# An Optimized Vector Tracking Architecture for Pseudo-Random Pulsing CDMA Signals
###### Abstract
The VTL algorithm can track weak signals, which increases the number of visible satellites and, thus, has better performance in harsh environments [29]. Furthermore, the VTL algorithm utilizes other normal operation tracking channels to predict and compensate for the observations of monetary outage signals to maintain continuous tracking [30]. The typical types of VTL include vector delay lock loop (VDLL) [30; 31; 32], vector frequency lock loop (VFLL) [31; 32], vector delay frequency lock loop (VDFLL) [33; 34; 32], and vector phase lock loop (VPLL) [35]. [PERSON] et al. propose a VTL algorithm applicable to asynchronous PLPS, significantly mitigating the near-far problem [36]. However, its VTL is still based on the continuous GNSS signal model, ignoring the pseudo-random pulsing characteristic of pseudolite signals. The measurement inputs of the navigation filter in traditional VTL are discriminator outputs in each channel, and the navigation filter obtains the errors at similar sampling time. Nevertheless, this structure is not appropriate for the vector tracking of pseudolite signals. The reasons are as follows. Firstly, the output of the discriminator can only reflect the errors before the valid timeslot due to the pulse feature of PRPS. Secondly, as the active timeslots of different pseudolites do not overlap, the output time of each channel discriminator is different. The structure based on the pre-filters and navigation filter provides a possible solution to these problems in tracking pseudolite signals because, by improving the architecture of pre-filters, the robustness of VTL would be enhanced, and its application scope could be expanded. Unfortunately, the application of pre-filter-based VTL mainly focuses on GNSSsignals. [PERSON] et al. present a federal tracking loop consist of subfilters in every channel and a master filter [37]. By adding subfilters, the computational complexity of VTL is effectively reduced, and the robustness is also enhanced, while the tracking performance is almost unchanged. The pre-filters in the VTL proposed by [PERSON] et al. are based on the unscented Kalman filter (UKF) and directly operate on the in-phase and quadra-phase correlator outputs to obtain the phase and frequency biases [38]. However, these pre-filters are based on the continuous GNSS model, causing jitter degradation when tracking PRPS. To solve the problems in tracking PRPS, an optimized VTL architecture that includes irregular update periods (IUP) pre-filters and a navigation filter based on predicted measurements (PM) is proposed. The proposed VTL is abbreviated as IUPPM-VTL. According to the timeslot intervals, the IUP pre-filter based on the Kalman filter (KF) adjusts the update cycles, avoiding the impact of the pseudo-random pulsing signal. So, it can estimate the measurement accurately. However, since the pre-filters are updated at varying timeslots and not consistent with each other, using them directly as the inputs of the navigation filter would increase the vector tracking estimation noise. As a consequence, the IUPPM-VTL predicts the measurements so that they can be to sampled at the same time. Meanwhile, this paper compares the proposed IUPPM-VTL with traditional VTL based on pre-filter and IUP-VTL based on IUP-prefilter. The rest of this paper is organized as follows. Section 2 introduces the PRPS structure. Section 3 describes the problems in tracking PRPS. Section 4 proposes the optimized VTL architecture. The simulation and comparison testing are carried out in Section 5. Conclusions and future work are summarized in Section 6.