- Open Access
Generalized function projective synchronization of chaotic systems for secure communication
© Xu; licensee Springer. 2011
- Received: 17 January 2011
- Accepted: 27 June 2011
- Published: 27 June 2011
By using the generalized function projective synchronization (GFPS) method, in this paper, a new scheme for secure information transmission is proposed. The Liu system is employed to encrypt the information signal. In the transmitter, the original information signal is modulated into the system parameter of the chaotic systems. In the receiver, we assume that the parameter of receiver system is uncertain. Based on the Lyapunov stability theory, the controllers and corresponding parameter update rule are constructed to achieve GFPS between the transmitter and receiver system with uncertain parameters, and identify unknown parameters. The original information signal can be recovered successfully through some simple operations by the estimated parameter. Furthermore, by means of the proposed method, the original information signal can be extracted accurately in the presence of additional noise in communication channel. Numerical results have verified the effectiveness and feasibility of presented method.
Mathematics subject classification (2010)
68M10, 34C28, 93A30, 93C40
- Generalized function projective synchronization
- Scaling function factor
- Liu chaotic system
- Secure communication
- Parameter modulation
Chaos is a kind of characteristics for nonlinear systems, which is a bounded unstable dynamic behavior that exhibits sensitive dependence on initial conditions and includes infinite unstable periodic motions. Since Pecora and Carroll  presented the conception of chaotic synchronization for two identical chaotic systems with different initial conditions, many synchronization methods have been proposed, such as complete synchronization (CS) , generalized synchronization , phase synchronization , impulse synchronization , lag synchronization , projective synchronization [6–8], etc. Amongst all kinds of chaos synchronization, projective synchronization, first reported by Mainieri and Rehacek , has been especially extensively studied because it can obtain faster communication with its proportional feature [9–12].
However, the above projective synchronization (PS) method is characterized that its drive and response systems are synchronized up to a constant scaling factor. Recently, Chen et al.  introduced a new PS scheme which is called function projective synchronization (FPS), where the responses of synchronized dynamical states can synchronize up to a scaling function factor. Let the scaling function be constant or unity, one can obtain PS or CS. So FPS is a more general definition of PS. Be-cause the unpredictability of the scaling function in FPS can additionally enhance security of communication, this feature could be applied to get more secure communication. More recently, many studies concentrate on FPS of chaotic systems and its application to secure communication [13–20]. For instance, FPS of two identical or different chaotic systems was studied in [13, 14, 16, 18]. In [15, 20], another new synchronization phenomenon, generalized function projective synchronization (GFPS), was proposed, in which drive and response systems could be synchronized to a scaling function matrix. In , Yu and Li investigated GFPS of two entirely different systems with fully unknown parameters. Du et al.  studied GFPS in coupled chaotic systems and its application in secure communication.
In the past decades, the use of chaotic signals for information transmission attracts great attention of modern scientists from various fields [16, 17, 20–26]. Different approaches for transmission of information signals using chaotic dynamics have been proposed, such as chaotic masking, chaotic modulation, nonlinear mixing, chaotic switching, and others. In a typical chaotic synchronization communication scheme, the information to be transmitted is carried from the transmitter to the receiver by a chaotic signal through an analog channel. In the receiver, chaos synchronization is employed to recover the information signal. In many existing secure communication methods, the information signal is directly added to input of chaotic systems. The magnitude of transmitted signals is required to be sufficiently small, otherwise it may lead to the instability of whole system. On the other hand, although these communication methods have been successfully demonstrated in simulations, performance of communication schemes were usually quantified by assuming the identical chaos synchronization based on exact knowledge of the system parameters [27, 28], which may impose some limitations to applicability of these techniques. But in real situation, some or all of parameters are unknown and the noise exists. The effect of these uncertainties and noise will destroy the synchronization and even break it. As a result, one cannot extract the original information in the receiver. Therefore, it is essential to study secure communication in the presence of unknown parameters and noise.
Motivated by the above discussions, this paper proposes a new secure communication scheme based on GFPS of uncertain Liu chaotic system and parameter modulation. In the transmitter, the original information signal is firstly transformed by an invertible function. Then the processed signal is modulated into the parameter of Liu system. The resulting system is still chaotic. In our method, no constraint is imposed on the magnitude of the original information signal. Suppose the parameter of the receiver system is unknown. Based on the Lyapunov stability theory, we design the controllers and the parameter update rule to realize GFPS of uncertain Liu chaotic systems and identify the unknown parameter of the receiver system. Then the information signal in the receiver can be recovered by the estimated parameter. Moreover, it is worth noting that secure communication using GFPS can still be realized fast even if the transmission channel is perturbed by additive noise.
The rest of this paper is organized as follows. In Section 2, Liu system is described briefly and the definition of GFPS is presented. Section 3 gives the chaotic secure communication scheme using GFPS and parameter modulation. By means of the Lyapunov stability theory and adaptive control, the controllers and corresponding parameter update rule are designed to ensure GFPS between two identical Liu chaotic systems with uncertain parameters. Simulation experiments of the proposed secure communication system have been performed in Section 4. The conclusions are finally drawn in Section 5.
2.1 The Liu system
The Liu system has a butterfly-shaped attractor similar to the Lorenz attractor but not equivalent. The existing studies have shown that the Liu system is equivalent to Shimizu-Morioka system and is a representative example of chaotic attractors. Compared to other chaotic systems, when the system parameters of Liu system vary in a certain range, this chaotic system can still be chaotic, further the dynamical behaviors of this system become more abundant and complex. Therefore, it is possible to modulate information signal into the parameter of chaotic systems to realize chaotic secure communication.
Remark 1 It is found that many chaotic systems can maintain chaotic behavior when a system parameter continuously changes in a certain range. We can obtain a "new" system parameter by modulating the information signal into the parameter of chaotic systems. If the "new" system parameter can ensure the corresponding system still be chaotic, the detector cannot extract the information signal from the signals, and transmitted in the channel. So it is possible to modulate the information signal into the parameter of chaotic systems to realize chaotic secure communication.
2.2 The definition of GFPS
where e= (e1, e2, ..., e n ) T , and Λ= diag(ϕ1(t), ϕ2t, ..., ϕ n (t)) is reversible and differentiable, where ϕ i (t): R n → R(≠ 0) is a continuously differentiable functions with bounded.
Definition 1 (GFPS) For the drive system (2) and the response system (3), it is said that system (2) and (3) are GFPS, if there exists a scaling function matrix Λ such that limt→∞||e|| = 0.
Remark 2 We call Λ a scaling function matrix and ϕ i (t) a scaling function factor, respectively. It is easy to see that if ϕ1(t) = ϕ2(t) = ⋯ = ϕ n (t), GFPS is simplified to FPS. If ϕi = a i (i = 1, 2, ..., n) where a i ∈ R and a i ≠ 0, GPS will occur. If Λ = λI where λ ∈ R is a constant and I n is an n × n identity matrix, GFPS is reduced to PS. In particular, if ϕ i (t) = 1 or ϕ i (t) = -1, the problem further becomes standard CS or anti-synchronization (AS).
3.1 The chaotic secure communication scheme using GFPS and parameter modulation
The secure communication system involves the development of a signal that contains the information which is to remain undetectable by others within a carrier signal. We can ensure the security of this information by inserting it into a chaotic signal which is transmitted to a prescribed receiver that would be able to detect and recover the information from the chaotic signal.
where the state vector X= (x, y, z).
The transmitter module is composed of a chaotic system S1 and an invertible transformation function φ. The "new" parameter β is formulated as a function of the original information signal f (t), i.e., β = φ(f (t)), which meets the need of the parameter range of c. So the resulting system still exhibits a chaotic behavior. In our method, the following two cases: no circumstance noise and additive noise in the transmission channel will be considered. The receiver module consists of another chaotic system S2, the controller U(S1, S2) and the corresponding inverse function φ-1. Assume that the parameter of the receiver system S2 is uncertain. In the receiver, with the help of controller U(S1, S2), GFPS between systems S1 and S2 can be realized. The unknown parameter can be asymptotically identified as GFPS appears. Then the original in-formation can be recovered through some simple operations by the function φ-1 and the estimated parameter , i.e., .
3.2 GFPS of uncertain Liu chaotic system
where m and M are known constants.
will still be chaotic, which has more abundant dynamic behavior. Since the resulting system (8) is chaotic, it is hard to detect information signal from the signals transmit-ted in the channel.
where u i (t) (i = 1, 2, 3) are controllers to be designed, is unknown parameter of receiver system which needs to be estimated.
Then we have the following main theorem.
Theorem 1 For given nonzero scaling functions ϕ i (t)(i = 1, 2, 3), GFPS between the transmitter system (8) and the receiver system (9) can occur by the controllers (14) and the parameter update rule (15). It implies that the GFPS errors satisfy limt→∞e i (t) = 0. The uncertain parameter is well estimated from the system parameter in the sense of .
where Q = diag(10, 1, β ) and β ∈ [0.5, 8]. Clearly, Q is a positive definite matrix and is negative definite. Based on the Lyapunov stability theory, the error dynamical system (13) is globally and asymptotically stable at the origin, and we have e β → 0, as t → ∞. Hence GFPS between the transmitter system (8) and the receiver system (9) is achieved and the uncertain parameter is also identified in the receiver end simultaneously under the controllers (14) and the parameter update rule (15). The proof is completed.
which implies that the original information signal can be recovered successfully in the receiver end.
Remark 3 In practical situations, if the information signal to be transmitted is too large, it will result in a chaotic system to be asymptotically stable or emanative. In this case, one may fail to extract the information signal. In the presented method, no constraint is imposed on the information signal. The original information signal is firstly transformed by an invertible function. Then the transformed signal is used as the parameter of Liu system. The resulting system exhibits more abundant chaotic behavior. The interceptor cannot extract the information from the transmitted signals in the channel.
To verify and demonstrate the effectiveness of the proposed chaotic secure communication scheme, some numerical examples are performed in this section. In the simulations, the ODE45 algorithm is applied to solve the differential systems. The initial states of the transmitter system (8) and the receiver system (9) are arbitrarily taken as x1(0), y1(0), z1(0) = (2, 1, - 1) and (x2(0), y2(0), z2(0)) = (-3, -2, 4), respectively. The initial values of the unknown parameter is chosen arbitrarily as .
4.1 Secure communication without circumstance noise
Similarly, we have also tested the proposed secure communication scheme for other values of θ. Limited to the length of this paper, we omit these results here. Numeric evidence shows that the larger the value of θ, the faster to achieve GFPS, and the more accurate the recovered signal is.
4.2 Secure communication with additive noise
In this paper, a novel chaotic secure communication scheme based on GFPS and parameter modulation is proposed. In the transmitter end, the original information signal is modulated into the parameter of the chaotic system and the resulting system still chaotic. There is no constraint posed on the original information signal. In the receiver, we assume that the parameter of the receiver system is uncertain. On the basis of the Lyapunov stability theory, the controllers and corresponding parameter update rule are devised to make the states of two identical Liu chaotic systems with unknown parameter synchronized. Simultaneously the uncertain parameter of the receiver system is also identified. Furthermore, the information signal can also be recovered accurately and fast by applying our secure communication method when additive noise exists in the transmission channel. Numerical simulations show the effectiveness and feasibility of the proposed chaotic secure communication scheme based on GFPS and parameter modulation.
We would like to thank Prof. Linpeng Huang and Dr. Xiangjun Wu for their valuable suggestions and discussion. In addition, we would like to thank the anonymous reviewers who have helped to improve the paper. This paper is partially supported by the National Natural Science Foundation of China (NSFC) under Grant No.60673116, 60970010, the National Grand Fundamental Research 973 Program of China under Grant No.2009CB320705, and the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20090073110026.
- Pecora LM, Carroll TL: Synchronization in chaotic systems. Phys Rev Lett 1990,64(8):821-824. 10.1103/PhysRevLett.64.821MathSciNetView ArticleMATHGoogle Scholar
- Ge Z, Chang C: Generalized synchronization of chaotic systems by pure error dynamics and elaborate Lyapunov function. Nonlinear Anal Theory Methods Appl 2009,71(11):5301-5312. 10.1016/j.na.2009.04.020MathSciNetView ArticleMATHGoogle Scholar
- Breve FA, Zhao L, Quiles MG, Macau EEN: Chaotic phase synchronization and desynchronization in an oscillator network for object selection. Neural Netw 2009,22(5-6):728-737. 10.1016/j.neunet.2009.06.027View ArticleGoogle Scholar
- Ren Q, Zhao J: Impulsive synchronization of coupled chaotic systems via adaptive-feedback approach. Phys Lett A 2006,355(4-5):342-347. 10.1016/j.physleta.2006.02.053MathSciNetView ArticleGoogle Scholar
- Li C, Liao X, Wong K: Chaotic lag synchronization of coupled time-delayed systems and its applications in secure communication. Physica D Nonlinear Phenom 2004,194(3-4):187-202. 10.1016/j.physd.2004.02.005MathSciNetView ArticleMATHGoogle Scholar
- Mainieri R, Rehacek J: Projective synchronization in three-dimensional chaotic systems. Phys Rev Lett 1999,82(15):3042-3045. 10.1103/PhysRevLett.82.3042View ArticleGoogle Scholar
- Xu D: Control of projective synchronization in chaotic systems. Phys Rev E 2001, 63: 27201-27204.View ArticleGoogle Scholar
- Xu D, Chee CY: Controlling the ultimate state of projective synchronization in chaotic systems of arbitrary dimension. Phys Rev E 2002,66(4):046218-046222.View ArticleGoogle Scholar
- Chee CY, Xu D: Secure digital communication using controlled projective synchronization of chaos. Chaos Soliton Fract 2005,23(3):1063-1070.View ArticleMATHGoogle Scholar
- Chen J, Jiao L, Wu J, Wang X: Projective synchronization with different scale factors in a driven-response complex network and its application in image encryption. Nonlinear Anal Real World Appl 2010,11(4):3045-3058. 10.1016/j.nonrwa.2009.11.003View ArticleMATHGoogle Scholar
- Hoang TM, Nakagawa M: A secure communication system using projective-lag and/or projectiveanticipating synchronizations of coupled multidelay feedback systems. Chaos Soliton Fract 2008,38(5):1423-1438. 10.1016/j.chaos.2008.02.008View ArticleGoogle Scholar
- Li Z, Xu D: A secure communication scheme using projective chaos synchronization. Chaos Soliton Fract 2004,22(2):477-481. 10.1016/j.chaos.2004.02.019View ArticleMATHGoogle Scholar
- Chen Y, Li X: Function projective synchronization between two identical chaotic systems. Int J Mod Phys C 2007,18(5):883-888. 10.1142/S0129183107010607View ArticleMATHGoogle Scholar
- Du H, Zeng Q, Wang C: Function projective synchronization of different chaotic systems with uncertain parameters. Phys Lett A 2008,372(33):5402-5410. 10.1016/j.physleta.2008.06.036View ArticleMATHGoogle Scholar
- Du H, Zeng Q, Wang C: Modified function projective synchronization of chaotic system. Chaos Soliton Fract 2009,42(4):2399-2404. 10.1016/j.chaos.2009.03.120View ArticleMATHGoogle Scholar
- Du H, Zeng Q, Wang C, Ling M: Function projective synchronization in coupled chaotic systems. Nonlinear Anal Real World Appl 2010,11(2):705-712. 10.1016/j.nonrwa.2009.01.016MathSciNetView ArticleMATHGoogle Scholar
- Du H, Zeng Q, Lü N: A general method for modified function projective lag synchronization in chaotic systems. Phys Lett A 2010,374(13-14):1493-1496. 10.1016/j.physleta.2010.01.058View ArticleMATHGoogle Scholar
- Luo R: Adaptive function projective synchronization of Rösler hyperchaotic system with uncertain parameters. Phys Lett A 2008,372(20):3667-3671. 10.1016/j.physleta.2008.02.035MathSciNetView ArticleMATHGoogle Scholar
- Sudheer KS, Sabir M: Adaptive function projective synchronization of two-cell Quantum-CNN chaotic oscillators with uncertain parameters. Phys Lett A 2009,373(21):1847-1851. 10.1016/j.physleta.2009.03.052MathSciNetView ArticleMATHGoogle Scholar
- Yu Y, Li H: Adaptive generalized function projective synchronization of uncertain chaotic systems. Nonlinear Anal Real World Appl 2010,11(4):2456-2464. 10.1016/j.nonrwa.2009.08.002MathSciNetView ArticleMATHGoogle Scholar
- Chen G, Dong X: From Chaos to Order: Methodologies, Perspectives and Applications. World Scientific, Singapore; 1998.View ArticleGoogle Scholar
- Kocarev L, Parlitz U: General approach for chaotic synchronization with applications to communication. Phys Rev Lett 1995,74(25):5028-5031. 10.1103/PhysRevLett.74.5028View ArticleGoogle Scholar
- Lin J-S, Huang C-F, Liao T-L, Yan J-J: Design and implementation of digital secure communication based on synchronized chaotic systems. Digital Signal Process 2010,20(1):229-237. 10.1016/j.dsp.2009.04.006View ArticleGoogle Scholar
- Moskalenko OI, Koronovskii AA, Hramov AE: Generalized synchronization of chaos for secure communication: remarkable stability to noise. Phys Lett A 2010,374(29):2925-2931. 10.1016/j.physleta.2010.05.024View ArticleMATHGoogle Scholar
- Wu X: A new chaotic communication scheme based on adaptive synchronization. Chaos: An Inter-disciplinary J Nonlinear Sci 2006,16(4):043118. 10.1063/1.2401058View ArticleMathSciNetMATHGoogle Scholar
- Zhu F: Observer-based synchronization of uncertain chaotic system and its application to secure communications. Chaos Soliton Fract 2009,40(5):2384-2391. 10.1016/j.chaos.2007.10.052View ArticleMATHGoogle Scholar
- Andrievsky B: Adaptive synchronization methods for signal transmission on chaotic carriers. Math Comput Simul 2002,58(4-6):285-293. 10.1016/S0378-4754(01)00373-1MathSciNetView ArticleMATHGoogle Scholar
- Bai EW, Lonngren KE: A Ucar, Secure communication via multiple parameter modulation in a delayed chaotic system. Chaos Soliton Fract 2005,23(3):1071-1076.View ArticleMATHGoogle Scholar
- Lorenz EN: Deterministic nonperiodic flow. J Atmos Sci 1963, 20: 130-141. 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2View ArticleGoogle Scholar
- Rösler OE: An equation for continuous chaos. Phys Lett A 1976,57(5):397-398. 10.1016/0375-9601(76)90101-8View ArticleGoogle Scholar
- Chen G, Ueta T: Yet another chaotic attractor. Int J Bifurc Chaos 1999,9(7):1465-1466. 10.1142/S0218127499001024MathSciNetView ArticleMATHGoogle Scholar
- Lü J, Chen G, Zhang S: The compound structure of a new chaotic attractor. Chaos Soliton Fract 2002,14(5):669-672. 10.1016/S0960-0779(02)00007-3View ArticleMathSciNetMATHGoogle Scholar
- Liu C, Liu T, Liu L, Liu K: A new chaotic attractor. Chaos Soliton Fract 2004,22(5):1031-1038. 10.1016/j.chaos.2004.02.060View ArticleMathSciNetMATHGoogle Scholar
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