The Approach And Application Of Noise Jamming Signal Processing Based On Stochastic Differential

Noise is a jamming signal in everywhere.Cohen said,"it would be a dull,gray worldwithout noise",on the 100th anniversary of its birth.On the basis of probability densityfunction many aspects of noise jamming signal,such as chaos,bifurcation and stochasticresonance,could be studied in essence.But it is difficult for getting the time-dependentsolution to probability density function because the partial differential equation could notbe solved accurately.At present its steady state solution was only given in the literature.Inthe paper the application of noise in radar jamming and image signal processing wasanalyzed using stochastic differential method.It is the important problem in the radar countermeasures that the radar jammingeffectiveness evaluation.The basis of the radar jamming effectiveness evaluation is radarjamming signal processing.According to the intrinsic relations between the stochasticdifferential and the radar jamming signal processing,the stochastic calculus was used inthe radar jamming signal processing in this paper.The radio frequency noise jammingsignal was particularly analyzed.The Fokker-Planck equation of radio frequency noisewas presented and the Motion-Group Fourier Transform was used by converting thepartial differential equation into the homogenous linear differential equations.So thetime-dependent solution to probability density function of radio frequency noise in thefilter was given.On the basis of the probability density function the stochastic resonanceof radio frequency noise is analyzed.Similarly the noise frequency modulation signal was analyzed.The Fokker-Planckequation of noise frequency modulation was presented and the Motion-Group FourierTransform was used by converting the partial differential equation into the homogenouslinear differential equations.Then the solutions were given by using the matrixexponential.Through motion-group fourier inverse transform the time-dependent solutionto probability density function of noise frequency modulation in the filter was given.Onthe basis of the probability density function the stochastic resonance of noise frequencymodulation signal is analyzed.The noise frequency modulation signal was particularly analyzed for pulse compression radar.The normal stochastic differential equations in polar coordinates weregiven by the definition of stochastic differential.The Fokker-Planck equation of noisefrequency modulation was presented and the Motion-Group Fourier Transform was usedby converting the partial differential equation into the variable coefficient homogenouslinear differential equations.Then the solutions were given by using the peano-bakerseries.So the time-dependent solution to probability density function of noise frequencymodulation in the pulse compression radar filter was given.At last the stochasticresonance of noise frequency modulation signal is analyzed.According to image system intrinsic quality of self-comparability and the empiricalmode decomposition (EMD)algorithms' completeness and stability,an image filteringalgorithm using stochastic differential was presented based on EMD.It is fast andeffective.It can improve EMD decomposition's bidimensional interpolation method andend conditions of getting intrinsic mode images,and solve the questions of generalalgorithms for EMD image decomposition,such as low speed in decomposition,failure tocontain whole points,and the uncertainty of end requirements.If the image wasdecomposition,the image of intrinsic mode function was processed through filter ofstochastic differential in a long time and the image of residual passed the filter ofstochastic differential in a short time.Then the denoising image was given by rebuildingthe IMFs and residual image of denoise.The simulation experiments were made byMatlab and the effectiveness of the improved algorithm was tested and verified.