We 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, an iterative, 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. In this paper, receiver operating characteristics and Cramer–Rao lower bounds are given for several undersea signal processing cases of interest.