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Toward Data-Driven STAP Radar

Publication ,  Conference
Venkatasubramanian, S; Wongkamthong, C; Soltani, M; Kang, B; Gogineni, S; Pezeshki, A; Rangaswamy, M; Tarokh, V
Published in: Proceedings of the IEEE Radar Conference
January 1, 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.

Duke Scholars

Published In

Proceedings of the IEEE Radar Conference

DOI

EISSN

2375-5318

ISSN

1097-5764

Publication Date

January 1, 2022
 

Citation

APA
Chicago
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MLA
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Venkatasubramanian, S., Wongkamthong, C., Soltani, M., Kang, B., Gogineni, S., Pezeshki, A., … Tarokh, V. (2022). Toward Data-Driven STAP Radar. In Proceedings of the IEEE Radar Conference. https://doi.org/10.1109/RadarConf2248738.2022.9764354
Venkatasubramanian, S., C. Wongkamthong, M. Soltani, B. Kang, S. Gogineni, A. Pezeshki, M. Rangaswamy, and V. Tarokh. “Toward Data-Driven STAP Radar.” In Proceedings of the IEEE Radar Conference, 2022. https://doi.org/10.1109/RadarConf2248738.2022.9764354.
Venkatasubramanian S, Wongkamthong C, Soltani M, Kang B, Gogineni S, Pezeshki A, et al. Toward Data-Driven STAP Radar. In: Proceedings of the IEEE Radar Conference. 2022.
Venkatasubramanian, S., et al. “Toward Data-Driven STAP Radar.” Proceedings of the IEEE Radar Conference, 2022. Scopus, doi:10.1109/RadarConf2248738.2022.9764354.
Venkatasubramanian S, Wongkamthong C, Soltani M, Kang B, Gogineni S, Pezeshki A, Rangaswamy M, Tarokh V. Toward Data-Driven STAP Radar. Proceedings of the IEEE Radar Conference. 2022.

Published In

Proceedings of the IEEE Radar Conference

DOI

EISSN

2375-5318

ISSN

1097-5764

Publication Date

January 1, 2022