Robust time-hopping pseudolite signal acquisition method based on dynamic Bayesian network

Abstract

The time-hopping direct sequence spread spectrum (TH-DSSS) signal has been widely used in Pseudolites Positioning Systems to overcome the near-far problem. To capture the TH-DSSS signal, an additional parameter representing the time-hopping (TH) rules should be estimated in addition to the PRN code phase and carrier Doppler. However, the techniques of estimating a TH parameter in existing TH-DSSS signal acquisition methods have significant issues in poor signal quality environments. Here, we propose a robust and general TH-DSSS signal acquisition method to reduce the impact of signal degradation. In this method, we first capture every short pulse to obtain the code phase and carrier Doppler. After sufficient successful pulse acquisitions, we model the process of TH parameter acquisition as a dynamic Bayesian network. The so-called state confidence that describes the probability of each candidate TH parameter is then introduced to infer the real TH parameter. Finally, this method has been seen, both theoretically and experimentally, to be both general and effective to compensate for harsh signal environments. Simulation results show that compared with baseline algorithms, this method provides a significant improvement in detection probability and considerable reduction in acquisition time.