Adaptive Techniques in Scalar Tracking Loops with Direct-State Kalman-Filter

This paper evaluates the implementation of an adaptive technique for direct-state Kalman-filter (DSKF)-based scalar tracking loops used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve the minimum mean square error (MMSE). In time-varying scenarios, the measurements’ distribution changes over time due to noise, signal dynamics, multipath, and non-line-of-sight effects. In this kind of scenarios, it is not easy to find a suitable model, and the DSKF tends to be a suboptimal solution. This study introduces a method to adapt the noise covariances of the DSKF by using the loop-bandwidth control algorithm (LBCA). The LBCA adapts the loop bandwidth of the DSKF based on the statistics of the tracking channel. The presented technique is compared with the Cramér-Rao bound (CRB)-based DSKF, which adjusts the measurement noise covariance depending on the CRB. These two adaptive DSKFs are compared with the LBCA-based standard scalar tracking loop (STL). The LBCA-based DSKF, the CRB-based DSKF, and the LBCA-based standard STL are implemented in an open software interface GNSS hardware receiver. For each implementation, the receiver is evaluated in simulated scenarios with different dynamics and noise cases. The results confirm that the LBCA-based DSKF exhibits superior dynamic tracking performance than the CRB-based DSKF. Moreover, the LBCA-based standard STL still shows the best dynamic tracking performance, while having the lowest complexity