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Posts
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portfolio
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publications
Spatial Interference Cancellation for Simultaneous Wireless and Information Power Transfer
Published in U.S. Patent and Trademark Office, 2020
Recommended citation: S. Gupta, E. Ghaderi, S. Shekhar, S. Venkatasubramanian and A. S. Ramani, "Spatial Interference Cancellation for Simultaneous Wireless and Information Power Transfer," U.S. Patent 10,804,988, Oct. 13, 2020. https://patents.google.com/patent/US10804988B2/en
GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes With High Memories
Published in IEEE International Symposium on Information Theory (ISIT), 2021
Recommended citation: S. Yang, A. Hareedy, S. Venkatasubramanian, R. Calderbank and L. Dolecek, "GRADE-AO: Towards Near-Optimal Spatially-Coupled Codes With High Memories," IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, 2021, pp. 587-592, doi: 10.1109/ISIT45174.2021.9517931. https://doi.org/10.1109/ISIT45174.2021.9517931
Toward Data-Driven STAP Radar
Published in IEEE Radar Conference (RadarConf), 2022
Recommended citation: S. Venkatasubramanian, C. Wongkamhthong, M. Soltani, B. Kang, S. Gogineni, A. Pezeshki, M. Rangaswamy and V. Tarokh, "Toward Data-Driven STAP Radar," IEEE Radar Conference (RadarConf), New York City, NY, USA, 2022, pp. 1-5, doi: 10.1109/RadarConf2248738.2022.9764354. https://doi.org/10.1109/RadarConf2248738.2022.9764354
Subspace Perturbation Analysis for Data-Driven Radar Target Localization
Published in IEEE Radar Conference (RadarConf), 2023
Recommended citation: S. Venkatasubramanian, S. Gogineni, B. Kang, A. Pezeshki, M. Rangaswamy and V. Tarokh, "Subspace Perturbation Analysis for Data-Driven Radar Target Localization," IEEE Radar Conference (RadarConf), San Antonio, TX, USA, 2023, pp. 1-5, doi: 10.1109/RadarConf2351548.2023.10149781. https://doi.org/10.1109/RadarConf2351548.2023.10149781
Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound
Published in arXiv preprint, 2024
Recommended citation: S. Venkatasubramanian, S. Gogineni, B. Kang, M. Rangaswamy, "Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound," arXiv preprint arXiv:2401.11176, 2024, doi: 10.48550/arXiv.2401.11176. https://doi.org/10.48550/arXiv.2401.11176
Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks
Published in The 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024
Recommended citation: S. Venkatasubramanian, A. Aloui and V. Tarokh, "Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks," The 40th Conference on Uncertainty in Artificial Intelligence, 2024, doi: 10.48550/arXiv.2311.12356 . https://proceedings.mlr.press/v244/venkatasubramanian24a.html
RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications
Published in arXiv preprint, 2024
Recommended citation: S. Venkatasubramanian, B. Kang, A. Pezeshki, M. Rangaswamy and V. Tarokh, "RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications," arXiv preprint, 2024, doi: 10.48550/arXiv.2406.09638. https://doi.org/10.48550/arXiv.2406.09638
Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks
Published in IET Radar, Sonar & Navigation, 2024
Recommended citation: S. Venkatasubramanian, S. Gogineni, B. Kang, A. Pezeshki, M. Rangaswamy and V. Tarokh, "Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks," IET Radar, Sonar & Navigation, 2024, doi: 10.1049/rsn2.12600. https://doi.org/10.1049/rsn2.12600
Learn2Mix: Training Neural Networks Using Adaptive Data Integration
Published in arXiv preprint, 2024
Recommended citation: S. Venkatasubramanian, A. Pezeshki and V. Tarokh, "Learn2Mix: Training Neural Networks Using Adaptive Data Integration," arXiv preprint, 2024, doi: 10.48550/arXiv.2412.16482. https://doi.org/10.48550/arXiv.2412.16482
Steinmetz Neural Networks for Complex-Valued Data
Published in The 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Recommended citation: S. Venkatasubramanian, A. Pezeshki and V. Tarokh, "Steinmetz Neural Networks for Complex-Valued Data," The 28th International Conference on Artificial Intelligence and Statistics, 2025, doi: 10.48550/arXiv.2409.10075. https://doi.org/10.48550/arXiv.2409.10075
talks
Toward Data-Driven STAP Radar
Published:
Using an amalgamation of techniques from classical radar, deep learning, and computer vision, we characterize our ongoing data-driven approach to space-time adaptive processing (STAP) radar. We generate a rich dataset of received radar signals for random target locations and strengths via the RFView simulation software, and generate heatmap image tensors in range, azimuth, and elevation of this received signal data. We present a regression convolutional neural network (CNN) for estimating target locations from these image tensors, and conclude by demonstrating the feasibility of and significant improvements provided by our data-driven approach.
Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks
Published:
Facilitated by the recent emergence of radio frequency (RF) modeling and simulation tools purposed for adaptive radar processing applications, data-driven approaches to classical problems in radar have rapidly grown in popularity over the past decade. Despite this surge, limited focus has been directed toward the theoretical foundations of these data-driven approaches. In this regard, using adaptive radar processing techniques, we propose a data-driven approach in this work to address the classical problem of radar target localization post adaptive radar detection. To give context to the performance of this data-driven approach, we analyze the asymptotic breakdown signal-to-clutter-plus-noise ratio (SCNR) threshold of the normalized adaptive matched filter (NAMF) test statistic within the context of radar target localization, and augment this analysis by benchmarking our proposed deep learning framework for target location estimation across matched and mismatched settings.
Subspace Perturbation Analysis for Data-Driven Radar Target Localization
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Recent works exploring data-driven approaches to classical problems in adaptive radar have demonstrated promising results pertaining to the task of radar target localization. Via the use of space-time adaptive processing (STAP) techniques and convolutional neural networks, these data-driven approaches to target localization have helped benchmark the performance of neural networks for matched scenarios. However, the thorough bridging of these topics across mismatched scenarios still remains an open problem. As such, in this work, we augment our data-driven approach to radar target localization by performing a sub-space perturbation analysis, which allows us to benchmark the localization accuracy of our proposed deep learning framework across mismatched scenarios. To evaluate this framework, we generate comprehensive datasets by randomly placing targets of variable strengths in mismatched constrained areas via RFView®, a high-fidelity, site-specific modeling and simulation tool. For the radar returns from these constrained areas, we generate heatmap tensors in range, azimuth, and elevation using the normalized adaptive matched filter (NAMF) test statistic. We estimate target locations from these heatmap tensors using a convolutional neural network, and demonstrate that the predictive performance of our framework in the presence of mismatches can be predetermined.
Data-Driven Radar Target Position and Velocity Estimation
Published:
Target position and velocity estimation is a crucial aspect in the design of modern radar systems, being foundational to various radar applications such as surveillance, navigation, and military operations. This task involves determining the position and velocity of a target in the presence of clutter and noise using radar STAP techniques. Much of the framework for estimating these target attributes is defined post radar STAP detection, where received radar signals are spatially and temporally processed to detect and subsequently estimate target positions and velocities. Despite their advancements, however, radar STAP methods have several limitations, including (1) sensitivity to noise and interference, and (2) high computational costs. Expanding upon (1), the interference due to clutter, modeled via the clutter covariance matrix, must be estimated from limited available samples when it is unknown a priori. This weakens any optimality claims made regarding the adaptive radar detection and subsequent target position and velocity estimation procedures (i.e., finding the peak location from a detection test statistic). Resultantly, while optimal detection can be achieved in the non-adaptive case (where the true clutter covariance matrix is known), we cannot make claims of invariance in the adaptive case (where the clutter covariance matrix is unknown). As a notional proof in the context of suboptimal detection for the adaptive case, we analyze whether the accuracy of target position and velocity estimation post radar STAP detection can be improved using a data-driven methodology.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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