs higher than that of the pulsed system over the frequency band of interest, the signal-to-noise ratio difference between these two systems can be just a few dBs or even smaller depending on the jitter level.

In this paper, an approach to differentiate between various dielectric threat objects in security applications is investigated. The scattering information in form of the Sinclair matrix of relevant scenarios is gained from a fully polarimetric, synthetic aperture radar. Both monostatic and multistatic array configurations are examined. A possible polarimetric calibration procedure is presented. The radar data are processed with the H-α decomposition algorithm. The H-α scattering characteristics of threat objects are analyzed in terms of a weighted averaging. It is shown that an object classification is possible even for threat objects conceiled under thick layers of clothing. Measurement results are presented to illustrate the topic.