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

2020 ◽  
Author(s):  
Zhe Yan ◽  
Xiyuan Chen ◽  
Xinhua Tang ◽  
Xuefen Zhu
Keyword(s):  
Kalman Filter ◽  
Vector Tracking ◽  
Tracking Loops

New LQG based Vector Tracking Algorithm using Additional Tracking Loops for High Disturbances Environment

2017 ◽  
Author(s):  
Minhuck Park ◽  
Sanghoon Jeon ◽  
Beomju Shin ◽  
Heekwon No ◽  
Changdon Kee ◽  
...  
Keyword(s):  
Tracking Algorithm ◽  
Vector Tracking ◽  
Tracking Loops

Implementation and Performance Assessment of a Vector Tracking Method Based on a Software GPS Receiver

2011 ◽  
Vol 64 (S1) ◽  
pp. S151-S161 ◽  
Author(s):  
Sihao Zhao ◽  
Mingquan Lu ◽  
Zhenming Feng
Keyword(s):  
Global Positioning System ◽  
Carrier Frequency ◽  
Gps Receiver ◽  
Tracking Loop ◽  
Frequency Tracking ◽  
Tracking Method ◽  
Global Positioning ◽  
Vector Tracking ◽  
Tracking Loops ◽  
And Performance

A number of methods have been developed to enhance the robustness of Global Positioning System (GPS) receivers when there are a limited number of visible satellites. Vector tracking is one of them. It utilizes information from all channels to aid the processing of individual channels to generate receiver positions and velocities. This paper analyzes relationships among code phase, carrier frequency, and receiver position and velocity, and presents a vector loop-tracking algorithm using an Extended Kalman filter implemented in a Matlab-based GPS software receiver. Simulated GPS signals are generated to test the proposed vector tracking method. The results show that when some of the satellites are blocked, the vector tracking loop provides better carrier frequency tracking results for the blocked signals and produces more accurate navigation solutions compared with traditional scalar tracking loops.

Increasing the Resistance of GPS Receivers by Using a Fuzzy Smart Estimator in Weak Signal Conditions

2020 ◽  
Vol 73 (5) ◽  
pp. 991-1013
Author(s):  
M. A. Farhad ◽  
M. R. Mosavi ◽  
A. A. Abedi ◽  
K. Mohammadi
Keyword(s):  
Kalman Filter ◽  
Simulated Data ◽  
Real Data ◽  
Navigation Systems ◽  
Weak Signal ◽  
Signal Tracking ◽  
Advantages And Disadvantages ◽  
Vector Tracking ◽  
Gnss Receivers ◽  
Satellite Navigation Systems

Global satellite navigation systems (GNSS) are nowadays used in many applications. GNSS receivers experience limitations in receiving weak signals in a degraded environment. Hence, tracking weak GNSS signals is a topic of interest to researchers in this field. Different methods have been proposed to address this issue, each of which has advantages and disadvantages. In this paper, a method based on the vector tracking method is proposed for weak signal tracking. This method has been developed based on a strong Kalman filter instead of the extended Kalman filter used in conventional vector tracking methods. In order to adjust important parameters of this filter, the fuzzy method is used. The results of tests performed with both simulated data and real data demonstrate that the proposed method performs better than previous ones in weak signal tracking.

scholarly journals Kalman Filter-Based RAIM for Reliable Aircraft Positioning with GPS and NavIC Constellations

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6606
Author(s):  
Susmita Bhattacharyya ◽  
Dinesh Mute
Keyword(s):  
Kalman Filter ◽  
Weighted Least Squares ◽  
Unmanned Aircraft ◽  
Global Navigation Satellite Systems ◽  
Navigation Systems ◽  
Satellite Systems ◽  
Vector Tracking ◽  
Receiver Autonomous Integrity Monitoring ◽  
Global Navigation Satellite ◽  
Computational Resources

This paper presents a novel Kalman filter (KF)-based receiver autonomous integrity monitoring (RAIM) algorithm for reliable aircraft positioning with global navigation satellite systems (GNSS). The presented method overcomes major limitations of the authors’ previous work, and uses two GNSS, namely, Navigation with Indian Constellation (NavIC) of India and the Global Positioning System (GPS). The algorithm is developed in the range domain and compared with two existing approaches—one each for the weighted least squares navigation filter and KF. Extensive simulations were carried out for an unmanned aircraft flight path over the Indian sub-continent for validation of the new approach. Although both existing methods outperform the new one, the work is significant for the following reasons. KF is an integral part of advanced navigation systems that can address frequent loss of GNSS signals (e.g., vector tracking and multi-sensor integration). Developing KF RAIM algorithms is essential to ensuring their reliability. KF solution separation (or position domain) RAIM offers good performance at the cost of high computational load. Presented range domain KF RAIM, on the other hand, offers satisfactory performance to a certain extent, eliminating a major issue of growing position error bounds over time. It requires moderate computational resources, and hence, shows promise for real-time implementations in avionics. Simulation results also indicate that addition of NavIC alongside GPS can substantially improve RAIM performance, particularly in poor geometries.

Study on Vector Tracking Loop Based on Extended Kalman Filter

2014 ◽  
Vol 35 (6) ◽  
pp. 1400-1405 ◽  
Author(s):  
Yu Luo ◽  
Yong-qing Wang ◽  
Hai-kun Luo ◽  
Yuan-xing Ma ◽  
Si-liang Wu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Tracking Loop ◽  
Vector Tracking

scholarly journals A Novel Design for the Ultra-Tightly Coupled GPS/INS Navigation System

2012 ◽  
Vol 65 (4) ◽  
pp. 717-747 ◽  
Author(s):  
Dah-Jing Jwo ◽  
Chi-Fan Yang ◽  
Chih-Hsun Chuang ◽  
Kun-Chieh Lin
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
Doppler Frequency ◽  
Superior Performance ◽  
Gps Tracking ◽  
Doppler Velocity ◽  
Tightly Coupled ◽  
Tracking Loops ◽  
Number Of Particles

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.

scholarly journals Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1844
Author(s):  
Junren Sun ◽  
Zun Niu ◽  
Bocheng Zhu
Keyword(s):  
Fault Detection ◽  
Navigation System ◽  
Satellite System ◽  
Navigation Satellite ◽  
Vector Tracking ◽  
Tightly Coupled ◽  
Tracking Loops ◽  
Deep Integration ◽  
Navigation Service ◽  
Global Navigation Satellite

The Inertial Navigation System (INS) is often fused with the Global Navigation Satellite System (GNSS) to provide more robust and superior navigation service, especially in degraded signal environments. Compared with loosely and tightly coupled architectures, the Deep Integration (DI) architecture has better tracking and positioning performance. Information is shared among channels, and the assistant information from INS helps to reduce the dynamic stress of tracking loops. However, this vector tracking architecture may result in easy propagation of errors among tracking channels. To solve this problem, a Fault Detection and Exclusion (FDE) method for the deeply integrated BeiDou Navigation Satellite System (BDS)/INS navigation system is proposed in this paper. This method utilizes pre-filters’ outputs and integration filter’s estimations to form test statistics. These statistics can help to detect and exclude both step errors and Slowly Growing Errors (SGEs) correctly. The monitoring capability of the method was verified by a simulation which was based on a software receiver. The simulation results show that the proposed FDE method works effectively. Additionally, the method is convenient to be implemented in real-time applications because of its simplicity.

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