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 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.

Performances of Non-Line-of-Sight Ultraviolet Multi-Scatter Propagation for Noncoplanar Geometries

2012 ◽  
Vol 571 ◽  
pp. 214-218
Author(s):  
Yi Nan Tang ◽  
Xiao Ping Xie ◽  
Wei Zhao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Impulse Response ◽  
Mathematical Description ◽  
Pulse Width ◽  
Path Loss ◽  
Propagation Model ◽  
Line Of Sight ◽  
Non Line Of Sight

A multi-scatter propagation model based on Monte Carlo method is presented. This model can be applied to all the geometries, including coplanar or noncoplanar scenario. The mathematical description of this model is deduced. We obtain the spatial positions of photon with three Cartesian coordinates after each propagation step and the received judgment conditions. Employing a photon tracing technique, Monte Carlo simulation is performed to investigate the signal impulse response and the path loss. The results indicate that, when the off-axis angle increases, the amplitude of the impulse response decreases, while the path loss increases. In addition, it is observed that the pulse width increases with the off-axis angle.

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%.

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 Noise Reduction Scheme for Parametric Loop Division 3D Wireless Localization Algorithm Based on Extended Kalman Filtering

2019 ◽  
Vol 8 (2) ◽  
pp. 24 ◽  
Author(s):  
Tanveer Ahmad ◽  
Xue Jun Li ◽  
Boon-Chong Seet
Keyword(s):  
Kalman Filtering ◽  
Ieee 802.15.4 ◽  
Outer Region ◽  
Measurement Noise ◽  
Sensor Nodes ◽  
Node Degree ◽  
Personal Area Networks ◽  
Extended Kalman Filtering ◽  
Localization Accuracy ◽  
Localization Scheme

Thanks to IEEE 802.15.4 defining the operation of low-rate wireless personal area networks (LR-WPANs), the door is open for localizing sensor nodes using tiny, low power digital radios such as Zigbee. In this paper, we propose a three-dimensional (3D) localization scheme based on well-known loop invariant for division algorithm. Parametric points are proposed by using the reference anchor points bounded in an outer region named as Parametric Loop Division (PLD) algorithm. Similar to other range-based localization methods, PLD is often influenced by measurement noise which greatly degrades the performance of PLD algorithm. We propose to adopt extended Kalman filtering (EKF) to refine node coordinates to mitigate the measurement noise. We provide an analytical framework for the proposed scheme and find the lower bound for its localization accuracy. Simulation results show that compared with the existing PLD algorithm, our technique always achieves better positioning accuracy regardless of network topology, communication radius, noise statistics, and the node degree of the network. The proposed scheme PLD-EKF provides an average localization accuracy of 0.42 m with a standard deviation of 0.26 m.

Accuracy Bounds for Wireless Localization Methods

2009 ◽  
pp. 380-405 ◽  
Author(s):  
Michael L. McGuire ◽  
Konstantinos N. Plataniotis
Keyword(s):  
Lower Bound ◽  
Location Estimation ◽  
Line Of Sight ◽  
Regularity Conditions ◽  
Node Localization ◽  
Localization Accuracy ◽  
Sensory Data ◽  
Line Of Sight Propagation ◽  
Cramer Rao Lower Bound ◽  
Non Line Of Sight

Node localization is an important issue for wireless sensor networks to provide context for collected sensory data. Sensor network designers need to determine if the desired level of localization accuracy is achievable from their network configuration and available measurements. The Cramér-Rao lower bound is used extensively for this purpose. This bound is loose since it uses only information from measurements in its calculations. Information, such as that from the sensor selection process, is not considered. In addition, non-line-of-sight radio propagation causes the regularity conditions of the Cramér-Rao lower bound to be violated. This chapter demonstrates the Weinstein-Weiss and extended Ziv-Zakai lower bounds for localization error which remain valid with non-line-of-sight propagation. These bounds also use all available information for bound calculations. It is demonstrated that these bounds are tight to actual estimator performance and may be used determine the available accuracy of location estimation from survey data collected in the network area.

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