Research On Estimation Of Spread Spectrum Code For Non-cooperative DSSS Signals

The direct sequence spread spectrum(DSSS)signals expand the information frequency band through the spread spectrum code,which makes the transmission bandwidth much larger than the information bandwidth.The process of broadening the frequency band reduces the power spectral density of the transmitted signal and ensures that the signal can be transmitted under the condition of low SNR.Therefore,DSSS signals have good anti-jamming and anti-interception characteristics,and have been widely used in civil and military communications.But at the same time,the characteristics of DSSS signals also increase the difficulty of signal detection and estimation in non-cooperative communication.Therefore,in the field of communication countermeasure,the demodulating and despreading of DSSS signals and parameter estimation are of great value.In this thesis,the blind synchronization and estimation of spread spectrum codes in non-cooperative communication are studied.In order to solve the problem of blind synchronization of spread spectrum code,the information code difference function is constructed on the basis of analyzing the structure characteristics of base band DSSS signals.The mathematical relationship between the starting point of segmented window and the difference function is analyzed,the relationship between the peak position of the function and the synchronization time of the spread spectrum code is derived.The simulation results show that the ICDM algorithm can achieve correct blind synchronization of spread spectrum code.And the influence of spread spectrum code on the performance of the ICDM algorithm is analyzed.In order to solve this problem,the signal is processed by delay multiplication before using the ICDM algorithm.Compared with the improved ICDM algorithm and the sliding window norm maximum algorithm,it is proved that the algorithm proposed in this thesis has lower computational complexity and occupies less hardware storage resources.In the study of spread spectrum code estimation based on principal component analysis:The EVD algorithm obtains the spread spectrum code estimation by extracting the maximum feature vector of the signal covariance matrix.To solve the problem of high computational complexity of the EVD algorithm,this thesis proposes an iterative algorithm based on the CW function to extract the feature vector,which avoids the process of matrix decomposition with high computational complexity and improves the real-time processing.The neural network algorithm can also extract the principal component,the estimation of spread spectrum code can be obtained by using the neural network algorithm with the constraint Hebb learning rule.In order to solve the problem of slow convergence,this thesis proposes the CHA neural network algorithm based on the variable correlation factor,which not only does not affect the estimation performance,but also speeds up the convergence speed and improves the work efficiency.In the study of spread spectrum code estimation based on correlation analysis: Based on the cyclic correlation characteristic of DSSS signals,correlation algorithm estimates the spread spectrum code by making correlation decisions on the signals in different periods,but it is susceptible to noise.In order to solve this problem,this thesis proposes a correlation algorithm based on variable decision basis,which uses the set average method to suppress noise and achieves good estimation performance under the condition of low SNR.In the study of spread spectrum code estimation based on maximum likelihood estimate:Cross entropy represents the similarity between two probability distributions,by using this characteristic,the cross entropy of the received signal sampling sequence and a group of sequences to be determined are compared after transformed into probability distribution.The sequence with a small cross entropy is the estimation of spread spectrum code.In order to solve the problem that the number of decision sequences is too large,the cross entropy algorithm based on bitwise decision is proposed to estimate the spread spectrum code,and the simulation results show the effectiveness of the proposed algorithm.There are similarities between the block coding and the modulation process of base band DSSS signals and between the channel decoding and the estimation of spread spectrum code.Based on the maximum likelihood decoding criterion,an inner product sum function is constructed,and the sequence to be determined with the maximum value of the function is searched as the estimation of the spread spectrum code.Aiming at the high computational complexity of the search process,the Viterbi algorithm and the BVA algorithm are used to improve the estimation performance under the condition of low SNR.