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publications
Spatial Interference Cancellation for Simultaneous Wireless and Information Power Transfer
Published in U.S. Patent and Trademark Office, 2020
A discrete-time delay (TD) technique in a baseband receiver array is disclosed for canceling wide modulated bandwidth spatial interference and reducing the Analog-to-Digital Conversion (ADC) dynamic range requirements. In particular, the discrete-time delay (TD) technique first aligns the interference using non-uniform sampled phases followed by uniform cancellation using a cancellation matrix, such as, for example, a Truncated Hadamard Transform implemented with antipodal binary coefficients.
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
Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications and data storage systems. SC codes are constructed by partitioning an underlying block code, followed by rearranging and concatenating the partitioned components in a “convolutional” manner. The number of partitioned components determines the “memory” of SC codes. While adopting higher memories results in improved SC code performance, obtaining optimal SC codes with high memory is known to be hard. In this paper, we investigate the relation between the performance of SC codes and the density distribution of partitioning matrices. We propose a probabilistic framework that obtains (locally) optimal density distributions via gradient descent. Starting from random partitioning matrices abiding by the obtained distribution, we perform low complexity optimization algorithms over the cycle properties to construct high memory, high performance quasi-cyclic SC codes. Simulation results show that codes obtained through our proposed method notably outperform state-of-the-art SC codes with the same constraint length and codes with uniform partitioning.
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://ieeexplore.ieee.org/abstract/document/9517931
Toward Data-Driven STAP Radar
Published in IEEE Radar Conference (RadarConf), 2022
Using an amalgamation of techniques from classical radar, computer vision, and deep learning, we characterize our ongoing data-driven approach to space-time adaptive processing (STAP) radar. We generate a rich example dataset of received radar signals by randomly placing targets of variable strengths in a predetermined region using RFView, a site-specific radio frequency modeling and simulation tool developed by ISL Inc. For each data sample within this region, we generate heatmap tensors in range, azimuth, and elevation of the output power of a minimum variance distortionless response (MVDR) beamformer, which can be replaced with a desired test statistic. These heatmap tensors can be thought of as stacked images, and in an airborne scenario, the moving radar creates a sequence of these time-indexed image stacks, resembling a video. Our goal is to use these images and videos to detect targets and estimate their locations, a procedure reminiscent of computer vision algorithms for object detection-namely, the Faster Region Based Convolutional Neural Network (Faster R-CNN). The Faster R-CNN consists of a proposal generating network for determining regions of interest (ROI), a regression network for positioning anchor boxes around targets, and an object classification algorithm; it is developed and optimized for natural images. Our ongoing research will develop analogous tools for heatmap images of radar data. In this regard, we will generate a large, representative adaptive radar signal processing database for training and testing, analogous in spirit to the COCO dataset for natural images. Subsequently, we will build upon, adapt, and optimize the existing Faster R-CNN framework, and develop tools to detect and localize targets in the heatmap tensors discussed previously. As a preliminary example, we present a regression network in this paper for estimating target locations to demonstrate the feasibility of and significant improvements provided by our data-driven approach.
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://ieeexplore.ieee.org/abstract/document/9764354
Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks
Published in arXiv preprint arXiv:2209.02890, 2023
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 first 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 through our proposed deep learning framework for target location estimation. In this procedure, we generate comprehensive datasets by randomly placing targets of variable strengths in predetermined constrained areas using RFView, a site-specific, digital twin, RF modeling and simulation tool. For each radar return from these predefined constrained areas, we generate heatmap tensors in range, azimuth, and elevation of the NAMF test statistic, and of the output power of a generalized sidelobe canceller (GSC). Using our deep learning framework, we estimate target locations from these heatmap tensors to demonstrate the feasibility of and significant improvements provided by our data-driven approach across matched and mismatched settings.
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," arXiv preprint arXiv:2209.02890, 2023, doi: 10.48550/arXiv.2209.02890. https://arxiv.org/abs/2209.02890
Subspace Perturbation Analysis for Data-Driven Radar Target Localization
Published in 2023 IEEE Radar Conference (RadarConf), 2023
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 subspace 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.
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://ieeexplore.ieee.org/document/10149781
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
Published:
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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