A High Performance Code and Carrier Tracking Architecture for Ground-Based Mobile GNSS Receivers

It is well-known that vector delay and frequency locked loops (VDFLLs) have several advantages over traditional scalar tracking loops in GNSS receivers, especially in mobile platforms subject to poor signal environments and accelerations. This paper presents an approach to further VDFLL performance improvement for ground-based mobile receivers which apparently has not yet been exploited. The performance improvement results from using the dynamics limitations of typical ground-based vehicles, long-term premeasurement integration, and the weak-signal advantages of direct maximum-likelihood (ML) estimation of position and velocity to materially improve weak-signal tracking capability. In the new VDFLL design, which will be denoted by MLVTL (Maximum Likelihood Vector Tracking Loop), a Kalman filter for the navigation processor is not required. Additionally, map aiding can be incorporated directly in the tracking loop to reduce position and velocity errors, lower the tracking threshold, and reduce the minimum number of satellites required to maintain tracking. 1. DESIGN PHILOSOPHY Figure 1 is a simplified block diagram of a scalar signal tracking method which has been used for many years in GPS receivers. Each satellite signal is independently tracked with a code delay-locked loop which enables measurement of pseudorange, and a carrier frequency-locked loop which enables measurement of pseudorange rate. The measurements are fed to a navigation processor, typically a Kalman filter or recursive least-squares estimator, which produces the navigation solution for position, velocity, and time (PVT). A simplified block diagram of a typical VDFLL tracking loop is shown in Figure 2. Predictions of position and velocity navigation states from a navigation processor (such as a Kalman filter or least-squares estimator) are converted into predictions of satellite pseudoranges and pseudorange rates, which are fed as references to code delay and carrier frequency discriminators operating on the satellite channels. These references are coupled because they all are derived from the same navigation states. The discriminators provide measurement residuals for each satellite, which are estimates of the difference between the predicted pseudoranges and pseudorange rates and the same parameters inherent in the received satellite signal. The measurement residuals are fed back to the navigation processor, thus closing the tracking loop. For legacy C/A coded GPS signals, the loop iteration time interval typically spans from 1 to 10 bits of the 50 bps navigation data stream (20-200 milliseconds). The advantages of the VDFLL in Figure 2 over the scalar tracking loop in Figure 1 accrue mainly from the coupling of the references for the code and carrier discriminators. It can be shown that this reduces tracking error due to thermal noise when the number of satellites exceeds the number of navigation states. The VDFLL is also more robust in the presence of signal dropouts and vehicle accelerations. Background information on conventional and VDFLL tracking can be found in [1-4]. However, despite the coupling of the discriminator references, it is important to note that the measurement residuals are still independently estimated for each satellite channel. This exposes a vulnerability when operating with the weak signals often encountered in mobile operation. It is well known that the measurement error from an individual discriminator increases very rapidly as the C/No drops below the level required for the error to approach the Cramer-Rao lower bound. Inevitably, this leads to outlier or “wild” measurements which disruptively propagate through the navigation processor. Since each discriminator operates in the presence of its own independent noise, there is no opportunity to increase the processing gain at this point in the loop and lower the tracking threshold by using joint signal characteristics. To solve these problems, the new MLVTL architecture shown in Figure 3 has been developed. It does not use discriminators to provide the usual measurement residuals to the navigation processor. Instead, direct maximum-likelihood (ML) estimates of the navigation states are performed using small simultaneously-generated segments of the code correlation functions and frequency spectra from all satellites. The tracking loop is closed by using the estimated navigation states to update the centers of the segments of the code correlation functions and frequency spectra for the next ML estimate. Unlike a typical VDFLL Kalman filter, the ML estimates are repeated at a relatively slow rate (from 1 to 3 seconds apart) to permit a large amount of pre-estimation processing gain, as well as sufficient time for the ML computations. Also unlike a Kalman filter, the ML estimator is not recursive. Each estimate uses enough signal data to permit good weak-signal performance on its own. There are other advantages. Because frequency discriminators are not used, their limited operating range (typically ± 25 Hz) is no longer a limitation on how much platform acceleration can be tolerated without loss of frequency lock, especially with weak signals. The new MLVTL design exploits the fact that in typical land-based mobile operation large accelerations (a significant fraction of 1 g) are infrequent and when they do occur, they are sustained for no more than a few seconds. Acceleration is therefore not modeled in the ML estimator. If an ML estimate of position and velocity is degraded by a large acceleration, essentially complete recovery is possible at the next estimate. Gone is the problem of making a Kalman filter properly responsive to changes in acceleration without adding more states or fiddling with its covariance matrix. Smaller accelerations simply cause a momentary small loss of sensitivity and non-noticeable position and velocity errors which do not propagate forward in time. The new MLVTL architecture also uses altitude aiding to reduce the position and velocity dimensionality, which results in better tracking accuracy and a lower tracking threshold. Memory size for stored altitude data can be much smaller than horizontal aiding data, because altitude generally varies much more slowly than horizontal position. 2. DETAILED DESCRIPTION The ML Estimator In the new MLVTL, the ML estimator jointly estimates receiver position, velocity, GPS time error, and GPS time rate error. GPS time is generated within the receiver by a process that will be described later. It can be shown that the ML estimate is equivalent to a least-squares estimate which minimizes the integral