Practical Co-Prime and Nested Samplers and Arrays for Radar and Radar Sensor Networks

Abstract

This project is to develop practical co-prime and nested samplers and arrays for radar, sensor networks, and wireless communications. Major research tasks include: 1) Representation Learning and Nature Encoded Fusion for Heterogeneous Sensor Networks; 2) Sparse Nested Cylindrical Sensor Networks for Internet of Mission Critical Things; 3) Information Theoretic Bounds for Sparse Reconstruction in Random Noise; 4) Increasing Capacity of Multi-Cell Cooperative Cellular Networks with Nested Deployment; 5) Coprime Interpolation and Compressive Sensing for Future Heterogeneous Network Towards 5G; 6) Channel estimation for massive MIMO with 2-D nested array deployment. 4 Ph.D students have been supported by this ONR project and 2 have graduated with Ph.D. degrees.

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Document Details

Document Type
Technical Report
Publication Date
Dec 05, 2019
Accession Number
AD1086317

Entities

People

  • Qilian Liang

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Acquisition
  • Arrays
  • Cellular Networks
  • Channel Allocation
  • Channel Estimation
  • Compressed Sensing
  • Detectors
  • Electrical Engineering
  • Heterogeneous Networks
  • Information Theory
  • Sensor Networks
  • Signal Processing
  • Two Dimensional
  • Unmanned Aerial Systems
  • Wireless Communications
  • Wireless Networks
  • Wireless Sensor Networks

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Linear Algebra
  • Radio communications and signal processing.

Technology Areas

  • 5G
  • 5G - Internet of Things