2024

1. 

   Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach

   Published TAES

   Michael Potter, Murat Akcakaya, Marius Necsoiu, and 3 more authors

   IEEE Transactions on Aerospace and Electronic Systems, 2024

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   Radar Automated Target Recognition (RATR) for Unmanned Aerial Vehicles (UAVs) involves transmitting Electromagnetic Waves (EMWs) and performing target type recognition on the received radar echo, which has important applications in defense and aerospace. Previous work has demonstrated the benefits of employing multistatic radar configurations in RATR compared to monostatic radar configurations. However, multistatic radar configurations commonly use fusion methods which combine the classification vectors of multiple individual radars suboptimally from a probabilistic perspective. To address this issue, this work leverages Bayesian analysis to provide a fully Bayesian RATR framework for UAV type classification. Specifically, we employ an Optimal Bayesian Fusion (OBF) method, from the Bayesian perspective of expected 0-1 loss, to formulate a posterior distribution that aggregates the classification probability vectors from multiple individual radar observations at a given time step. This OBF method is used to update a separate Recursive Bayesian Classification (RBC) posterior distribution on the target UAV type. The RBC posterior is conditioned on all historical observations made from multiple radars across multiple time steps. To evaluate the proposed approach, we simulate random walk trajectories for seven drones and correspond the target’s aspect angles to Radar Cross Section (RCS) measurements acquired in an anechoic chamber. We then compare the performance of single radar Automated Target Recognition (ATR) system and suboptimal fusion methods against the OBF method. We empirically show that the OBF method, integrated with RBC, significantly outperforms other fusion methods and single radar configuration in terms of classification accuracy.

2. 

   Learning Semilinear Neural Operators: A Unified Recursive Framework For Prediction And Data Assimilation

   Published ICLR

   Ashutosh Singh, Ricardo Augusto Borsoi, Deniz Erdogmus, and 1 more author

   International Conference on Learning Representations, 2024

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   Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present a systematic framework to perform data assimilation and efficiently correct the evolution of PDE solutions over time based on sparsely sampled noisy measurements. In this paper, we propose a learning-based state-space approach to compute the solution operators to infinite-dimensional semilinear PDEs. Exploiting the structure of semilinear PDEs and the theory of nonlinear observers in function spaces, we develop a flexible recursive method that allows for both prediction and data assimilation by combining prediction and correction operations. The proposed framework is capable of producing fast and accurate predictions over long time horizons, dealing with irregularly sampled noisy measurements to correct the solution, and benefits from the decoupling between the spatial and temporal dynamics of this class of PDEs. We show through experiments on the KuramotoSivashinsky, Navier-Stokes and Korteweg-de Vries equations that the proposed model is robust to noise and can leverage arbitrary amounts of measurements to correct its prediction over a long time horizon with little computational overhead.

3. 

   Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder

   Published TMLR

   Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, and 5 more authors

   Transactions on Machine Learning Research, 2024

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   We study the task of gesture recognition from electromyography (EMG), with the goal of enabling expressive human-computer interaction at high accuracy, while minimizing the time required for new subjects to provide calibration data. To fulfill these goals, we define combination gestures consisting of a direction component and a modifier component. New subjects only demonstrate the single component gestures and we seek to extrapolate from these to all possible single or combination gestures. We extrapolate to unseen combination gestures by combining the feature vectors of real single gestures to produce synthetic training data. This strategy allows us to provide a large and flexible gesture vocabulary, while not requiring new subjects to demonstrate combinatorially many example gestures. We pre-train an encoder and a combination operator using self-supervision, so that we can produce useful synthetic training data for unseen test subjects. To evaluate the proposed method, we collect and release a real-world EMG dataset, and measure the effect of augmented supervision against two baselines: a partially-supervised model trained with only single gesture data from the unseen subject, and a fully-supervised model trained with real single and real combination gesture data from the unseen subject. We find that the proposed method provides a dramatic improvement over the partially-supervised model, and achieves a useful classification accuracy that in some cases approaches the performance of the fully-supervised model.

2023

1. 

   Recursive Estimation of User Intent From Noninvasive Electroencephalography Using Discriminative Models

   Published ICASSP

   Niklas Smedemark-Margulies, Basak Celik, Tales Imbiriba, and 2 more authors

   In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023

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   We study the problem of inferring user intent from noninvasive electroencephalography (EEG) to restore communication for people with severe speech and physical impairments (SSPI). The focus of this work is improving the estimation of posterior symbol probabilities in a typing task. At each iteration of the typing procedure, a subset of symbols is chosen for the next query based on the current probability estimate. Evidence about the user’s response is collected from event-related potentials (ERP) in order to update symbol probabilities, until one symbol exceeds a predefined confidence threshold. We provide a graphical model describing this task, and derive a recursive Bayesian update rule based on a discriminative probability over label vectors for each query, which we approximate using a neural network classifier. We evaluate the proposed method in a simulated typing task and show that it outperforms previous approaches based on generative modeling.