Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter

Abstract

In the scenario of high dynamics and low C/N0, the discriminator output of a GNSS tracking loop is noisy and nonlinear. The traditional method uses a fixed-gain loop filter for error estimation, which is prone to lose lock and causes inaccurate navigation and positioning. This paper proposes a cascaded adaptive vector tracking method based on the KF+EKF architecture through the GNSS Software defined receiver in the signal tracking module and the navigation solution module. The linear relationships between the pseudo-range error and the code phase error, the pseudo-range rate error and the carrier frequency error are obtained as the measurement, and the navigation filter estimation is performed. The signal C/N0 ratio and innovation sequence are used to adjust the measurement noise covariance matrix and the process noise covariance matrix, respectively. Then, the estimated error value is used to correct the navigation parameters and fed back to the local code/carrier NCO. The field vehicle test results show that, in the case of sufficient satellite signals, the positioning error of the proposed method has a slight advantage compared with the traditional method. When there is signal occlusion or interference, the traditional method cannot achieve accurate positioning. However, the proposed method can maintain the same accuracy for the positioning results.

A MCKF-based cascade vector tracking method designed for ship navigation

“…The code phase error δτ (chips), carrier phase error δθ (rad), carrier frequency error δf (Hz) and carrier frequency rate error δ ˙f (Hz s −1 ) form the state vector of the proposed VTL method. The discretized state vector and the corresponding measurement equation are as follows [18]: (7) where i indicates the satellite numbering. A total of m satellites are involved in the positioning.…”

“…T 0 is the coherent integration time and it is 20 ms. C/N 0 is the CNR of the corresponding channel and it varies with the environment. The method of CNR estimation is based on the variance sum method [2,18]. C/N 0 is estimated as follows:…”

“…Many scholars have designed cascade noncoherent algorithms for the carrier tracking loop of GNSS receivers, including three-state KF-based pre-filter [16] and four-state KF-based pre-filter [17] architectures, to improve the carrier-phase tracking performance of GNSS receivers under high dynamics. The code and carrier tracking loops have also been combined to construct a four-state pre-filter architecture for the code and carrier loops [18]. However, GNSS signals have large outliers and non-Gaussian noise in occluded environments [19].…”

Suppression of Jammer Multipath in GNSS Antenna Array Receiver

“…When the GPS modern plan is completed, the L1 band will contain three kinds of signals, which are C/A code, P(Y) code, and M code signal, respectively. The bandwidths of the three signals are 2.046 MHz, 20.46 MHz, and 30.69 MHz, separately, so the maximum interference bandwidth is 2.046 MHz, 20.46 MHz, and 30.69 MHz [30][31][32]. This simulation considers that the interference bandwidth is the maximum.…”

Optimal Order of Time-Domain Adaptive Filter for Anti-Jamming Navigation Receiver

“…It takes advantage of the difference between the signal and interference in the frequency spectrum and designs a filter to filter out the interference signal in the time domain [7] to ensure the normal ranging and positioning function [8]. With the development of digital signal processing in navigation applications, navigation receivers often face different hardware problems such as speed, design scale, and power consumption during digital signal processing, which put forward high requirements of filter complexity [9]. However, the main parameter affecting the filter complexity is the filter length [10], which heavily relies on engineering practices instead of being adjusted according to the actual interference suppression requirements.…”