A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System

Liu, Ang; Lin, Shieh-Shing; Wang, Jianguo Jack; Kong, Xiaoying

doi:10.3390/s22218247

Cited by 13 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Relying solely on this method for NLOS identification is not accurate enough; therefore, we apply the sliding window algorithm proposed in previous research [38] to ∆𝑒. If The UWB system provides distance measurements from the robot to the four anchor points, while the wheeled odometer calculates information about the robot's motion and position.…”

Section: Nlos Identification and Mitigationmentioning

confidence: 99%

See 1 more Smart Citation

A Dynamic UKF-Based UWB/Wheel Odometry Tightly Coupled Approach for Indoor Positioning

Liu,

Wang,

Lin

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

The centimetre-level accuracy of Ultra-wideband (UWB) has attracted significant attention in indoor positioning. However, the precision of UWB positioning is severely compromised by non-line-of-sight (NLOS) conditions that arise from complex indoor environments. On the other hand, odometry is widely applicable to wheeled robots due to its reliable short-term accuracy and high sampling frequency, but it suffers from long-term drift. This paper proposes a tightly coupled fusion method with a Dynamic Unscented Kalman Filter (DUKF), which utilises odometry to identify and mitigate NLOS effects on UWB measurements. Horizontal Dilution of Precision (HDOP) was introduced to assess the impact of geometric distribution between robots and UWB anchors on UWB positioning accuracy. By dynamically adjusting UKF parameters based on NLOS condition, HDOP values, and robot motion status, the proposed method achieves excellent UWB positioning results in a severe NLOS environment, which enables UWB positioning even when only one line-of-sight (LOS) UWB anchor is available. Experimental results under severe NLOS conditions demonstrate that the proposed system achieves a Root Mean Square Error (RMSE) of approximately 7.5 cm.

show abstract

Section: Nlos Identification and Mitigationmentioning

confidence: 99%

“…Relying solely on this method for NLOS identification is not accurate enough; therefore, we apply the sliding window algorithm proposed in previous research [38] to ∆e. If sudden changes or outliers occur within ∆e, it indicates that the current UWB distance measurement is affected by NLOS.…”

Section: Nlos Identification and Mitigationmentioning

confidence: 99%

A Dynamic UKF-Based UWB/Wheel Odometry Tightly Coupled Approach for Indoor Positioning

Liu,

Wang,

Lin

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are several methods to detect bad ranging estimates. For example, previous knowledge of moving constraints can be used as in [11]. By calculating the variance of the difference between consecutive ranging estimates in a sliding window and comparing it with a threshold, bad ranging estimates can be detected.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the known maximum velocity must be low. That is the reason why the proposal of [11] is thought for indoor robots moving at 1 m/s. In other scenarios, it cannot be known if a sudden change in a ranging estimate is the result of a bad measurement or the consequence of a high moving velocity.…”

Section: Introductionmentioning

confidence: 99%

Novel Classification Method to Predict the Accuracy of UWB Ranging Estimates

Arsuaga,

Ochoa-De-Eribe-Landaberea,

Zamora-Cadenas

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Real time location systems (RTLSs) are becoming more relevant in a more data driven economy and society due to their wide range of application cases. When the location of an object needs to be tracked with high accuracy, ultra wideband (UWB) technology is usually the best option. Nevertheless, UWB ranging estimates are not completely immune to some sources of error such as non line of sight (NLOS) or multipath conditions. Thus, this paper proposes a real-time classification model based on machine learning (ML) to predict if received ranging estimates are in line of sight (LOS) or NLOS conditions and discard those in NLOS. However, it is also shown that classifying measurements as LOS or NLOS does not guarantee detecting inaccurate ranging estimates, since LOS measurements can also yield large errors. As an example, the ranging root mean square error (RMSE) of the data labelled as LOS in a UWB based localization system database in the literature is of 0,714 m, significantly higher than the theoretical accuracy of a UWB system. Thus, a novel ML-based classification model is proposed to predict the magnitude of the ranging error. After applying the proposed classification model in the same data, the ranging RMSE of those ranging samples classified as most accurate is of only 0.183 m, significantly lower than the best RMSE we can obtain on the classical LOS/NLOS classification approach.INDEX TERMS DWM1000, machine learning, random forest, ranging errors, real time location system, ultra wideband.

show abstract

“…The precise impulse timestamps can help reduce the multipath effect. UWB chips provide channel impulse responses (CIRs), which can be used for non-line-of-sight (NLOS) identification or mitigation [ 10 , 11 , 12 ]. UWB can be combined with inertial measurement units (IMU) and GPS for indoor–outdoor roaming [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System

Huang

Chen

Wei

et al. 2023

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT) for smart industry requires the surveillance and management of people and objects. The ultra-wideband positioning system is an attractive solution for achieving centimeter-level accuracy in target location. While many studies have focused on improving the accuracy of the anchor coverage range, it is important to note that in practical applications, positioning areas are often limited and obstructed by furniture, shelves, pillars, or walls, which can restrict the placement of anchors. Furthermore, some positioning regions are located beyond anchor coverage, and a single group with few anchors may not be able to cover all rooms and aisles on a floor due to non-line-of-sight errors causing severe positioning errors. In this work, we propose a dynamic-reference anchor time difference of arrival (TDOA) compensation algorithm to enhance accuracy beyond anchor coverage by eliminating local minima of the TDOA loss function near anchors. We designed a multidimensional and multigroup TDOA positioning system with the aim of broadening the coverage of indoor positioning and accommodating complex indoor environments. By employing an address-filter technique and group-switching process, tags can seamlessly move between groups with a high positioning rate, low latency, and high accuracy. We deployed the system in a medical center to locate and manage researchers with infectious medical waste, demonstrating its usefulness for practical healthcare institutions. Our proposed positioning system can thus facilitate precise and wide-range indoor and outdoor wireless localization.

show abstract

A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System

Cited by 13 publications

References 31 publications

A Dynamic UKF-Based UWB/Wheel Odometry Tightly Coupled Approach for Indoor Positioning

A Dynamic UKF-Based UWB/Wheel Odometry Tightly Coupled Approach for Indoor Positioning

Novel Classification Method to Predict the Accuracy of UWB Ranging Estimates

Range-Extension Algorithms and Strategies for TDOA Ultra-Wideband Positioning System

Contact Info

Product

Resources

About