TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization

Xiong, Wenxin; Schindelhauer, Christian; So, Hing Cheung; Bordoy, Joan; Gabbrielli, Andrea; Liang, Junli

doi:10.1016/j.sigpro.2020.107774

Cited by 44 publications

(20 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, two models are used, a model which assumes a high number of NLOS measurements and one which assumes only LOS measurements. Maximum correntropy based location approaches are also capable of mitigating the effect of non-line-of-sight measurements [16] and can also be combined with a Kalman filter when a certain motion model can be assumed [17]. Using relative signal amplitudes, a probabilistic algorithm for classification and rejection of NLOS signals, as shown by Haigh et al [18] may be developed.…”

Section: A Acoustic Localizationmentioning

confidence: 99%

Multimodal Indoor Localization: Fusion Possibilities of Ultrasonic and Bluetooth Low-Energy Data

et al. 2022

Self Cite

View full text Add to dashboard Cite

The resilience of indoor localization systems is a main concern of their industrial application. A combination of different techniques can enhance the overall robustness of such systems. In this work, we present fusion possibilities of coarse Bluetooth Low Energy localization based on the received signal strength indicator and the finer ultrasound time difference of arrival (TDOA) technique.This approach offers the advantage to robustify the high-accuracy ultrasonic localization in areas with non-optimal coverage. Moreover, the data fusion enables to enhance the overall localization area in a cost effective manner. This contribution proposes and evaluates (i) novel methods of how the ultrasonic system can be extended to a constrained area and (ii) a novel possibility to incorporate available Bluetooth signal strength information in the TDOA algorithm to improve accuracy.

show abstract

Section: A Acoustic Localizationmentioning

confidence: 99%

Multimodal Indoor Localization: Fusion Possibilities of Ultrasonic and Bluetooth Low-Energy Data

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to improve the performance of PCL algorithms, one may appropriately select TDOA measurements (removing outliers). Investigations for selecting time difference of flight (TDOF) and time of flight (TOF) measurements have been published for active location systems (when the target directly receives reference signals from transmitters or when the target emits reference signals that sensors detect) [17]- [24].…”

Section: Introductionmentioning

confidence: 99%

TDOA Data Selection for Accurate Passive Coherent Location

RODRIGUEZ¹,

Martins²,

Apolinario³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>This paper addresses the problem of selecting suitable time difference of arrival (TDOA) measurements for passive coherent location with multiple-transmitters/receivers. Firstly, we propose a unified mathematical framework for the following target location algorithms: spherical interpolation (SI), spherical intersection (SX), and nonlinearly constrained least squares (NLCLS). This paper generalizes the currently available models for scenarios with multiple-transmitters/one-receiver and one-transmitter/multiple-receivers. The SI/SX algorithms use closed-form expressions to tackle the location problem without considering all the nonlinear relationships among the optimization variables. As for the NLCLS algorithm, such nonlinearities are taken into account with the help of nonlinear constraints. Effects like multipath propagation and shadow fading increase TDOA measurement values resulting in outliers. To remove the outliers, we propose three TDOA selection processes. The first one uses a simple comparison rule. The second approach iteratively detects outliers based on cost function comparisons. The last approach divides the search region into cuboids. The cuboid-based approach separates consistent TDOAs from outliers, and its centroid represents a new location estimate. The numerical experiments show that the proposed cuboid-based method has greater robustness when increasing the probability of outliers.</p>

show abstract

“…In TDOA positioning, solving optimization problems by convex relaxation is also a hot topic for NLOS mitigation. Through l(1)-norm robustification and neurodynamic optimization in NLOS, the convex optimization problems with inequality constraints are solved by redefining Augmented Lagrangian effectively [7].…”

Section: Introductionmentioning

confidence: 99%

A Mobile Positioning Algorithm Based on Improved Robust Extended Kalman Filter with NLOS Identification and Mitigation

Wang

Yang

Gong

2022

Preprint

View full text Add to dashboard Cite

With the development of information age and the maturity of Internet of things (IoT) technology, wireless sensor network (WSN) has been widely applied in indoor localization. However, the non-line-of-sight (NLOS) environment and the inherent noise of the sensor will introduce errors in the measurement, which will seriously lead to inaccurate positioning. In this paper, a novel localization scheme based on mean reconstruction method is proposed, which reconstructs the distance measurements from each beacon nodes by taking the average twice to weaken the adverse effects of NLOS. At the same time, the noise average is re-estimated when the distance difference is tiny. Next, the robust extended Kalman filter (REKF) is used to process the reconstructed distance measurements to obtain positioning results. In order to prevent invalid reconstruction, hypothesis testing is used as NLOS identification to classify the position estimates which generated from all distance combinations by least-squares. Then we use residual weighting (RWGH) method to combine position estimates that fall into validation gate, and take it as a collaborative localization with the REKF algorithm. The simulation and experimental results show that the proposed algorithm has high positioning accuracy and strong positioning robustness.

show abstract

TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization

Cited by 44 publications

References 36 publications

Multimodal Indoor Localization: Fusion Possibilities of Ultrasonic and Bluetooth Low-Energy Data

Multimodal Indoor Localization: Fusion Possibilities of Ultrasonic and Bluetooth Low-Energy Data

TDOA Data Selection for Accurate Passive Coherent Location

A Mobile Positioning Algorithm Based on Improved Robust Extended Kalman Filter with NLOS Identification and Mitigation

Contact Info

Product

Resources

About