Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. This paper explains the rationale behind the selection of the ML network architecture, along with other model hyperparameters, in an effort to demystify the process of arriving at a useful ML model. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real-time applications.Comment: 13 pages, 7 figure

The Federal Aviation Administration/Massachusetts Institute of Technology (FAA/MIT) Lincoln Laboratory Doppler weather radar program

The program focuses on providing real-time information on hazardous aviation weather to end users such as air traffic control and pilots. Existing systems will soon be replaced by a Next Generation Weather Radar (NEXRAD), which will be concerned with detecting such hazards as heavy rain and hail, turbulence, low-altitude wind shear, and mesocyclones and tornadoes. Other systems in process are the Central Weather Processor (CWP), and the terminal Doppler weather radar (TDWR). Weather measurements near Memphis are central to ongoing work, especially in the area of microbursts and wind shear

SFNet: Learning Object-aware Semantic Correspondence

We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We propose to use images annotated with binary foreground masks and subjected to synthetic geometric deformations to train a convolutional neural network (CNN) for this task. Using these masks as part of the supervisory signal offers a good compromise between semantic flow methods, where the amount of training data is limited by the cost of manually selecting point correspondences, and semantic alignment ones, where the regression of a single global geometric transformation between images may be sensitive to image-specific details such as background clutter. We propose a new CNN architecture, dubbed SFNet, which implements this idea. It leverages a new and differentiable version of the argmax function for end-to-end training, with a loss that combines mask and flow consistency with smoothness terms. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms the state of the art on standard benchmarks.Comment: cvpr 2019 oral pape

Multiple Target, Multiple Type Filtering in the RFS Framework

A Multiple Target, Multiple Type Filtering (MTMTF) algorithm is developed using Random Finite Set (RFS) theory. First, we extend the standard Probability Hypothesis Density (PHD) filter for multiple types of targets, each with distinct detection properties, to develop a multiple target, multiple type filtering, N-type PHD filter, where $N\geq2$, for handling confusions among target types. In this approach, we assume that there will be confusions between detections, i.e. clutter arises not just from background false positives, but also from target confusions. Then, under the assumptions of Gaussianity and linearity, we extend the Gaussian mixture (GM) implementation of the standard PHD filter for the proposed N-type PHD filter termed the N-type GM-PHD filter. Furthermore, we analyze the results from simulations to track sixteen targets of four different types using a four-type (quad) GM-PHD filter as a typical example and compare it with four independent GM-PHD filters using the Optimal Subpattern Assignment (OSPA) metric. This shows the improved performance of our strategy that accounts for target confusions by efficiently discriminating them