Use of Chebyshev Polynomial Kalman Filter for pseudo-blind demodulation of CD3S signals

Abstract

Chaos based communication represents an attractive solution in order to design secure multiple access digital communication systems. In this paper we investigate the use of piecewise linear chaotic maps as chaotic generators combined, on the receiver side, with Chebyshev Polynomial Kalman Filters in a dual scheme configuration for demodulation purpose. Piecewise linear maps results into enhanced robustness properties of the spreading chaotic sequence, while approximation of nonlinear systems through Chebyshev polynomial series allows closed form estimation of mean and variance. Therefore, statistical moments can be computed by means of simple algebraic operations on matrices in compact form. In this work we extend these concepts to a dual Chebyshev Polynomial Kalman Filter scheme, suitable for signal recovery in chaos based spread spectrum systems. Numerical simulations show that the proposed method achieves lower error levels on a wide range of the bit-energy-tonoise- power-spectral-density ratio with respect to a state-of-the-art method based on unscented Kalman filters

A Synchronization Approach Based on Bidirectional Correlation Search for Aperiodic Chaotic Direct Sequence Spread Spectrum Signals

“…Accordingly, targeted intelligent jamming can be implemented on the pilot and fixed chaotic sequences [18]. In the semi-blind demodulation approach, the receiver uses state observer such as Extended Kalman Filter (EKF) [19]- [22], Unscented Kalman Filter (UKF) [23]- [26], Chebyshev Polynomial Kalman Filter (CPKF) [27] or Particle Filter (PF) [28]- [30] to estimate chaotic sequence from the received signal. In the semi-blind demodulation approach, the receiver can demodulate the message bits without locally generating the same chaotic sequence as the transmitter.…”