Improving Positioning Accuracy Using Optimization Approaches: A Survey, Research Challenges and Future Perspectives

Asaad, Safar Maghdid; Potrus, Moayad Yousif; Ghafoor, Kayhan Zrar; Maghdid, Halgurd S.; Mulahuwaish, Aos

doi:10.1007/s11277-021-09090-y

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…There are several methods that can be used indoors and outdoors to pinpoint the precise location of moving or stationary objects. When these methods depend on multiple technologies or a merging of different technologies, they are able to improve localization precision by a larger margin [36]. Numerous measurements are used in these methods to identify the precise location of unknown targets.…”

Section: Rf Range-based Localization Parametersmentioning

confidence: 99%

Indoor Positioning Based Ranging: A Review and Literature Survey of UWB and Li-Fi Technologies

Majeed,

Rashudah,

Muhammad

et al. 2024

Elektrika

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Why IPS (Indoor Positioning Systems)? This issue is one of the most challenging things to solve in wireless localization due to the lack of a Global Positioning System (GPS) and the existence of distinctive radio propagation characteristics. Although there are different localization options available, the accuracy of localization cannot satisfy customers' requirements. Positioning algorithms could be grouped into two groups, including range-based and range-free techniques. Before actually putting a localization technique into practice, the accuracy of those techniques is of the utmost importance. Range-based methods can often attain great accuracy with the help of specialized hardware, and this accuracy can be dependent on either the distances between nodes or the angles between them. In this paper, a comparison of different strategies utilized for positioning is presented, as well as an analysis of the pros and cons associated with supporting technologies for each strategy. A literature survey of the recent IPS technologies range-based with focuses on Ultra-Wide Band (UWB) and Light Fidelity (Li-Fi) is presented with significant recommendations. This literature considered the accuracy, complexity, scalability, cost, latency, deployment, and usability, as well as strengths, shortcomings, approaches, and issues determined by each work. This paper highlights the most recent research gaps and reviews the most promising findings, with recommendations to the reader and researcher, in UWB and Li-Fi indoor positioning systems over the last five years based on range-based techniques. In addition, this paper serves as a guide that discusses all of the measures that may be utilized in the process of evaluating localization technologies, and it could be considered a roadmap for existing and new researchers to identify and characterize suitable technologies for creating innovative systems and apps via stand-alone range-based positioning.

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Section: Rf Range-based Localization Parametersmentioning

confidence: 99%

Indoor Positioning Based Ranging: A Review and Literature Survey of UWB and Li-Fi Technologies

Majeed,

Rashudah,

Muhammad

et al. 2024

Elektrika

View full text Add to dashboard Cite

show abstract

“…However, this comes at the expense of increased computational complexity, as these methods involve the estimation of multiple parameters simultaneously. Moreover, the need for an accurate dynamic model can be a limiting factor in dynamic or nonlinear scenarios [43]. Conversely, observation domain filtering methods, with their computational simplicity, are well-suited for applications with limited computational resources.…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, observation domain filtering methods, with their computational simplicity, are well-suited for applications with limited computational resources. They can often provide satisfactory positioning accuracy in scenarios where signal quality is high and visibility is clear [43,44]. Nonetheless, their sensitivity to signal quality issues and the lack of consideration for dynamic models limit their usefulness in scenarios that require high precision or encounter dynamic movement [40,45].…”

Section: Introductionmentioning

confidence: 99%

Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method

Li,

Psimoulis,

et al. 2024

Remote Sensing

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To enhance the positioning accuracy of standalone GNSS receivers in environments unable to provide precise ephemeris and clock offset, such as undeveloped forest areas that lack network communication and power supply, this study employed the Time Difference Carrier Phase (TDCP) technology to improve the positioning accuracy of Standard Point Positioning (SPP), where the Least-Squares (LS) and the extended Multi-Epoch Least Squares (MELS) method were applied in the position domain filtering for a single GNSS receiver and compare its performance with the existing observation domain filtering method. Firstly, the simulated data sets with various positioning accuracies were used to verify the effectiveness and convergence of the LS filtering methods. The results indicate that the LS filtering method produces a lower root mean square (RMS) error than the original strategy. Secondly, this study uses two kinematic GNSS data sets to evaluate the performance of the observation and position domain filtering, with an emphasis on the MELS method. The numerical experiment results show that the position domain LS filtering method outperforms the other two methods. The open environment experiments result shows that the positioning domain filtering method achieved positioning accuracies of 0.202 m, 0.843 m, and 2.036 m in the E, N, and U directions, respectively, with improvements of 68.0%, 21.6%, and 24.0%, compared to the original algorithm which achieved positioning accuracies of 0.631 m, 1.076 m, and 2.680 m. It also achieved improvements of 24.0%, 4.0%, and 18.3%, respectively, compared to the observation domain filtering method with positioning accuracies of 0.353 m, 0.886 m, and 2.526 m. The forest scenes experiments result shows that the positioning domain filtering method achieved positioning accuracies of 1.308 m, 1.375 m, and 2.133 m in the E, N, and U directions, respectively, with improvements of 42.4%, 36.2%, and 27.6%, compared to original algorithm which achieved positioning accuracies of 1.863 m, 1.873 m, and 2.722 m, and also achieved improvements of 27.0%, 19.4% and 10.6%, respectively, comparing to observation domain filtering method with positioning accuracies of 1.661 m, 1.642 m and 2.359 m. Moreover, the examination of the LS method results based on different epochs reveals that the filtering accuracy increases as more epochs are incorporated into the position domain integration and the enhancement value reaches a few millimeters.

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“…LBS requirements have been adequately handled and incorporated into the GNSS in outdoor settings. However, the GNSS is unsuitable for IP systems since its signal weakens after passing through interior physical surroundings, resulting in inaccurate location data or entirely blocking GNSS signals ( Asaad et al, 2021 ). IP systems commonly employ Bluetooth ( Bai et al, 2020 ), ultra-wideband (UWB) ( Djosic et al, 2021 ), radio frequency identification (RFID), geomagnetic positioning ( Wu et al, 2021 ), visible light ( Guan et al, 2021 ), ZigBee ( Li, 2021 ), cellular networks (including LTE and 5G) ( Chai, Li & Huang, 2020 ; García, Maier & Philips, 2020 ), and Wi-Fi ( Maghdid et al, 2019 ) technologies.…”

Section: Introductionmentioning

confidence: 99%

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Asaad

Maghdid

2023

PeerJ Computer Science

Self Cite

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The Internet-of-Things (IoT) has been used with greater frequency to track peoples’ daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.

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Improving Positioning Accuracy Using Optimization Approaches: A Survey, Research Challenges and Future Perspectives

Cited by 8 publications

References 21 publications

Indoor Positioning Based Ranging: A Review and Literature Survey of UWB and Li-Fi Technologies

Indoor Positioning Based Ranging: A Review and Literature Survey of UWB and Li-Fi Technologies

Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

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