scholarly journals In-Flight Alignment Algorithm Based on ADD2 for Airborne POS

2012 ◽  
Vol 66 (2) ◽  
pp. 209-225 ◽  
Author(s):  
Jiancheng Fang ◽  
Xiaoying Han
Keyword(s):  
Global Positioning System ◽  
Adaptive Filtering ◽  
Integrated System ◽  
Alignment Accuracy ◽  
Alignment Algorithm ◽  
Initial Alignment ◽  
Gps Measurement ◽  
Global Positioning ◽  
Difference Filter ◽  
Divided Difference Filter

The Position and Orientation System (POS) is a special Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) integrated system, widely employed in airborne remote sensing. In-Flight Alignment (IFA) is an effective way to improve the accuracy and speed of initial alignment for an airborne POS. IFA is normally accomplished with references from the position and velocity of GPS for SINS, so that unstable GPS measurements will result in poor alignment accuracy. To improve alignment accuracy under unstable GPS conditions, an adaptive filtering algorithm of the Second-order Divided Difference filter (DD2) based on adaptive innovation estimation is proposed, which introduces calculated innovation covariance directly into computation of the filter gain matrix. Then, the adaptive DD2 algorithm is used for the IFA of the POS with a large initial heading error. To validate the proposed algorithm, simulations are undertaken, followed by IFA experiments for the prototype of the airborne POS (TX-F30) under a turning manoeuvre in a car-mounted experiment, and under an “8” manoeuvre in-flight. The simulations and experimental results show that the proposed algorithm can reach better alignment accuracy under unknown statistical characteristic of GPS measurement noises.

scholarly journals An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yuming Chen ◽  
Wei Li ◽  
Gaifang Xin ◽  
Hai Yang ◽  
Ting Xia
Keyword(s):  
Kalman Filter ◽  
Adaptive Filtering ◽  
Alignment Algorithm ◽  
Forgetting Factor ◽  
Initial Alignment ◽  
Simulation And Experiment ◽  
Noise Covariance ◽  
Kalman Filter Algorithm ◽  
Fading Factor ◽  
Sequential Filter

The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm.

scholarly journals Advanced Anti-Spoofing Methods in Tracking Loop

2016 ◽  
Vol 69 (4) ◽  
pp. 883-904 ◽  
Author(s):  
M. R. Mosavi ◽  
Z. Nasrpooya ◽  
M. Moazedi
Keyword(s):  
Quality Assurance ◽  
Correlation Function ◽  
High Resolution ◽  
Global Positioning System ◽  
Adaptive Filtering ◽  
Positioning System ◽  
Tracking Loop ◽  
Gps Receivers ◽  
Global Positioning ◽  
Interference Elimination

The Global Positioning System (GPS) has become widespread in many civilian applications. GPS signals are vulnerable to interference and even low-power interference can easily spoof GPS receivers. In this paper, two techniques are proposed based on correlators and adaptive filtering to diminish the effect of spoofing on GPS-based positioning. The suggested algorithms are implemented in the tracking loop of the receiver. As a first method, a high-resolution correlator is utilised to avoid big parts of the influence of interference. To improve the results, a multicorrelator technique is also employed. In the second method, an adaptive filter is used for estimating the parameters of authentic plus spoof signals. Interference elimination is performed by subtracting the estimated conflict effects from the measured correlation function. These techniques provide easy-to-implement quality assurance tools for anti-spoofing. As a primary step, in this article, the proposed algorithms have been implemented in a Software Receiver (SR) to prove the concept of idea in multipath-free environments.

Structures oscillations monitoring with global positioning system and adaptive filtering techniques

2010 ◽  
Vol 28 (3) ◽  
pp. 197-206
Author(s):  
Ana Paula C. Larocca ◽  
Ricardo Ernesto Schaal ◽  
Edvaldo Simões da Fonseca
Keyword(s):  
Global Positioning System ◽  
Adaptive Filtering ◽  
Positioning System ◽  
Global Positioning ◽  
Filtering Techniques

A Novel Dynamic Approach of Accelerometer Calibration in GPS/DR Integrated Navigation System

2012 ◽  
Vol 241-244 ◽  
pp. 927-933
Author(s):  
Yan Ran Wang ◽  
Hai Zhang ◽  
Qi Fan Zhou
Keyword(s):  
Global Positioning System ◽  
Dead Reckoning ◽  
Integrated System ◽  
Integrated Navigation ◽  
Dynamic Approach ◽  
Integrated Navigation System ◽  
Global Positioning ◽  
Calibration Algorithm ◽  
The Cost ◽  
Accumulated Error

In this paper, a novel GPS (Global Positioning System) and DR (Dead Reckoning) system which is based on the accelerometer and gyroscope integrated system is designed and implemented, a new accelerometer calibration algorithm is also proposed. In this system, the odometer used in traditional DR system is replaced by a MEMS tri-axis accelerometer in order to decrease the cost and the volume of the system. The system is integrated by the Kalman filter and a new mathematical model is introduced. In the purpose of reducing the effect of the accumulated error caused by drift and bias of accelerometer, the accelerometer is calibrated online when GPS performs well. In this way, the integrated system does not only could obtain the high-precision positioning in real time, but also performs stably in practice.

scholarly journals An Innovative High-Precision Scheme for a GPS/MEMS-SINS Ultra-Tight Integrated System

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2291
Author(s):  
Qunsheng Li ◽  
Yan Zhao
Keyword(s):  
High Precision ◽  
Carrier Phase ◽  
Integrated System ◽  
Integration Scheme ◽  
Tracking Loop ◽  
Phase Measurements ◽  
Gps Measurement ◽  
Global Positioning ◽  
Navigation Accuracy ◽  
Tight Integration

The Doppler-assisted error provided by a low-precision microelectromechanical system (MEMS) strapdown inertial navigation system (SINS) increases rapidly. Therefore, the bandwidth of the tracking loop for a global positioning system (GPS)/MEMS-SINS ultra-tight integration system is too narrow to track Doppler shift. GPS measurement error is correlated with the MEMS-SINS velocity error when the Doppler-assisted error exists, leading to tracking loop lock loss. The estimated precision of the integrated Kalman filter (IKF) also decreases. Even the integrated system becomes unstable. To solve this problem, an innovative GPS/MEMS-SINS ultra-tight integration scheme based on using high-precision carrier phase measurements as the IKF measurements is proposed in this study. By assisting the tracking loop with time-differenced carrier phase (TDCP) velocity, the carrier loop noise bandwidth and code correlator spacing are reduced. The tracking accuracies of the carrier and code are increased. The navigation accuracy of GPS/MEMS-SINS ultra-tight integration is further improved.

scholarly journals GA-SVR and Pseudo-position-aided GPS/INS Integration during GPS Outage

2015 ◽  
Vol 68 (4) ◽  
pp. 678-696 ◽  
Author(s):  
Xinglong Tan ◽  
Jian Wang ◽  
Shuanggen Jin ◽  
Xiaolin Meng
Keyword(s):  
Global Positioning System ◽  
Adaptive Filtering ◽  
High Speed ◽  
Angular Rate ◽  
Mapping Function ◽  
Integration Scheme ◽  
Support Vector ◽  
Integrated Navigation ◽  
Straight Line ◽  
Global Positioning

The performance of Global Positioning System and Inertial Navigation System (GPS/INS) integrated navigation is reduced when GPS is blocked. This paper proposes an algorithm to overcome the condition where GPS is unavailable. Together with a parameter-optimised Genetic Algorithm (GA), a Support Vector Regression (SVR) algorithm is used to construct the mapping function between the specific force, angular rate increments of INS measurements and the increments of the GPS position. During GPS outages, the real-time pseudo-GPS position is predicted with the mapping function, and the corresponding covariance matrix is estimated by an improved adaptive filtering algorithm. A GPS/INS integration scheme is demonstrated where the vehicle travels along a straight line and around a curve, with respect to both low-speed-stable and high-speed-unstable navigation platforms. The results show that the proposed algorithm provides a better performance when GPS is unavailable.

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