Breaking a chaotic direct sequence spread spectrum communication system using interacting multiple model-unscented Kalman filter

Abstract

In this paper, a new method to break chaotic direct sequence spread spectrum (CD3S) communication systems is proposed. Here, the CD3S communication system transmitting different information symbols is considered as a combination of two subsystems which are driven by two different chaotic dynamic models, respectively. At every single time moment, the CD3S signal can be regarded as generated by the subsystem corresponding to the information symbol transmitted. Then, based on the multiple model form of CD3S signals, an interacting multiple model unscented Kalman filter with model switching detection mechanism is exploited to track the CD3S signals. The l(2)-norm of tracking errors is used to choose the model which best matches the intercepted signals. Thus, the information symbols are recovered indirectly. Compared with the existing methods, the proposed algorithm can: (1) reduce the influence of a low spreading factor; (2) calculate the spreading factor using the length of time intervals between model switching; and (3) be more effective under scenarios of low signal-to-noise ratio or multipath fading. Simulation results verify the superiority of the proposed method.

A chaotic spread spectrum system for underwater acoustic communication

“…The other existing methods to break the CD3S system in Refs. [36][37][38] are based on the model in Ref. [35]; therefore, they are unable to decode the information of our proposed scheme from the received signal as well.…”

“…A growing interest in attacking chaotic spread spectrum communication has emerged. References [35][36][37][38] presented several methods for attacking CD3S communication systems. Reference [35] proposed a single Unscented Kalman Filter (UKF) filtering method to recover the binary information and the chaotic spread code in AWGN channel without any knowledge of the chaotic transmitter.…”

“…Reference [35] proposed a single Unscented Kalman Filter (UKF) filtering method to recover the binary information and the chaotic spread code in AWGN channel without any knowledge of the chaotic transmitter. Reference [36] used Interacting Multiple Model-Unscented Kalman Filter (IMM-UKF) to estimate spread spectrum code and binary information in multipath fading and noisy channel, which yields lower BER as compared to the method in [35]. Reference [37] proposed blind equalization and UKF to estimate binary information and chaotic spread code in channel with multipath propagation and white noise.…”

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

“…In fact, several works have been proposed with the aim of investigating the security levels guaranteed by these schemes. For instance, methods for breaking CD3S communications with different knowledge of the transmitter/receiver structure were introduced in recent years [13,14]. Figure 1 reports an example of three signals at different levels of a typical CD3S system.…”

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.…”