A chaotic receiver estimation engine and method of use. The estimation engine synchronizes and recovers data and performs its decision and tracking processes by mapping probability calculation results onto chaotic dynamics via a strange attractor geometrical approximation. Restrictive standard chaotic synchronization requirements of either a stable/unstable subspace separation or a chaotic system inversion are not required. The receiver determines and models both the logical zero and logical one versions of the strange attractor and the transmitted chaotic sequence probability density function (PDF). Two estimates of the transmitted value are created from each received iterate by probability and the transmitted PDF calculations. A third estimate is generated from the chaotic processing of the previous receiver final decisions. The three estimates are combined using a probability-based weighted average to form the initial current decision. A final current decision incorporates chaotic dynamics by mapping the initial decision onto the geometrical model of the attractors via a minimum Euclidean distance metric.
Arthur Fleming-Dahl