Miniaturized microwave radiometers deployed on nanosatellites in Low Earth Orbit are now demonstrating cost-effective weather monitoring capability, with increased temporal and spatial resolution compared to larger weather satellites. MicroMAS-2A is a 3U CubeSat that launched on January 11, 2018 with a 1U 10-channel passive microwave radiometer with channels near 90, 118, 183, and 206 GHz for moisture and temperature profiling and precipitation imaging. The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission is projected to launch in 2020, and its 1U 12-channel passive microwave radiometer is based on the current CubeSat mission MicroMAS-2A. TROPICS will provide rapid-refresh measurements over the tropics and measure environmental and inner-core conditions for tropical cyclones. In order to effectively use small satellites such as MicroMAS-2A and TROPICS as a weather monitoring platform, calibration must ensure consistency with state of the art measurements, such as the Advanced Technology Microwave Sounder (ATMS), which has a noise equivalent delta temperature (NEDT) at 300 K of 0.5 - 3.0 K. In this work, we present initial analysis from the MicroMAS-2A radiometric bias validation, which compares MicroMAS-2A measured brightness temperatures to simulated brightness temperatures calculated by the Community Radiative Transfer Model (CRTM) using input from GPS radio occultation (GPSRO), radiosonde, and numerical weather prediction (NWP) atmospheric profiles. We also model solar and lunar intrusions for TROPICS, and show that the frequency of intrusions with a scanning payload allows for the novel opportunity of using the solar and lunar intrusions as a calibration source.
An essential milestone in the development of lidar for biological aerosol detection is accurate characterization of agent,
simulant, and interferent scattering signatures. MIT Lincoln Laboratory has developed the Standoff Aerosol Active
Signature Testbed (SAAST) to further this task, with particular emphasis on the near- and mid-wave infrared.
Spectrally versatile and polarimetrically comprehensive, the SAAST can measure an aerosol sample's full Mueller
Matrix across multiple elastic scattering angles for comparison to model predictions. A single tunable source covers the
1.35-5 μm spectral range, and waveband-specific optics and photoreceivers can generate and analyze all six classic
polarization states. The SAAST is highly automated for efficient and consistent measurements, and can accommodate a
wide angular scatter range, including oblique angles for sample characterization and very near backscatter for lidar
This paper presents design details and selected results from recent measurements.
Polarimetric Lidar has been recently proposed as a method for remote detection of aerosolized biological warfare
agents. Accurate characterization of the optical signatures for both biological agents and environmental interferents is a
critical first step toward successful sensor deployment.
MIT Lincoln Laboratory has developed the Standoff Aerosol Active Signature Testbed (SAAST) as a tool for
characterizing aerosol elastic scattering cross sections.<sup>1</sup> The spectral coverage of the SAAST includes both the nearinfrared
(1-1.6 μm) and mid-infrared (3-4 μm) spectral regions. The SAAST source optics are capable of generating all
six classic optical polarization states, while the polarization-sensitive receiver is able to reconstruct the full Stokes
vector of the scattered wave. All scattering angles, including those near direct backscatter, can be investigated. The
SAAST also includes an aerosol generation system capable of producing biological and inert samples with various size
This paper discusses the underlying scattering phenomenology, SAAST design details, and presents some representative
A variety of new imaging modalities, such as optical diffusion tomography, require the inversion of a forward problem that is modeled by the solution to a three-dimensional partial differential equation. For these applications, image reconstruction can be formulated as the solution to a non-quadratic optimization problem.
In this paper, we discuss the use of nonlinear multigrid methods as both tools for optimization and algorithms for the solution of difficult inverse problems. In particular, we review some existing methods for directly formulating optimization algorithm in a multigrid framework, and we introduce a new method for the solution of general inverse problems which we call multigrid inversion. These methods work by dynamically adjusting the cost functionals at different scales so that they are consistent with, and ultimately reduce, the finest scale cost functional. In this way, the multigrid optimization methods can efficiently compute the solution to a desired fine scale optimization problem. Importantly, the multigrid inversion algorithm can greatly reduce computation because both the forward and the inverse problems are more coarsely discretized at lower resolutions. An application of our method to optical diffusion tomography shows the potential for very large computational savings.
A Bayesian optimization scheme is presented for reconstructing fluorescent yield and lifetime, the absorption coefficient, and the scattering coefficient in turbid media, such as biological tissue. The method utilizes measurements at both the excitation and emission wavelengths for reconstructing all unknown parameters. The effectiveness of the reconstruction algorithm is demonstrated by simulation and by application to experimental data from a tissue phantom containing a fluorescent agent.
Optical diffusion tomography is a new imaging modality that offers significant potential in medical applications. The resulting nonlinear image reconstruction problem is further complicated by the fact that for practical imaging variable source excitation and detector coupling needs to be accounted for in order to obtain quantitative images. We formulated the joint problem of coupling coefficient estimation and three-dimensional image reconstruction in a Bayesian framework, and the resulting estimates are computed in an iterative coordinate-descent optimization scheme. Simulations show that this approach is an accurate and efficient method for simultaneous reconstruction of absorption and diffusion coefficients, as well as the coupling coefficients.
We demonstrate accurate and efficient three-dimensional optical diffusion imaging using simulated noisy data from a set of measurements at a single modulation frequency. A Bayesian framework provides for prior model conditioning, and a dual-step cost function optimization allows sequential estimation of the data noise variance and the image.