Lossless information fusion for active ranging and detection systems

Journal article

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. © 2006 IEEE.

Authors: Sibul, L; Roan, M; Schwartz, S; Coviello, C

• Publication status: Published
• Journal: IEEE Transactions on Signal Processing
• Volume: 54
• Issue: 10
• Pages: 3980-3990
• Publication date: 2006-10-01
• DOI: 10.1109/TSP.2006.880197
• ISSN: 1053-587X