Frequency spreading wave blind estimation method based on odd value analysis

Abstract

The invention discloses a spread spectrum waveform blind estimation method based on singular value analysis, wherein, firstly, segmented signal observation matrixes are established by utilization of a plurality of received signals of a spread spectrum period; secondly, a spread spectrum waveform and an information sequence in each segment of data are estimated through singular value decomposition; thirdly, connection of various segments of spread spectrum waveforms is finally completed. The spread spectrum waveform blind estimation method based on singular value analysis well meets the demandon quick extraction of the spread spectrum waveform of an unknown spread spectrum signal in noncooperative communication and software radio, greatly improves the estimation speed of the spread spectrum waveform and can be suitable for two kinds of direct spread system signals which are relatively short code spread spectrum and long code spread spectrum. The invention can be directly applied to a noncooperative spread spectrum communication system and also can be used to corresponding systems like software radio and so on.

Description

technical field [0001] The invention belongs to the non-cooperative communication signal processing technology in the field of signal processing, and specifically refers to a blind estimation method of spread spectrum waveform based on singular value analysis. Background technique [0002] The spread spectrum waveform is the key parameter for the direct sequence spread spectrum signal to achieve spectrum expansion, and then obtain many excellent characteristics such as low probability of interception and anti-narrowband interference. It is also a key parameter for realizing multiple access communication in the code division multiple access communication system (CDMA) one. In the processing of non-cooperative direct-sequence spread spectrum communication signals, obtaining spread spectrum waveforms is an indispensable and important link. Otherwise, it will be difficult to demodulate the spread spectrum signal, and it will be impossible to obtain communication information. T...

AI Technical Summary

Problems solved by technology

The BM algorithm can use a small number of baseband signal samples to estimate the linear shift register sequence by solving the equation system, but this method cannot adapt to the low signal-to-noise ratio environment, and cannot estimate the nonlinear sequence; the third-order correlation algorithm uses the three The order correlation characteristics can estimate longer m-sequences, but the current research is limited to m-sequences; the performance of the MLE tabu search algorithm is slightly better than the blind estimation algorithm based on eigenvalue analysis, but it also cannot adapt to long-code spread spectrum signals. At the same time, the amount of calculation is relatively large

Moreover, the above methods and technologies are all based on the code sequence after signal demodulation. However, in the actual system, due to the weak power of the spread spectrum signal and the low signal-to-noise ratio, it is difficult to obtain ideal demodulated data.

[0005] It can be seen that the existing blind estimation method of spread spectrum waveform can not meet the needs of the actual system

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