Research On Key Technologies Of Spread Spectrum Based On TDMA System In High Dynamic Scenarios

In recent years,spread spectrum communication technology has received extensive attention and research due to its advantages of strong anti-interference,low interception,and resistance to multipath interference.Signal acquisition and carrier synchronization are particularly critical,and only after signal synchronization can correct despreading be achieved.Nowadays,with the rapid development of high dynamic communication and the continuous improvement of object motion speed,the Doppler effect brought about by this cannot be ignored.Conventional spread spectrum technology has been able to perform well in anti-interference and multipath effects,but there is still less research on algorithms for acquisition synchronization during high-speed motion,and the demand for algorithms that can resist the large frequency offset caused by high dynamics is becoming increasingly strong.Therefore,starting from the traditional signal acquisition and carrier synchronization,this article studies their respective advantages and shortcomings,and then studies the signal acquisition and carrier synchronization technology under large frequency offset.The main research contents are as follows:Firstly,the principle of spread spectrum communication is introduced,and its classification,advantages and disadvantages are summarized.Secondly,the selection of spread spectrum codes and the principle of signal acquisition in spread spectrum systems are analyzed.Then,based on the direct sequence spread spectrum system,three acquisition methods,namely,time domain sliding correlation method,matched filter search method,and frequency domain parallel search method based on FFT,are briefly introduced,and their advantages and disadvantages are pointed out.Subsequently,the time-domain carrier synchronization technology is studied.First,the system model of a data assisted spread spectrum system is presented.Then,the principles of several traditional carrier synchronization technologies are theoretically derived,and their estimation performance,accuracy,and complexity are analyzed.It is concluded that the carrier synchronization of the Fitz algorithm is unbiased,and performs well in terms of estimation accuracy and computational complexity,However,due to the phase inversion problem of its autocorrelation function,the estimation range is relatively narrow.Finally,the phase inversion compensation algorithm is studied,and a phase compensation algorithm based on the maximum speed of the system is proposed,which solves the problem of narrow estimation range of Fitz algorithm.At the same time,in a certain frequency offset range under low signal-to-noise ratio,the estimation performance can still be close to the cramerow bound.Then,this paper studies the signal acquisition algorithms for DS/SS systems,introduces the frequency domain parallel algorithm based on FFT and the FFT acquisition algorithm based on partial matched filtering,and analyzes the principles,advantages and disadvantages of both.Then,an FFT acquisition algorithm based on parity two channels in large frequency offset communication systems is proposed.Simulation analysis shows that the acquisition performance of the algorithm fluctuates greatly with the change of sampling points.Therefore,based on this,a joint sequence based FFT acquisition algorithm is proposed,which sacrifices frequency offset resolution and uses a shorter spread spectrum sequence to combine two spread spectrum sequences for FFT search.The capture range can reach 50% of the chip rate,and the capture success rate has increased by 1.5d B compared to before the improvement.Finally,for the barrier effect caused by the proposed acquisition scheme,several different compensation algorithms are studied,and based on the designed acquisition scheme,a compensation algorithm based on the least square method is proposed to achieve signal acquisition and carrier synchronization under large frequency offset.Meanwhile,the normalized mean square error is reduced by one order of magnitude compared to traditional interpolation algorithms.