Study On Application Of Computational Intelligence Methods To The Parameter Estimation In The Noncooperative Signals  Posted on:20060318  Degree:Doctor  Type:Dissertation  Country:China  Candidate:Y Q Chen  Full Text:PDF  GTID:1118360185456759  Subject:Communication and Information System  Abstract/Summary:  PDF Full Text Request  It is well known that the maximum likelihood estimation (MLE) is the most popular and important methods of the statistics. MLE is also the effective consistent estimation. Although the performance of the MLE is the most optimal, it involves the high computational load of the multivariate nonlinear maximization problem. The computational load of the MLE limits it practical application in the present chip technology. The main application area of the computational intelligence methods is the hard solved problems in the optimization, namely the nondeterministic polynomial (NP) problems. Based on this consideration, this thesis is dedicated to the application of the computational intelligence methods such as tabu search, chaos optimization algorithm, genetic algorithm and their improved algorithms to solve the two NP optimization problems which are pseudo noise (PN) code estimation of the direct sequence spread spectrum (DS/SS) signal and the maximum likelihood localization of multiple sources in the noncooperative context.The main contribution of this thesis can be summarized as follows:(1) This thesis presents maximum likelihood estimation to perform the PN code sequence estimation of DS/SS signal in a noncooperative context. The MLE of the PN code is a combinatorial optimisation that has the exponential computation complexity.(2) A PN code sequence estimation of DS/SS signal using tabu search (named MLtabu) is presented. The method takes advantage of the global optimisation property to solve this NP problem. The simulation results show that a good estimation is obtained even with lower SNR such as 18dB. The method doesn't need the prior knowledge about the transmitter except the duration of the PN code sequence. The estimator has the polynomial computation complexity. The performance of the presented method is superior to the eigenanalysis method for the small number of temporal sampled windows.(3) A PN code sequence estimation of DS/SS signal based on the improved...  Keywords/Search Tags:  direct sequence spread spectrum (DS/SS) signal, pseudo noise (PN) code sequence, direction of arrivals (DOA), global optimisation, tabu search, genetic algorithm, chaos optimization algorithm(COA), subspace iteration, QR decomposition  PDF Full Text Request  Related items 
 
