scholarly journals A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6634
Author(s):  
Long Cheng ◽  
Sihang Huang ◽  
Mingkun Xue ◽  
Yangyang Bi
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Extended Kalman Filter ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Robust Localization

With the rapid development of information and communication technology, the wireless sensor network (WSN) has shown broad application prospects in a growing number of fields. The non-line-of-sight (NLOS) problem is the main challenge to WSN localization, which seriously reduces the positioning accuracy. In this paper, a robust localization algorithm based on NLOS identification and classification filtering for WSN is proposed to solve this problem. It is difficult to use a single filter to filter out NLOS noise in all cases since NLOS cases are extremely complicated in real scenarios. Therefore, in order to improve the robustness, we first propose a NLOS identification strategy to detect the severity of NLOS, and then NLOS situations are divided into two categories according to the severity: mild NLOS and severe NLOS. Secondly, classification filtering is performed to obtain respective position estimates. An extended Kalman filter is applied to filter line-of-sight (LOS) noise. For mild NLOS, the large outliers are clipped by the redescending score function in the robust extended Kalman filter, yielding superior performance. For severe NLOS, a severe NLOS mitigation algorithm based on LOS reconstruction is proposed, in which the average value of NLOS error is estimated and the measurements are reconstructed and corrected for subsequent positioning. Finally, an interactive multiple model algorithm is employed to obtain the final positioning result by weighting the position estimation of LOS and NLOS. Simulation and experimental results show that the proposed algorithm can effectively suppress NLOS error and obtain higher positioning accuracy when compared with existing algorithms.

scholarly journals A Mobile Localization Method in Smart Indoor Environment Using Polynomial Fitting for Wireless Sensor Network

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Yan Wang ◽  
Yang Yan ◽  
Zhengjian Li ◽  
Long Cheng
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Indoor Environment ◽  
Detection Threshold ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Polynomial Fitting

The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.

scholarly journals A Hierarchical Voting Based Mixed Filter Localization Method for Wireless Sensor Network in Mixed LOS/NLOS Environments

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2348 ◽  
Author(s):  
Yan Wang ◽  
Jinquan Hang ◽  
Long Cheng ◽  
Chen Li ◽  
Xin Song
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Unscented Kalman Filter ◽  
Rapid Development ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Localization Method

In recent years, the rapid development of microelectronics, wireless communications, and electro-mechanical systems has occurred. The wireless sensor network (WSN) has been widely used in many applications. The localization of a mobile node is one of the key technologies for WSN. Among the factors that would affect the accuracy of mobile localization, non-line of sight (NLOS) propagation caused by a complicated environment plays a vital role. In this paper, we present a hierarchical voting based mixed filter (HVMF) localization method for a mobile node in a mixed line of sight (LOS) and NLOS environment. We firstly propose a condition detection and distance correction algorithm based on hierarchical voting. Then, a mixed square root unscented Kalman filter (SRUKF) and a particle filter (PF) are used to filter the larger measurement error. Finally, the filtered results are subjected to convex optimization and the maximum likelihood estimation to estimate the position of the mobile node. The proposed method does not require prior information about the statistical properties of the NLOS errors and operates in a 2D scenario. It can be applied to time of arrival (TOA), time difference of arrival (TDOA), received signal (RSS), and other measurement methods. The simulation results show that the HVMF algorithm can efficiently reduce the effect of NLOS errors and can achieve higher localization accuracy than the Kalman filter and PF. The proposed algorithm is robust to the NLOS errors.

scholarly journals Positioning of Wireless Sensor Network under Emergency Communication Environment

2020 ◽  
Vol 19 (4) ◽  
pp. 273-279
Author(s):  
Ruilin Yuan
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Embedded System ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Initial Value ◽  
Emergency Communication ◽  
Positioning Accuracy ◽  
Communication Environment ◽  
Value Range

With the development of microelectromechanical system (MEMS), embedded system, and wireless communication, it is now feasible to implement and deploy wireless sensor network (WSN) in emergency communication environment. However, the positioning accuracy of WSN nodes needs to be further improved. To solve the problem, this paper improves the initial value calculation method of multi-hop positioning algorithms, which are suitable for emergency communication environment, and puts forward a WSN node positioning algorithm that narrows the initial values of Kalman filter. By narrowing the initial value range of Kalman filter, the specially deployed sensors could accurately derive its position from the known positions of anchor nodes. To prevent error accumulation in the network, distributed computing was performed to solve the global nonlinear optimization problem, and calculate the position of the nodes. Simulation results show that the proposed algorithm can improve the WSN positioning accuracy under emergency communication environment, while greatly saving computing and communication costs. The research further improves the practicability and efficiency of multi-hop positioning algorithms in emergency communication environment.

scholarly journals A Robust Indoor Mobile Localization Algorithm for Wireless Sensor Network in Mixed LOS/NLOS Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ou Yong Kang ◽  
Cheng Long
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Unscented Kalman Filter ◽  
Wireless Sensor ◽  
Selection Strategy ◽  
Time Of Arrival ◽  
Multiple Model ◽  
Localization Algorithm ◽  
Extend Kalman Filter

Wireless sensor network (WSN) is a self-organizing network which is composed of a large number of cheap microsensor nodes deployed in the monitoring area and formed by wireless communication. Since it has the characteristics of rapid deployment and strong resistance to destruction, the WSN positioning technology has a wide application prospect. In WSN positioning, the nonline of sight (NLOS) is a very common phenomenon affecting accuracy. In this paper, we propose a NLOS correction method algorithm base on the time of arrival (TOA) to solve the NLOS problem. We firstly propose a tendency amendment algorithm in order to correct the NLOS error in geometry. Secondly, this paper propose a particle selection strategy to select the standard deviation of the particle swarm as the basis of evolution and combine the genetic evolution algorithm, the particle filter algorithm, and the unscented Kalman filter (UKF) algorithm. At the same time, we apply orthogon theory to the UKF to make it have the ability to deal with the target trajectory mutation. Finally we use maximum likelihood localization (ML) to determine the position of the mobile node (MN). The simulation and experimental results show that the proposed algorithm can perform better than the extend Kalman filter (EKF), Kalman filter (KF), and robust interactive multiple model (RIMM).

Sign in / Sign up

Export Citation Format

Share Document