Multi Antenna Radar System for American Sign Language Recognition Using Deep Learning

This paper investigates RF-based system for automatic American Sign Language (ASL) recognition. We consider radar for ASL by joint spatio-temporal preprocessing of radar returns using time frequency (TF) analysis and high-resolution receive beamforming. The additional degrees of freedom offered by joint temporal and spatial processing using a multiple antenna sensor can help to recognize ASL conversation between two or more individuals. This is performed by applying beamforming to collect spatial images in an attempt to resolve individuals communicating at the same time through hand and arm movements. The spatio-temporal images are fused and classified by a convolutional neural network (CNN) which is capable of discerning signs performed by different individuals even when the beamformer is unable to separate the respective signs completely. The focus group comprises individuals with varying expertise with sign language, and real time measurements at 77 GHz frequency are performed using Texas Instruments (TI) cascade radar.

Sparse Array Beamformer Design for Active and Passive Sensing

Sparse sensor placement, with various design objectives, has successfully been employed in diverse application areas, particularly for enhanced parameter estimation and receiver performance. The sparse array design criteria are generally categorized into environment-independent and environment-dependent performance metrics. The former are largely benign to the underlying environment and, in principle, seek to maximize the spatial degrees of freedom by extending the coarray aperture. Environment-dependent objectives, on the other hand, consider the operating conditions characterized by emitters and targets in the array field of view, in addition to receiver noise. In this regard, applying such objectives renders the array configuration as well as the array weights time-varying in response to dynamic and changing environment. This work is geared towards designing environment-dependent sparse array beamformer to improve the output signal-to-interference and noise ratio using both narrowband and wideband signal platforms. One key challenge in implementing the data-dependent approaches is the lack of knowledge of exact or estimated values of the data autocorrelation function across the full sparse array aperture. At the core of this work is to address the aforementioned issues by devising innovative solutions using convex optimization and machine learning tools, structured sparsity concepts, low rank matrix completion schemes and fusing the environment-dependent and environment-independent deigns by developing a hybrid approach.