Data Fusion in Robotics and Machine Intelligence

Data fusion and sensor integration - state-of-the-art 1990s, R.C. Luo and M.G. Gay; multi-source spatial fusion using Bayesian reasoning, A. Elfes; multi-sensor strategies using Dempster/Shafer belief accumulation, S.A. Hutchinson and A.C. Kak; data fusion techniques using robust statistics, R. McKendall and M. Mintz; recursive fusion operators - desirable properties and illustrations, Y. Chen and R.L. Kashyap; distributed data fusion using Kalman filtering - a robotics application, C. Brown, et al; kinematic and satistical models for data fusion using Kalman filtering, T.J. Broida and S.S. Blackman; least-squares fusion of multi-sensory data, R.O. Eason and R.C. Gonzalez; fusion of multi-dimensional data using regularization, M.A. Abidi; geometric fusion - minimizing uncertainty ellipsoid volumes, Y. Nakamura; combination of fuzzy information in the framework of possibility theory, D. Dubois and H. Prade; data fusion - a neural networks implementation, T.L. Huntsberger.