A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation

et al. 2020

IJGI

Currently, pedestrian dead reckoning (PDR) is widely used in indoor positioning. Since there are restrictions on a device’s pose in the procedure of using a smartphone to perform the PDR algorithm, this study proposes a novel heading estimation solution by calculating the integral of acceleration along the direction of the user’s movement. First, a lightweight algorithm, that is, a finite state machine (FSM)-decision tree (DT), is used to monitor and recognize the device mode, and the characteristics of the gyroscope at the corners are used to improve the heading estimate performance during the linear phase. Moreover, to solve the problem of heading angle deviation accumulation on positioning, a map-aided particle filter (PF) and behavior perception techniques are introduced to constrain the heading and correct the trajectory through the wall after filtering. The results indicate that the recognition of phone pose can be 93.25%. The improved heading estimation method can achieve higher stability and accuracy than the traditional step-wise method. The localization error can reduce to approximately 2.2 m when the smartphone is held at certain orientations.

Section: Introductionmentioning

confidence: 99%

A Multi-Mode PDR Perception and Positioning System Assisted by Map Matching and Particle Filtering

Wang

Chen

et al. 2020

IJGI

“…The methods for NLOS identification to compute the position of mobile nodes have been studied [14,[22][23][24][25][26]. It is necessary to determine whether the current measurement is obtained under NLOS conditions.…”

Section: Introductionmentioning

confidence: 99%

“…It fits several historical measurements and compares measurement noise with the root mean square residual of the fitted value and measurement. Another method conducts NLOS identification via comparing measurement noise with the residual of prediction and measurement [25]. These two methods above have achieved good positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The Wylie algorithm does not require prior information of NLOS error; however, it is not sensitive enough when LOS/NLOS switching. The method in [25] requires prior NLOS information because it has to obtain the parameters of RSS indication. Therefore, in order to make a compromise between sensitivity and robustness, this paper states a TOA-based fitting NLOS identification via distance prediction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wang

et al. 2020

Journal of Sensors

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.

“…In practical applications, fusion of multiple measurement methods is one of the effective ways to improve the localization effect. Han et al [4] proposed a novel indoor positioning algorithm based on the received signal strength indication and pedestrian dead reckoning in order to enhance the accuracy and reliability of our proposed probabilistic position selection algorithm in mixed line-of-sight (LOS) and non-lineof-sight (NLOS) environments. Angelo and Fascista [5] used the statistical characterization of the joint maximum-likelihood estimator to estimate the performance of hybrid RSSI and TOA ranging and proposed a novel closed-form estimator based on an ad hoc relaxation of the likelihood function.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Localization Algorithm of WSN Nodes Based on RSSI-TOA and Single Mobile Anchor Node

Zhang

Journal of Electrical and Computer Engineering

Zhang

et al. 2019

Aimed at the shortcomings of low localization accuracy of the fixed multianchor method, a three-dimensional localization algorithm for wireless sensor network nodes is proposed in this paper, which combines received signal strength indicator (RSSI) and time of arrival (TOA) ranging information and single mobile anchor node. A mobile anchor node was introduced in the proposed three-dimensional localization algorithm for wireless sensor networks firstly, and the mobile anchor node moves according to the Gauss-Markov three-dimensional mobility model. en, based on the idea of using RSSI ranging in the near end and TOA ranging in the far end, a ranging method combining RSSI and TOA ranging information is proposed to obtain the precise distance between the anchor node and the unknown node. Finally, the maximum-likelihood estimation method is used to estimate the position of unknown nodes based on the obtained ranging values. e MATLAB simulation results show that the proposed algorithm had a higher localization accuracy and lower localization energy consumption compared with the traditional RSSI localization method or TOA localization method.