scholarly journals Multi-Target Localization and Tracking Using TDOA and AOA Measurements Based on Gibbs-GLMB Filtering

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5437
Author(s):  
Zhengwang Tian ◽  
Weifeng Liu ◽  
Xinfeng Ru
Keyword(s):  
Cross Correlation ◽  
Measurement Data ◽  
Angle Of Arrival ◽  
Simulation Experiments ◽  
Acoustic Localization ◽  
Cluttered Environment ◽  
Detection And Tracking ◽  
Correlation Algorithm ◽  
Acoustic Array ◽  
Localization And Tracking

This paper deals with mobile multi-target detection and tracking. In the traditional method, there are uncertainties such as misdetection and false alarm in the measurement data, and it will be inevitable having to deal with the data association. To solve the target trajectory and state estimation problem under a cluttered environment, this paper proposes a non-concurrent multi-target acoustic localization tracking method based on the Gibbs-generalized labelled multi-Bernoulli (Gibbs-GLMB) filter and considers an acoustic array of a fixed arrangement for the tracking of targets by joint time difference of arrival (TDOA) and angle of arrival (AOA) measurements. Firstly, the TDOAs are calculated by using the generalized cross-correlation algorithm (GCC) and the AOAs are derived from the received signal directions. Secondly, we assume the independence of the targets and fuse the measurements which are used to track the multiple targets via the Gibbs-GLMB filter. Finally, the effectiveness of the method is verified by Monte Carlo simulation experiments.

scholarly journals A Single-Way Ranging Localization of AUVs Based on PSO of Outliers Elimination

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xinnan Fan ◽  
Zhongjian Wu ◽  
Jianjun Ni ◽  
Chengming Luo
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Time Of Arrival ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Simulation Experiments ◽  
Range Measurements ◽  
Acoustic Localization ◽  
Long Baseline ◽  
Key Factor

Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. In long baseline underwater acoustic localization networks, the accuracy of single-way range measurements is the key factor for the precision of localization of AUVs, whether it is based on the way of time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). The single-way range measurements do not depend on water quality and can be taken from long distances; however, there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method compared with another localization algorithm.

Application of cross-correlation algorithm in moving target range-gated three-dimensional laser active imaging

2016 ◽  
Vol 45 (6) ◽  
pp. 0617003
Author(s):  
范有臣 Fan Youchen ◽  
赵洪利 Zhao Hongli ◽  
孙华燕 Sun Huayan ◽  
郭惠超 Guo Huichao ◽  
赵延仲 Zhao Yanzhong
Keyword(s):  
Cross Correlation ◽  
Three Dimensional ◽  
Target Range ◽  
Moving Target ◽  
Correlation Algorithm ◽  
Active Imaging

scholarly journals Estimation for Motion in Tracking and Detection Objects with Kalman Filter

2020 ◽  
Author(s):  
Afef Salhi ◽  
Fahmi Ghozzi ◽  
Ahmed Fakhfakh
Keyword(s):  
Computer Vision ◽  
Kalman Filter ◽  
Visual Motion ◽  
Optimal Solution ◽  
Block Matching ◽  
Detection And Tracking ◽  
Tracking Object ◽  
Tracking Objects ◽  
Localization And Tracking ◽  
Prediction Parameters

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

scholarly journals A New Cross-Correlation Algorithm Based on Distance for Improving Localization Accuracy of Partial Discharge in Cables Lines

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4549 ◽  
Author(s):  
Xianjie Rao ◽  
Kai Zhou ◽  
Yuan Li ◽  
Guangya Zhu ◽  
Pengfei Meng
Keyword(s):  
Cross Correlation ◽  
Partial Discharge ◽  
Effective Means ◽  
Sampling Rate ◽  
Propagation Distance ◽  
Test System ◽  
Computer Simulation Model ◽  
Localization Accuracy ◽  
Correlation Algorithm ◽  
Cable Lines

Locating the partial discharge (PD) source is one of the most effective means to locate local defects in power cable lines. The sampling rate and the frequency-dependent characteristic of phase velocity have an obvious influence on localization accuracy based on the times of arrival (TOA) evaluation algorithm. In this paper, we present a cross-correlation algorithm based on propagation distance to locate the PD source in cable lines. First, we introduce the basic principle of the cross-correlation function of propagation distance. Then we verify the proposed method through a computer simulation model and investigate the influences of propagation distance, sampling rate, and noise on localization accuracy. Finally, we perform PD location experiments on two 250 m 10 kV XLPE power cables using the oscillation wave test system. The simulation and experiment results indicate that compared with traditional TOA evaluation methods, the proposed method has superior locating precision.

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