A chaotic communication system of improved performance based on the Derivative-free nonlinear Kalman filter

Abstract

The Derivative-free nonlinear Kalman Filter is used for developing a communication system that is based on a chaotic modulator such as the Duffing system. In the transmitter’s side, the source of information undergoes modulation (encryption) in which a chaotic signal generated by the Duffing system is the carrier. The modulated signal is transmitted through a communication channel and at the receiver’s side demodulation takes place, after exploiting the estimation provided about the state vector of the chaotic oscillator by the Derivative-free nonlinear Kalman Filter. Evaluation tests confirm that the proposed filtering method has improved performance over the Extended Kalman Filter and reduces significantly the rate of transmission errors. Moreover, it is shown that the proposed Derivative-free nonlinear Kalman Filter can work within a dual Kalman Filtering scheme, for performing simultaneously transmitter–receiver synchronisation and estimation of unknown coefficients of the communication channel.

Keywords:

• chaotic communication system
• chaotic oscillators
• Derivative-free nonlinear Kalman Filter
• differential flatness theory
• synchronisation
• channel equalisation

Additional information

Notes on contributors

Gerasimos Rigatos

Dr. G. Rigatos obtained a diploma in 1995 and a Ph.D. in 2000, both from the Department of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), Greece. In 2001 he was a post-doctoral researcher at the Institut de Recherche en Informatique et Systèmes Aléatoires IRISA in Rennes, France. Since 2002 he holds a researcher position (Grade B) at the Industrial Systems Institute (Greek Secretariat for Research and Technology) on the topic of “Modelling and Control of Industrial Systems”. In 2007 he was an invited professor (maître des conférences) at Université Paris XI (Institut d’ Electronique Fondamentale). In 2012 he held a lecturer position at the Department of Engineering of Harper-Adams University College in Shropshire, UK on the topic of “Mechatronics and Artificial Intelligence”. He has been also an adjunct professor in Greek Universities where he has taught courses on systems and control theory. His research interests include the areas of computational intelligence and adaptive systems, mechatronics, robotics and control, optimization and fault diagnosis. He is editor-in-chief of Springer’s Journal on Intelligent Industrial Systems and member of the IEEE, IET and IMACS.