A Two-Step Optimizing Algorithm for TOA Real-Time Dynamic Localization in NLOS Environment

2013 ◽  
Vol 347-350 ◽  
pp. 3604-3608
Author(s):  
Shan Long ◽  
Zhe Cui ◽  
Fei Song
Keyword(s):  
Real Time ◽  
Weighted Least Squares ◽  
Line Of Sight ◽  
Wireless Localization ◽  
Localization Accuracy ◽  
Time Dynamic ◽  
Real Time Tracking ◽  
Step Algorithm ◽  
Optimizing Algorithm ◽  
Non Line Of Sight

Non-line-of-sight (NLOS) is one of the main factors that affect the ranging accuracy in wireless localization. This paper proposes a two-step optimizing algorithm for TOA real-time tracking in NLOS environment. Step one, use weighted least-squares (WLS) algorithm, combined with the NLOS identification informations, to mitigate NLOS bias. Step two, utilize Kalman filtering to optimize the localization results. Simulation results show that the proposed two-step algorithm can obtain better localization accuracy, especially when there are serious NLOS obstructions.

scholarly journals A Non-Parametric Propagation Condition Identification Method and Non-Line of Sight Mitigation Algorithm for Wireless Sensor Network

2016 ◽  
Vol 10 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Hao Chu ◽  
Cheng-dong Wu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Propagation Condition ◽  
Localization Accuracy ◽  
Chi Squared ◽  
Potential Applications ◽  
Unknown Node ◽  
Non Line Of Sight

The wireless sensor network (WSN) has received increasing attention since it has many potential applications such as the internet of things and smart city. The localization technology is critical for the application of the WSN. The obstacles induce the larger non-line of sight (NLOS) error and it may decrease the localization accuracy. In this paper, we mainly investigate the non-line of sight localization problem for WSN. Firstly, the Pearson's chi-squared testing is employed to identify the propagation condition. Secondly, the particle swarm optimization based localization method is proposed to estimate the position of unknown node. Finally the simulation experiments are implemented. The simulation results show that the proposed method owns higher localization accuracy when compared with other two methods.

An Efficient Fast-Mapping SLAM Method for UAS Applications Using Only Range Measurements

2016 ◽  
Vol 04 (02) ◽  
pp. 155-165 ◽  
Author(s):  
A. Torres-González ◽  
J. R. Martinez-de Dios ◽  
A. Jiménez-Cano ◽  
A. Ollero
Keyword(s):  
Experimental Validation ◽  
Field Experiments ◽  
Line Of Sight ◽  
Map Building ◽  
Localization Accuracy ◽  
Range Measurements ◽  
Localization And Mapping ◽  
Significant Interest ◽  
Local Map ◽  
Non Line Of Sight

This paper deals with 3D Simultaneous Localization and Mapping (SLAM), where the UAS uses only range measurements to build a local map of an unknown environment and to self-localize in that map. In the recent years Range Only (RO) SLAM has attracted significant interest, it is suitable for non line-of-sight conditions and bad lighting, being superior to visual SLAM in some problems. However, some issues constrain its applicability in practical cases, such as delays in map building and low map and UAS estimation accuracies. This paper proposes a 3D RO-SLAM scheme for UAS that specifically focuses on improving map building delays and accuracy levels without compromising efficiency in the consumption of resources. The scheme integrates sonar measurements together with range measurements between the robot and beacons deployed in the scenario. The proposed scheme presents two main advantages: (1) it integrates direct range measurements between the robot and the beacons and also range measurements between beacons — called inter-beacon measurements — which significantly reduce map building times and improve map and UAS localization accuracies; and (2) the SLAM scheme is endowed with a supervisory module that self-adapts the measurements that are integrated in SLAM reducing computational, bandwidth and energy consumption. Experimental validation in field experiments with an octorotor UAS showed that the proposed scheme improved map building times in 72%, map accuracy in 40% and UAS localization accuracy in 12%.

scholarly journals A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4438 ◽  
Author(s):  
Xin Tian ◽  
Guoliang Wei ◽  
Jianhua Wang ◽  
Dianchen Zhang
Keyword(s):  
Monte Carlo ◽  
Kalman Filtering ◽  
Probability Model ◽  
Likelihood Estimation ◽  
Line Of Sight ◽  
Extended Kalman Filtering ◽  
Optimal Position ◽  
Localization Accuracy ◽  
Localization And Tracking ◽  
Non Line Of Sight

In this paper, an optimization algorithm is presented based on a distance and angle probability model for indoor non-line-of-sight (NLOS) environments. By utilizing the sampling information, a distance and angle probability model is proposed so as to identify the NLOS propagation. Based on the established model, the maximum likelihood estimation (MLE) method is employed to reduce the error of distance in the NLOS propagation. In order to reduce the computational complexity, a modified Monte Carlo method is applied to search the optimal position of the target. Moreover, the extended Kalman filtering (EKF) algorithm is introduced to achieve localization. The simulation and experimental results show the effectiveness of the proposed algorithm in the improvement of localization accuracy.

scholarly journals Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging

Optica ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 63 ◽  
Author(s):  
Christopher A. Metzler ◽  
Felix Heide ◽  
Prasana Rangarajan ◽  
Muralidhar Madabhushi Balaji ◽  
Aparna Viswanath ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Line Of Sight ◽  
Non Line Of Sight

scholarly journals Deep-inverse correlography: towards real-time high-resolution non-line-of-sight imaging: erratum

Optica ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 249 ◽  
Author(s):  
Christopher A. Metzler ◽  
Felix Heide ◽  
Prasana Rangarajan ◽  
Muralidhar Madabhushi Balaji ◽  
Aparna Viswanath ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Line Of Sight ◽  
Non Line Of Sight
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