Search

Accurate human identification using radar has a variety of potential applications, such as surveillance, access control and security checkpoints. Nevertheless, radar-based human identification has been limited to a few motion-based biometrics that are solely reliant on micro-Doppler signatures. This paper proposes for the first time the use of combined radar-based heart sound and gait signals as biometrics for human identification. The proposed methodology starts by converting the extracted biometric signatures collected from 18 subjects to images, and then an image augmentation technique is applied and the deep transfer learning is used to classify each subject. A validation accuracy of 58.7% and 96% is reported for the heart sound and gait biometrics, respectively. Next, the identification results of the two biometrics are combined using the joint probability mass function (PMF) method to report a 98% identification accuracy. To the best of our knowledge, this is the highest reported in the literature to date. Lastly, the trained networks are tested in an actual scenario while being used in an office access control platform to identify different human subjects. We report an accuracy of 76.25%.

This paper introduces a new Bayesian approach to the inverse problem of passive microwave rainfall retrieval. The proposed methodology [called the shrunken locally linear embedding algorithm for retrieval of precipitation (ShARP)] relies on a regularization technique and makes use of two joint dictionaries of coincident rainfall profiles and their corresponding upwelling spectral radiative fluxes. A sequential detection-estimation strategy is adopted, which basically assumes that similar rainfall intensity values and their spectral radiances live close to some sufficiently smooth manifolds with analogous local geometry. The detection step employs a nearest neighbor classification rule, whereas the estimation scheme is equipped with a constrained shrinkage estimator to ensure the stability of retrieval and some physical consistency. The algorithm is examined using coincident observations of the active precipitation radar and the passive microwave imager onboard the TRMM satellite. We present promising results of instantaneous rainfall retrieval for some tropical storms and mesoscale convective systems over ocean, land, and coastal zones. We provide evidence that the algorithm is capable of properly capturing different storm morphologies including high-intensity rain cells and trailing light rainfall, particularly over land and coastal areas. The algorithm is also validated at an annual scale for calendar year 2013 versus the standard (version 7) radar (2A25) and radiometer (2A12) rainfall products of the TRMM satellite.

This paper introduces a new Bayesian approach to the inverse problem of passive microwave rainfall retrieval. The proposed methodology [called the shrunken locally linear embedding algorithm for retrieval of precipitation (ShARP)] relies on a regularization technique and makes use of two joint dictionaries of coincident rainfall profiles and their corresponding upwelling spectral radiative fluxes. A sequential detection-estimation strategy is adopted, which basically assumes that similar rainfall intensity values and their spectral radiances live close to some sufficiently smooth manifolds with analogous local geometry. The detection step employs a nearest neighbor classification rule, whereas the estimation scheme is equipped with a constrained shrinkage estimator to ensure the stability of retrieval and some physical consistency. The algorithm is examined using coincident observations of the active precipitation radar and the passive microwave imager onboard the TRMM satellite. We present promising results of instantaneous rainfall retrieval for some tropical storms and mesoscale convective systems over ocean, land, and coastal zones. We provide evidence that the algorithm is capable of properly capturing different storm morphologies including high-intensity rain cells and trailing light rainfall, particularly over land and coastal areas. The algorithm is also validated at an annual scale for calendar year 2013 versus the standard (version 7) radar (2A25) and radiometer (2A12) rainfall products of the TRMM satellite.

As part of the NASA International Polar Year activities, a Ka-band cross-track interferometric synthetic aperture radar (SAR) recently demonstrated high-precision elevation swath mapping capability. This proof-of-concept instrument was achieved by interfacing two Ka-band slotted-waveguide antennas in a cross-track geometry and Ka-band electronics with the Jet Propulsion Laboratory's L-band uninhabited aerial vehicle SAR. Deployed on the NASA Gulfstream III, initial engineering flights in March and April 2009 marked the first airborne demonstration of single-pass cross-track interferometry at Ka-band. Results of a preliminary interferometric assessment indicate height precisions that, for a 3 m × 3 m posting, range from 30 cm in the near range to 3 m in the far range and greater than 5 km of swath over the urban areas imaged. The engineering flights were followed by a comprehensive campaign to Greenland in May 2009 for ice-surface topography mapping assessment. Toward that end, coordinated flights with the NASA Wallops Airborne Topographic Mapper lidar were conducted in addition to establishing ground calibration sites at both the Summit Station of the National Science Foundation and the Swiss Camp of the Cooperative Institute for Research in the Environmental Sciences. Comparisons of the radar-derived elevation measurements with both in situ and lidar data are planned for a subsequent paper; however, at this stage, a single data example over rugged ice cover produced a swath up to 7 km with the desired height precision as estimated from interferometric correlation data. While a systematic calibration, including assessment and modeling of biases, due to penetration of the electromagnetic waves into the snow cover has not yet been addressed, these initial results indicate that we will exceed our system requirements. © 2006 IEEE.

As part of the NASA International Polar Year activities, a Ka-band cross-track interferometric synthetic aperture radar (SAR) recently demonstrated high-precision elevation swath mapping capability. This proof-of-concept instrument was achieved by interfacing two Ka-band slotted-waveguide antennas in a cross-track geometry and Ka-band electronics with the Jet Propulsion Laboratory's L-band uninhabited aerial vehicle SAR. Deployed on the NASA Gulfstream III, initial engineering flights in March and April 2009 marked the first airborne demonstration of single-pass cross-track interferometry at Ka-band. Results of a preliminary interferometric assessment indicate height precisions that, for a 3 m × 3 m posting, range from 30 cm in the near range to 3 m in the far range and greater than 5 km of swath over the urban areas imaged. The engineering flights were followed by a comprehensive campaign to Greenland in May 2009 for ice-surface topography mapping assessment. Toward that end, coordinated flights with the NASA Wallops Airborne Topographic Mapper lidar were conducted in addition to establishing ground calibration sites at both the Summit Station of the National Science Foundation and the Swiss Camp of the Cooperative Institute for Research in the Environmental Sciences. Comparisons of the radar-derived elevation measurements with both in situ and lidar data are planned for a subsequent paper; however, at this stage, a single data example over rugged ice cover produced a swath up to 7 km with the desired height precision as estimated from interferometric correlation data. While a systematic calibration, including assessment and modeling of biases, due to penetration of the electromagnetic waves into the snow cover has not yet been addressed, these initial results indicate that we will exceed our system requirements. © 2006 IEEE.