Lossless information fusion for active ranging and detection systems

Leon H. Sibul, Michael J. Roan, Stuart C. Schwartz, Christian M. Coviello

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The authors develop a centralized information fusion architecture from basic principles of information theory and Bayesian statistics. It is well known that any clustering, quantizing, or thresholding of data causes loss of information unless a sufficient statistic is computed in the processing. For the case of wideband active ranging systems, the coherent output of an optimum beamformer and a matched filter is a sufficient statistic that can be transmitted to the fusion center. For unknown target velocity, range, and bearing, the wideband space-time matched filter output can be interpreted as a multidimensional wavelet transform or a delay-scale-bearing map. In this paper, a Bayesian, joint estimation-detection approach is used for computation of sufficient statistics and multisensor information fusion. An approach borrowed from sequential Bayesian processing is used to compute prior densities for joint Bayesian estimation-detection. In this approach, a posteriori densities become priors after a coordinate transformation that transforms the outputs of each sensor to a common reference frame for all sensors. Reproducing prior densities are used to simplify Bayesian computation.

Original languageEnglish (US)
Pages (from-to)3980-3990
Number of pages11
JournalIEEE Transactions on Signal Processing
Volume54
Issue number10
DOIs
StatePublished - Oct 2006

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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