Research On Detection Method Of Direct-Sequence Spread Spectrum Signals

Spread Spectrum communication technology has good secrecy, agile channel distribution ability, strong anti-interference, anti-multipath ability, so it is widely used in personal communication network, WLAN, 3G, satellite communication and military tactics communication. Therefore, corresponding DSSS electronic counter technique becomes a big problem to solve in electronic warfare domain. Spread Spectrum anti-jamming include blind detection, blind parameter estimation and blind estimation ect. People have great interest in DS-SS detection that is damaged by strong gauss noise and have developed the research work with high passion. To meet future demand of electronic reconnaissance techniques, it is of great significance to study detection methods for DSSS signals under low SNRs.At first, Spread Spectrum communication is adopted as a means of anti-interference. As a bran-new communication system, Spread Spread communication has characteristic of Pseudo-Random, DS signals are always transmitted in low Signal-to-Noise environment. No matter what homothetic between DS signals and noise, DS signals are different from noise affirmatively. So the counterplot of DS signals make full use of their difference in such field as time field and frequency field to distinguish DS signals from other communication signals and noise. This is the main content to study these methods.In the time field, the paper mainly studied Delay Correlation Detection, High-order Statistics-based Detection and Duffing Oscillator Detection System. The methods have the suppression ability and immunity for noise, so they can detect the DSSS signal and estimate parameter. The paper presented time-domain joint detection method; In the frequency field, the paper mainly studied Cyclic Spectrum Detection, Reprocessing of Power Spectrum Detection and Cepstrum Detection. reference to the optimization of power spectrum calculation, the paper has effectively improved the cepstrum detection; For the data processing, we used the cyclic superposition method and local statistical variance method to improve detection effect. All the methods mentioned in the paper were computer simulated, analyzed and compared. In the low SNRs, it is proved that the new methods presented can enhance the detection performance, and obtain excellent effect.