Blind Beamforming for Collaborative Array Processing in Sensor Networks

Abstract

The detection, localization, tracking, and identification of a single target by acoustical/seismic measured data are fairly well understood. Many of the methods considered in the SensIT program proven in various ways for a single target in an open-air environment, will not be applicable to multiple targets. In the proposal, we advocated a new algorithm based on an efficient computational Approximate Maximum-Likelihood (AML) method using alternate projection to tackle the multiple target cases. The idea is that instead of performing the AML search in high dimensions for M targets, we first perform the ML estimate for the strongest target, then by fixing that target, we perform the ML estimate for the second strongest target, until the M-th target, and then iterate with the first target again.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2004
Accession Number
ADA424487

Entities

People

  • Kung Yao

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Angle Of Arrival
  • Detection
  • Detectors
  • Electrical Engineering
  • Engineering
  • Information Processing
  • Multiple Targets
  • Networks
  • Sensor Networks
  • Signal Processing
  • Supervised Machine Learning
  • Target Detection
  • Wireless Communications
  • Wireless Networks
  • Wireless Sensor Networks

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.