Sequential Bayesian detection of GNSS signals

Detecting and processing global navigation satellite system (GNSS) signals indoors and in urban canyons has gained immense attention due to the problems of very weak signals and hostile environments. Satellite navigation signal detection problem applies a statistical test in which a signal coming from a specific satellite is declared present or absent. This paper considers a new approach to the detection of weak GNSS signals using a Bayesian technique. The proposed detection method selects some search space cells as candidate cells where each candidate cell is associated with a code delay and a Doppler frequency. For each candidate cell a vector of a posteriori probabilities is propagated for a fixed number of operation cycles. At the end of the process, maximum a posteriori probability (MAP) criterion is used to select the correct cell. Simulation results are presented indicating that the proposed method provides a significant performance advantage over classical techniques of acquisition which utilize non-coherent integrations.