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

This paper presents a sensor fusion method for the Ultra-Tightly Coupled (UTC) Global Positioning System (GPS)/Inertial Navigation System (INS) integrated navigation. The UTC structure, also known as the deep integration, exhibits many advantages, e.g., disturbance and multipath rejection capability, improved tracking capability for dynamic scenarios and weak signals, and reduction of acquisition time. This architecture involves the integration of I (in-phase) and Q (quadrature) components from the correlator of a GPS receiver with the INS data. The Particle Filter (PF) exhibits superior performance as compared to an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) in state estimation for the nonlinear, non-Gaussian system. To handle the problem of heavy-tailed probability distribution, one of the strategies is to incorporate the UKF into the PF as the proposal distribution, leading to the Unscented Particle Filter (UPF). The combination of an adaptive UPF and Fuzzy Logic Adaptive System (FLAS) is adopted for reducing the number of particles with sufficiently good results. The GPS tracking loops may lose lock due to the signals being weak, subjected to excessive dynamics or completely blocked. One of the principal advantages of the UTC structure is that a Doppler frequency derived from the INS is integrated with the tracking loops to improve the receiver tracking capability. The Doppler frequency shift is calculated and fed to the GPS tracking loops for elimination of the effect of stochastic errors caused by the Doppler frequency. In this paper, several nonlinear filtering approaches, including EKF, UKF, UPF and ‘FLAS assisted UPF’ (FUPF), are adopted for performance comparison for ultra-tight integration of GPS and INS. It is assumed that no outage occurs such that the inertial sensor errors can be properly corrected and accordingly the aiding information is working well. Two examples are provided for performance assessment for the various data fusion methods. The FUPF algorithm with Doppler velocity aiding demonstrates remarkable improvement, especially in the high dynamic environments, in navigation estimation accuracy with reduction of number of particles.

Download Full-text

Complexity reduction of the Kalman filter-based tracking loops in GNSS receivers

GPS Solutions ◽

10.1007/s10291-016-0557-6 ◽

2016 ◽

Vol 21 (2) ◽

pp. 685-699 ◽

Cited By ~ 7

Author(s):

Xinhua Tang ◽

Gianluca Falco ◽

Emanuela Falletti ◽

Letizia Lo Presti

Keyword(s):

Kalman Filter ◽

Complexity Reduction ◽

Tracking Loops ◽

Gnss Receivers

Download Full-text

The Implementation and Comparison Between Kalman Filter-based and Vector Tracking Loops

2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) ◽

10.1109/icsmd50554.2020.9261648 ◽

2020 ◽

Author(s):

Zhe Yan ◽

Xiyuan Chen ◽

Xinhua Tang ◽

Xuefen Zhu

Keyword(s):

Kalman Filter ◽

Vector Tracking ◽

Tracking Loops

Download Full-text

A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals

Sensors ◽

10.3390/s19061369 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1369 ◽

Cited By ~ 1

Author(s):

Yan Cheng ◽

Qing Chang ◽

Hao Wang ◽

Xianxu Li

Keyword(s):

Kalman Filter ◽

Satellite System ◽

Convergence Time ◽

Integration Time ◽

Two Stage ◽

Navigation Data ◽

Coherent Integration ◽

Tracking Loops ◽

Coherent Integration Time ◽

Global Navigation Satellite

For global navigation satellite system receivers, Kalman filter (KF)-based tracking loops show remarkable advantages in terms of tracking sensitivity and robustness compared with conventional tracking loops. However, to improve the tracking sensitivity further, increasing the coherent integration time is necessary, but it is typically limited by the navigation data bit sign transition. Moreover, for standard KF-based tracking receivers, the KF parameters are initialized by the acquired results. However, especially under weak signal conditions, the acquired results have frequency errors that are too large for KF-based tracking to converge rapidly to a steady state. To solve these problems, a two-stage KF-based tracking architecture is proposed to track weaker signals and achieve faster convergence. In the first stage, coarse tracking refines the acquired results and achieves bit synchronization. Then, in the second stage, fine tracking initializes the KF-based tracking by using the coarse tracking results and extends the coherent integration time without the bit sign transition limitation. This architecture not only utilizes the self-tuning technique of the KF to improve the tracking sensitivity, but also adopts the two-stage to reduce the convergence time of the KF-based tracking. Simulation results demonstrate that the proposed method outperforms conventional tracking techniques in terms of tracking sensitivity. Furthermore, the proposed method is compared with the standard KF-based tracking approach, proving that the proposed method converges more rapidly.

Download Full-text

Multibody-Based Input and State Observers Using Adaptive Extended Kalman Filter

Sensors ◽

10.3390/s21155241 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5241

Author(s):

Antonio J. Rodríguez ◽

Emilio Sanjurjo ◽

Roland Pastorino ◽

Miguel Á. Naya

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Adaptive Methods ◽

Adaptive Method ◽

Force Estimation ◽

Adaptive Techniques ◽

State Observers ◽

State Estimators ◽

Noise Covariance ◽

Adaptive Extended Kalman Filter

The aim of this work is to explore the suitability of adaptive methods for state estimators based on multibody dynamics, which present severe non-linearities. The performance of a Kalman filter relies on the knowledge of the noise covariance matrices, which are difficult to obtain. This challenge can be overcome by the use of adaptive techniques. Based on an error-extended Kalman filter with force estimation (errorEKF-FE), the adaptive method known as maximum likelihood is adjusted to fulfill the multibody requirements. This new filter is called adaptive error-extended Kalman filter (AerrorEKF-FE). In order to present a general approach, the method is tested on two different mechanisms in a simulation environment. In addition, different sensor configurations are also studied. Results show that, in spite of the maneuver conditions and initial statistics, the AerrorEKF-FE provides estimations with accuracy and robustness. The AerrorEKF-FE proves that adaptive techniques can be applied to multibody-based state estimators, increasing, therefore, their fields of application.

Download Full-text