Research On Target Signal Separation Technology

It is very difficult to obtain the target signal due to the lack of prior information such assource signal and channel in non-cooperative communication. The blind signal separation(BSS),which only use mixed observed signal to separate and reconstruct target signal, has become oneof effective solution methods on target signal separation in non-cooperative communicationbecause of its independence on source signal. Therefore, aiming at how to achieve target signalseparation efficiency in non-cooperative communication, the main contents of the paper are asfollows:1. Considering the poor performance caused by signal with noise blind separation, thispaper proposed an adaptive blind signal separation algorithm based on JADE and waveletde-noising. The algorithm introduced the wavelet de-noising and consists of observed signalspre-de-noising and separated source signal post-de-noising, in order to use the differentadaptability of these two styles according to different Gaussian source, added SNRpre-estimation and realized the adaptive selection, therefore improved separation algorithmadaptability to different SNR.2. Without the knowledge of the amount of source signal, due to the inaccurately estimatingthe number of source signals through PCA, so the performance of signal separation wasdegraded. The improved whitening MDL-FastICA algorithm was proposed aimed at solving thisproblem. The algorithm estimated the number of source signal by the MDL to accuratelyseparate the signal and noise subspace, and based on that, noise average variance was estimatedby the eigenvalue of signal and in turn modify the main eigenvalue of signal, suppress noiseeffect. Simulation result shows that amount of source signal is more accurate and separationperformance is improved obviously in the proposed algorithm compared with traditional FastICAalgorithm in different condition of Gaussian source.3. The probability density function estimation is foundation of many blind signal separationalgorithm, but the algorithm separation performance will be severely declined when selectingunsuitable activation function. Aiming at this problem, this paper proposed the blind signalseparation algorithm based on normalized fourth-order cumulant. Considering maximizing theabsolute value of normalized fourth-order cumulant as the objective function, the algorithmcould avoid the activation function selection. The PSO-CF method was adopted to solve theoptimization problem, in which the whole situation optimum solution of whole situation and theprominent merit of low algorithm complexity could be acquired. Simulation shows that thesignal of super-Gaussian, sub-Gaussian and mixed Gaussian is successfully separated in theproposed algorithm and the strong applicability, fast convergence speed and great separationperformance could be achieved.4. Aiming at the actual application requirement of extracting finite target signal frommulti-source mixed signal, a limited blind target signal extract algorithm based on PSO-CF wasproposed, in which the efficiency of blind signal separation algorithm based on normalized fourth-order cumulant could be further increased. By estimating error between separate signaland expectative signal, the reference independent component analysis was introduced to obtainthe evaluation of separate efficiency as the convergence indication of separate algorithm.inwhich the target signal separation was achieved by least time cost. Simulation result shows thatthe algorithm has great separation performance, low complexity and high run efficiency.