© The Institution of Engineering and Technology
New Galileo and enhanced GPS signals use binary offset carrier (BOC) waveforms namely composite BOC (CBOC) and alternate BOC (AltBOC). For good tracking, these signals require precise acquisition. The more sidelobes the autocorrelation function has, the tighter the pullin range region is, and the more precise the acquisition must be. Further, when multipath propagation is encountered, the received waveform is so distorted that a precise acquisition becomes difficult. To solve this problem, two approaches are considered by enhancing either the acquisition stage or the tracking one. At the acquisition stage, the authors focus on Bayesian estimation where Bernoulli–Gaussian and Laplacian (Lp) priors for channel distribution are compared. On one hand, the BG prior leads to rather complex optimisation criterion that can be solved by a Markov Chain Monte Carlo algorithm. This is a very efficient but time consuming method. On the other hand, the Lp prior results in exact maximum a posteriori calculation. In this case, the Bayesian estimation involves an ℓ_{1}norm penalisation and provides quite good performance for reasonable acquisition complexity. At the tracking stage, many works have dealt with false lock positions, such as the sidelobe cancellation method (SCM) or multiple gate delay (MGD) technique. In this article, the authors highlight the need for accurate acquisition for better detection and estimation at the tracking output. The SCM and MGD methods are taken as examples to illustrate this influence. The analysed signals are GPS BPSK(1), BPSK(10), and Galileo CBOC(6, 1, 1/11) and AltBOC(15, 10) modulated open service signals. The impact of multipath is studied for line of sight (LOS) signal, LOS plus multipath replicas, and multipath alone. The authors compare the results of classical acquisition followed by precise tracking to those of Bayesian acquisition followed by classical tracking. The results of Bayesian acquisition followed by precise tracking are also provided.
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