Parameters Estimation Of DSSS Signals Based On Blind Source Separation

Direct sequence spread spectrum signals which are also called DSSS signals are widelyused in military and civilian communications. Especially in situation of electronic war,it’s difficult to detect and estimate parameters of enemy’s signals without enough priorinformation. And in the real environment of communication,the problem becomes morecomplex. Because that received signals are almost the mixture of multiple DSSS signals.To deal with this situation, we introduce the theory of Blind Source Separation into thejoint parameters estimation problem,and mainly consider the models which are closed toreal communication environment. Our innovations and main work is composed of theseparts:1. Consider that the number of DSSS signals which is needed to detect is unknown and changes with time in the real environment, a novel improved RLS algorithm combing with an online source number estimation method is proposed. First using the estimation of mean value and covariance matrix of observed signals to define a cost function.Then the function is minimized to achieve the estimation of source number. The dimensions of separated matrix and other related parameter matrices can be dynamic modified according to the estimated source number, which makes the RLS-like algorithm can separate the source signals efficiently in the super-condition and dynamic source numbers environments. Computer simulations show that the proposed algorithm is better than the existing algorithms with faster convergence and lower error index. In the application of parameters estimation of DSSS signals, the proposed algorithms can also trace the change of source number and estimate the parameters accurately.2. To solve the problem of joint parameters estimation of multiple DSSS signals in the low signal-to-noise ratio environment, the Expectation Maximum algorithm is usedto alternately estimate the mixing matrix and original signals. Then a denosingsource separation framework is proposed, using the equvalence of nonlineardenosing function and posterior expectation function of source estimation, manynonlinear Independent Component Analysis based algorithms is included in thisframework. The denosing principle of nonlinear function is expounded based on thedescription of the linear denosing function. And the unitary of separating anddenosing is also explained. In the condition of none prior knowledge of noisecovariance martrix, simulations result show that proposed denosing sourceseparation algorithm has better performance than the exsiting algorithm. And in theapplication of DSSS signals parameters estimation, results show that proposedmethod can also work well in the environment of high level of noise.3. Two new joint block diagonalization algorithms are proposed to solve the blindconvolutive separation of DSSS signals. The convolutive mixture is rewritten as aninstantaneous one which satisfied the joint block diagonalization model. In theminimization problem of the first cost function, considering that the exsiting iterativealgorithms may not converge to the correct solution, a special structured separationmatrix which is always invertible is proposed to aviod the divergence of thealgorithm. The whole matrix iteration is transformed to update of the each blocksub-matrix as that the minimization of the cost function is equivalent to theminimization of Frobenius norm of each block. The iterative algorithm is deducedboth in the situation of real and complex model. Then in the optimization of thesecond cost function,with the help of matrix-vector operator, the original peoblem istransfromed to a multivariant least squares model. A simple iterative joint blockdiagonalization algorithm is deduced using the differential matrix of orthogonalprojection operator. Computer simulation demonstrate the good performance of two algorithms in the different condition, and the base band DSSS signals can also beseparated successfully in the application of blind convolutive separation.4. A method based on characteristic function and matrxi-*algebraic is proposed to solvethe convolutive blind separation of DSSS signals in the high noise level environment.The main idea is from the knowledge of wireless communication that increasing thenumbers of receivers to improve the signal to noise ratio. First,the signals modelunder the condition of multiple sensors is built and proved to be consistent with thebasis of the Independent subspace analysis. Then the concept of independentsubspace analysis is introduced,also the Hessian of Characteristic functions of theDSSS signals are proved to be block diagonal.Finally the matrix decompositiontheory of matrix-*algebraic is used to transform the joint block diagonalization ofmultiple matries into the problem of finding a generic matrix of the commutantalgebra, which is correspond to the matrix-*algebraic formed by the Hessian ofCharacteristic functions of the observed signals. And the diagonalization of thegeneric matrix is proved to be equivalent with the Joint block diagnolization of somematrix-*algebraic. Then the original problem comes down to finding a randomsolution of a homogeneous linear equations. The theory analyze implies that theproposed algorithm is very robust if the sensor noise is gaussian and shares the samevariance,whenever the noise is white or color. Computer simulation shows thevalidity and reliability of the algorithm in the condition of three DSSS signals. Theresult also demonstrates that the proposed algorithm has better performance and lessconstraints comparing with the exsiting algorithms.