Improvement of fingerprinting technique for UWB indoor localization

Vinicchayakul, Wipassorn; Promwong, Sathaporn

doi:10.1109/jictee.2014.6804115

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…The data set is then used to train the ANN which can then be used to determine the location based on the online measurements of the selected features (and the other features if possible). [31,32,33] iii-k-Nearest Neighbor (kNN): The k-nearest neighbor algorithm relies upon other principles to determine a set of likely positions of the target and, by selectively discarding the outlying results, selects k-nearest positioning matches which are then averaged to obtain the location. The challenge becomes the determination of the nearest matches, one way relies on offline measurements to guess which readings are the most reliable [28].…”

Section: Fingerprintingmentioning

confidence: 99%

“…That is, works such as the ones presented in [31] and [33] use a single NN at the core of their positioning systems but [31] varies the amount and type of inputs to be fed into the NN while [33] investigates the accuracy variations with different dataset sizes used for training. On the other hand, [32] However, each method often comes with its set of drawbacks such as in [33] in which slightly better performances are achieved but at the cost of an extensive fingerprint collection campaigns with measurements 1 meter apart from each other over a gallery about 36 by 4 meters.…”

Section: Neural Network and Uwb In Ipsmentioning

confidence: 99%

“…The work in [31] also requires extensive fingerprint collection campaigns but ultimately achieves similar results or worse than this document's proposition in cases using 3 or less TOA measurements. The works of [31,32,33] all manage to achieve sub-meter accuracies using UWB.…”

Section: Neural Network and Uwb In Ipsmentioning

confidence: 99%

“…Furthermore, another key aspect of the system presented resides in its ability to mitigate error coming from mismeasurements of one or more sensors. That is, systems presented in [31,32,33] use a single NN to process all the data which can be risky if some sensor suffers from momentaneous interferences and provides inaccurate readings. Given the usage of a "swarm" of NNs in the presented system, KNN can efficiently mitigate outlier results and possibly, minimize the effects of a mismeasurement.…”

Section: Neural Network and Uwb In Ipsmentioning

confidence: 99%

See 3 more Smart Citations

Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band

Murari¹

2021

Preprint

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With the increasing widespread of sensor technology, new solutions for indoor positioning systems are continuously being developed and with them, new services requiring accurate positioning data have seen a great rise in popularity. In this thesis, a new design technique and deployment methodology for an indoor positioning system using neural networks is proposed to offer more flexibility and simplicity in the development of such a system which is currently very context-bound. The usage of battery-powered tags implies also that systems should not require excessive power consumption and the large number of targets to position requires a method that is not only accurate but also scalable. The proposed positioning system utilizes a small “swarm” of neural networks tasked to position targets based on distance measurements from Ultrawide Band sensors and requires shorter fingerprint collection campaigns and enables more flexibility in system deployment and alterations. Instead of relying solely on real data collected on the field for the training of neural networks, synthetic data is used for an initial training phase. Together, these propositions allow flexibility in terms of adding, removing or altering positions of reference nodes and simplifies offline deployment operations of an indoor positioning system. This thesis presents a system operating in a laboratory-workshop environment capable of good positioning accuracies and maintains robust performances in poor signal propagation.

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Section: Fingerprintingmentioning

confidence: 99%

Section: Neural Network and Uwb In Ipsmentioning

confidence: 99%

Section: Neural Network and Uwb In Ipsmentioning

confidence: 99%

Section: Neural Network and Uwb In Ipsmentioning

confidence: 99%

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Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band

Murari¹

2021

Preprint

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show abstract

“…However, in the indoor environment, the GPS positioning performance is poor due to the GPS signals being blocked and reflected by the building. Therefore, traditional indoor positioning mostly uses inertial measurement units [4,5], Wi-Fi [6,7], ultrawideband (UWB) [8], and Bluetooth [9,10] for positioning. However, unlike GPS, none of these methods have been shown to be most suitable for the majority of applications.…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization Based on VIO System and Three-Dimensional Map Matching

Zhang

Ren

Wang

et al. 2020

Sensors

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High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless.

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Development of Low-Cost Indoor Positioning Using Mobile-UWB-Anchor-Configuration Approach

Liu

Lin

Kong

et al. 2021

Parallel Architectures, Algorithms and Programming

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In recent years, with the growth of indoor positioning demand, many kinds of indoor positioning technologies have been studied. Compared with other technologies, UWB indoor positioning technology has the advantages of high positioning accuracy and strong anti-interference ability. However, the high cost of UWB hardware limits the application of this technology to practical applications. In particular, the effective communication distance of the UWB is within 10 meters, and if used in a large-area indoor environment, a plurality of anchor points is required to be installed to ensure the positioning accuracy. This leads to a high system hardware cost.In this paper, we proposed a mobile-UWB-anchor-network approach. We changed the fixed anchors in the UWB system into moving anchors to reduce the number of anchors in the area and reduce the cost of the system. This new approach is verified using experiments.

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Improvement of fingerprinting technique for UWB indoor localization

Cited by 11 publications

References 9 publications

Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band

Practical And Robust Approach For A Neural Networks Based Indoor Positioning System Using Ultrawide Band

Indoor Localization Based on VIO System and Three-Dimensional Map Matching

Development of Low-Cost Indoor Positioning Using Mobile-UWB-Anchor-Configuration Approach

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