Research On Satellite Broadband Multiple Signals Detection And Parameter Estimation

The multi-signal detection and demodulation technology under the condition of broadband reception is widely used in the fields of electromagnetic spectrum detection,electronic warfare and other civil and military fields.The research of this technology is of important theoretical and practical significance.Broadband spectrum contains multiple signals.The carrier frequency,bandwidth and modulation pattern of the signals are different.Based on this background,this paper studies the multi-signal detection technology under broadband reception condition,the automatic modulation recognition and parameter estimation technology of each narrowband signal.The main work and innovation of this paper are as follows:1.This paper introduces the research status of existing broadband signal detection,modulation recognition and parameter estimation technology at home and abroad,analyzes the advantages and disadvantages of existing methods,compares the performance,computational complexity,pretreatment requirements of the methods.The research object of this paper is also proposed.2.In order to solve the problem that the performance of the algorithm of the existing broadband multi-signal detection is seriously affected by the parameter setting,the difficulty of setting the threshold and the detection of the occupied band of the signal is not accurate enough,a broadband multi-signal detection algorithm based on fractal box dimension and singular value decomposition is proposed.Firstly,the fractal box dimension of broadband multi-signal is calculated.According to the difference of the fractal box dimension of noise and noisy signal,it is judged whether there is any signal in the corresponding frequency band.According to the power spectrum of wideband signal obtained by periodic graph method,the Hankel matrix is constructed.Then the singular value decomposition of the Hankel matrix is achieved and the original signal power spectrum is decomposed.The occupied band information of each signal is obtained by detecting the singularity of the second order component.Simulation results show that the proposed method can judge whether the frequency band of interest is occupied or blank,and the estimation of number,bandwidth and center frequency of multi-signal is more accurate.3.A new modulation recognition method for the common amplitud-phase modulation signal is proposed.The signal set is {BPSK,QPSK,8PSK,16 QAM,64QAM,16 APSK,32APSK}.Firstly,the signal is pretreated to obtain the constellation in the presence of the frequency offset and noise.By constructing the square grid,the probability of the constellation points falling into different grid is calculated and the variance of the probabilities is calculated.If the signal-to-noise ratio is high,this parameter can be used to identify the signal of PSK,QAM and APSK.The feature of spectral lines is used to achieve the recognition of the MPSK signal and the identification of the MQAM and MAPSK signal is carried out by calculating the average radius of the constellation points.Simulation results show that the proposed method has strong anti-frequency offset capability.The extraction and calculation of the parameter is simple and the algorithm complexity is low.4.A new carrier frequency offset estimation algorithm is proposed to solve the problem that the modulation order of APSK signal is high and the frequency offset is difficult to estimate.First of all,the signal is pretreated to obtain the constellation with frequency offset.The amplitude radius of the constellation points of the signal is obtained,combined with the characteristics of the standard constellation of APSK signal,a reasonable threshold is set.Then the points on the ring with the smallest radius of the constellation is reserved with the rest of the constellation points set to be zero.The quartic spectrum of the zero-processed data is calculated so that the carrier frequency offset is estimated by detecting the corresponding frequency of the spectral line.Simulation results show that the algorithm achieves low computational complexity,simple steps and high accuracy when the signal-to-noise ratio is high.