Application of Singer Tracking Model in Adaptive GPS Signal Tracking Algorithm

2012 ◽  
Author(s):  
Yang Jing ◽  
Yao Yuan-Fu
Keyword(s):  
Tracking Algorithm ◽  
Signal Tracking ◽  
Tracking Model

scholarly journals Research on Acquisition and Tracking Algorithm of Global Satellite Positioning Receiver Based on UWB

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Li Yang ◽  
Danshi Sun ◽  
Haote Ruan
Keyword(s):  
Influence Factor ◽  
Three Dimensional ◽  
Doppler Frequency ◽  
Pulse Generation ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Signal Tracking ◽  
Satellite Positioning ◽  
Working Principle ◽  
Satellite Signal

In order to overcome the problems of the traditional algorithm, such as the time-consuming execution of acquisition instructions, low signal tracking accuracy, and low signal capture accuracy, a global satellite positioning receiver acquisition and tracking algorithm based on UWB technology is designed in this study. On the basis of expounding the pulse generation method and working principle in UWB technology, this paper analyzes in detail the characteristics of UWB technology, such as antimultipath, low power consumption, and strong penetration. Then, on the basis of window function filtering, in the process of three-dimensional search of global satellite positioning signal, firstly, the satellite signal entering the GPS software receiver is processed by RF front-end mixing and AD sampling, and then, the signal tracking and navigation message solving are completed according to the relationship between the influence factor and Doppler frequency offset. The experimental results show that the execution time of the acquisition instruction of the proposed algorithm varies between 1129 ms and 1617 ms; the signal tracking accuracy ranges between 0.931 and 0.951, and the signal capture accuracy ranges between 93.3% and 95.6%, which proves that the proposed algorithm has achieved the design expectation.

scholarly journals Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lieping Zhang ◽  
Jinghua Nie ◽  
Shenglan Zhang ◽  
Yanlin Yu ◽  
Yong Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Particle Number ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Time Performance ◽  
Single Station ◽  
Tracking Model ◽  
Number Of Particles

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

scholarly journals Two-Step Galileo E1 CBOC Tracking Algorithm: When Reliability and Robustness Are Keys!

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Aleksandar Jovanovic ◽  
Cécile Mongrédien ◽  
Youssef Tawk ◽  
Cyril Botteron ◽  
Pierre-André Farine
Keyword(s):  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Multipath Mitigation ◽  
Binary Offset Carrier ◽  
Signal Tracking ◽  
Gps Navigation ◽  
Tracking Process ◽  
Galileo Satellite ◽  
Navigation Signals ◽  
Reliability And Robustness

The majority of 3G mobile phones have an integrated GPS chip enabling them to calculate a navigation solution. But to deliver continuous and accurate location information, the satellite tracking process has to be stable and reliable. This is still challenging, for example, in heavy multipath and non-line of sight (NLOS) environments. New families of Galileo and GPS navigation signals, such as Alternate Binary Offset Carrier (AltBOC), Composite Binary Offset Carrier (CBOC), and Time-Multiplex Binary Offset Carrier (TMBOC), will bring potential improvements in the pseudorange calculation, including more signal power, better multipath mitigation capabilities, and overall more robust navigation. However, GNSS signal tracking strategies have to be more advanced in order to profit from the enhanced properties of the new signals.In this paper, a tracking algorithm designed for Galileo E1 CBOC signal that consists of two steps, coarse and fine, with different tracking parameters in each step, is presented and analyzed with respect to tracking accuracy, sensitivity and robustness. The aim of this paper is therefore to provide a full theoretical analysis of the proposed two-step tracking algorithm for Galileo E1 CBOC signals, as well as to confirm the results through simulations as well as using real Galileo satellite data.

The effective model of transformation of ideological and political education in universities based on artificial intelligence

2021 ◽  
pp. 1-9
Author(s):  
Yu Zhang ◽  
Xuying Sun
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Bp Neural Network ◽  
Target Prediction ◽  
Tracking Algorithm ◽  
Ideological And Political Education ◽  
Political Education ◽  
Tracking Accuracy ◽  
Transmission Of Knowledge ◽  
Tracking Model

In the context of artificial intelligence, the path of knowledge transmission needs to be transformed. In essence, the transmission of knowledge and the transformation of information transmission methods are integrated. This paper studies the foreign object tracking algorithm, analyzes the error in the target tracking algorithm, and uses the BP neural network principle to modify the IMM algorithm. Aiming at the problem of low tracking accuracy when the target is maneuvering, this paper analyzes the linearization error of Kalman filter and builds a BP neural network to correct the tracking model of IMM. The model creates a target prediction training set and a test set, optimizes the parameters of the neural network, and conducts simulation experiments using MATLAB, which proved that the model had a higher accuracy in predicting the target trajectory of foreign objects. Therefore, the transformation of ideological and political teaching mode in colleges and universities can be realized, and the intelligent classroom of ideological and political education and intelligent communication have technical support.

Covariance Tracking Algorithm on Bilateral Filtering under Lie Group Structure

2014 ◽  
Vol 519-520 ◽  
pp. 684-688
Author(s):  
Ying Hong Xie ◽  
Cheng Dong Wu
Keyword(s):  
Object Tracking ◽  
Covariance Matrix ◽  
Model Updating ◽  
Group Structure ◽  
Tracking Algorithm ◽  
Bilateral Filtering ◽  
Covariance Modeling ◽  
Illumination Changes ◽  
Covariance Tracking ◽  
Tracking Model

The existing object tracking method using covariance modeling is hard to reach the desired tracking performance when the deformation of moving target and illumination changes are drastic, we proposed a object tracking algorithm based on bilateral filtering. Firstly, the algorithm deals the image to be tracked with bilateral filtering, and extracts the needed features of filtered image to construct covariance matrix as tracking model. Secondly, under log-Euclidean Riemannian metric, we construct similarity measure for object covariance matrix and model updating strategy. Extensive experiments show that the proposed method has better adaptability for object deformation and illumination changes.

Tracking algorithm of video echo signal based on big data processing

2021 ◽  
pp. 1-9
Author(s):  
Ying Chang ◽  
Qinghua Zhu
Keyword(s):  
Big Data ◽  
Feature Fusion ◽  
Rapid Development ◽  
Data File ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Echo Signal ◽  
Signal Tracking ◽  
Operation Speed ◽  
Target Data

With the rapid development of many storage devices and other science and technology, continuous discussion on the role of video target tracking technology in the practical application of photoelectric weapons, guidance systems and security tracking systems has become the current research direction of computer vision and artificial intelligence. The purpose of this study is to explore the differences and characteristics of different algorithms, and provide theoretical and methodological support for the realization of video echo signal tracking in complex environment. For echo signal tracking algorithm only uses a single feature to track, it is particularly easy to cause tracking failure. Therefore, this study uses a method of multi feature fusion to establish the observation model. From the four aspects of gray, color, shape and texture, these four visual characteristics are very representative. In order to study the tracking accuracy, stability and real-time performance of the algorithm, pedestrian, vehicle and face are used as tracking targets to verify the tracking performance of the algorithm in different environments. Using the technical analysis of big data to find the target data file can improve the search speed of the target data and the operation speed of the tracking algorithm. The experimental results show that, in terms of accuracy, the simplest gray feature is only 0.42, and CN feature is improved by about 14% compared with the gray feature. It takes less time to find the target data file by index file method than by traversing the file name method.

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