Power allocation strategies for target localization in distributed multiple-radar architectures

Hana Godrich, Athina P. Petropulu, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

173 Scopus citations

Abstract

Widely distributed multiple radar architectures offer parameter estimation improvement for target localization. For a large number of radars, the achievable localization minimum estimation mean-square error (MSE), with full resource allocation, may extend beyond the predetermined system performance goals. In this paper, performance driven resource allocation schemes for multiple radar systems are proposed. All available antennas are used in the localization process. For a predefined estimation MSE threshold, the total transmitted energy is minimized such that the performance objective is met, while keeping the transmitted power at each station within an acceptable range. For a given total power budget, the attainable localization MSE is minimized by optimizing power allocation among the transmit radars. The Cramer-Rao bound (CRB) is used as an optimization metric for the estimation MSE. The resulting nonconvex optimization problems are solved through relaxation and domain decomposition methods, supporting both central processing at the fusion center and distributed processing. It is shown that uniform or equal power allocation is not necessarily optimal and that the proposed power allocation algorithms result in local optima that provide either better localization MSE for the same power budget, or require less power to establish the same performance in terms of estimation MSE. A physical interpretation of these conclusions is offered.

Original languageEnglish (US)
Article number5753953
Pages (from-to)3226-3240
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume59
Issue number7
DOIs
StatePublished - Jul 2011

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Cramer-Rao bound (CRB)
  • multiple-input multiple-output (MIMO) radar
  • multistatic radar
  • nonconvex optimization
  • resource allocation
  • target localization

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