Modeling and Simulation Architecture for Studying Doppler-Based Radar with Complex Environments

This research effort develops a hybrid large-scale modeling and simulation frame- work that defines the requirements for a program to evaluate radar-aircraft-turbine- clutter interactions. Wind turbines and other moving structures can interfere with a radar’s ability to detect moving aircraft because radar returns from turbines are comparable to those from slow flying aircraft. This interference can lead to aircraft collisions or crashes, reducing the safety for air traffic. Two radar applications, INSSITE and IMOM, were investigated to determine which of the subsystems, in the proposed architecture, are currently available and which need additional development. Current radar applications either delve too deep into details, requiring years to process, or too shallow, ignoring the Doppler effect and assuming a static scattering value. Engineering-level radar, radiation, propagation, and scattering models are already developed. However, engagement-level stochastic scattering, amplitude and phase, data aren’t available. The hybrid modeling and simulation architecture could be realized once stochastic RCS models are developed

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

Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches

Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler ($\boldsymbol{\mu}$-D) and micro-Range ($\boldsymbol{\mu}$-R), respectively. Different moving targets will have unique $\boldsymbol{\mu}$-D and $\boldsymbol{\mu}$-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99\% accuracy in identifying humans from robots on a single R-D map.Comment: 6 pages, accepted in IEEE Radar Conference 201