Analysis of Differential Acquisition Methods by Using Monte-Carlo
Simulations
Proc. of the 18th ION GNSS Conf., (2005)Abstract
The signal acquisition is used to estimate the received code delay
and Doppler frequency based on the output of RF receiver front end.
Current GPS signals are detected with standard method which causes
C/No loss due to squaring in-phase and quadrature components of the
complex baseband signal. Squaring loss can be decreased significantly
if the receiver coherently processes the signal over a longer duration.
However, in this manner there are some additional requirements such
as navigation message of the satellite is required and the computational
burden is much higher. In this paper, a more advanced method, differential
correlation method, is studied that gives sensitivity gain over standard
method. The performance of acquisition can be stated in terms of
random processes; therefore probability density functions (pdf) are
considered. The aim of the proposed paper is to present Monte Carlo
simulations and to estimate the pdf for GPS/Galileo signals instead
of calculating pdf directly in order to decrease the complexity of
the calculations. In this paper, we limited our discussion with GPS
L1 and Galileo L1 signals. It is also assumed to acquire GPS/Galileo
signal from a single satellite with no multipath component and no
intra-system interference from other satellites. Additionally, in
our simulations we used post-correlated data as an input signal.
We consider complex white Gaussian noise (WGN) for thermal noise
effect and analyse the differential correlations method with complex
multiplication including Doppler frequency shift. The parameters
considered during simulations are probability of false alarm, probability
of detection, coherent integration time, number of non-coherent integrations
and carrier to noise ratio (C/N0). We investigated probability of
detection for a certain range of C/No with different sensitivity
(standard - 40 dBHz, medium - 35 dBHz and implementation for software
receiver) models and finally compare the results of proposed acquisition
algorithms with the standard acquisition algorithm.