Mobile Robot Indoor Positioning System Based on K-ELM

Hai-xia, Wang; Li, Junliang; Cui, Wei; Lu, Xiao; Zhang, Zhiguo; Sheng, Chunyang; Liu, Qingde

doi:10.1155/2019/7547648

Cited by 14 publications

(7 citation statements)

References 27 publications

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“…The extreme learning machine (ELM) has also been applied to indoor location fingerprinting due to its simplicity and speed [29]. Wang et al [30] used the kernel ELM (KELM) for indoor positioning of robots and showed that KELM not only has high positioning accuracy but also strong real-time performance. The [31] uses RF for position prediction and experiments show that the algorithm has a positioning accuracy of 1.68 m in 112 m 2 space.…”

Section: Fingerprint Positioning Algorithmsmentioning

confidence: 99%

Positioning by floors based on WiFi fingerprint

Hou,

Wang

2024

Meas. Sci. Technol.

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WiFi-based indoor positioning technology has gradually become a hot research topic in the field of indoor positioning, but the development of this technology has been facing the challenge of susceptibility to environmental interference. Therefore, in this paper, the kernel function method (KFM) with stronger interference resistance is used for positioning, and the adaptive σ algorithm is proposed for the time-consuming and laborious problem of manual parameter tuning, which incorporates the ideas of cross-validation and iteration. In addition, too many wireless access points (APs) mean higher computational cost and longer positioning time, so it is necessary to choose reasonable APs for positioning. In this paper, we use the random forest (RF) algorithm to assess the importance of APs and filter out a small number of APs with high importance. Considering the obvious differences in the WiFi signals received on different floors, a system framework for positioning by floors based on WiFi fingerprints is proposed. In the offline phase, the fingerprint library is first divided according to floors, and then perform separately AP selection and parameter tuning for each sub-fingerprint library. In the online phase, support vector machine (SVM) is used to discriminate the floors first, and then KFM is used for planar positioning. Experiments are conducted on the public dataset, and the results show that the proposed algorithm has higher positioning accuracy, more robustness, and less time-consuming compared to several common algorithms.

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Section: Fingerprint Positioning Algorithmsmentioning

confidence: 99%

Positioning by floors based on WiFi fingerprint

Hou,

Wang

2024

Meas. Sci. Technol.

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“…The In [19], the authors propose kernel ELM-(K-ELM-) based target L&T using 68,500 RSSI measurements obtained from indoor area of 32 meter × 16 meter with eight sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

Energy-Constrained Target Localization Scheme for Wireless Sensor Networks Using Radial Basis Function Neural Network

K¹,

Nandini

Jondhale

et al. 2023

International Journal of Distributed Sensor Networks

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The indoor object tracking by utilizing received signal strength indicator (RSSI) measurements with the help of wireless sensor network (WSN) is an interesting and important topic in the domain of location-based applications. Without the knowledge of location, the measurements obtained with WSN are of no use. The trilateration is a widely used technique to get location updates of target based on RSSI measurements from WSN. However, it suffers with high location estimation errors arising due to random variations in RSSI measurements. This paper presents a range-free radial basis function neural network (RBFN) and Kalman filtering- (KF-) based algorithm named RBFN+KF. The performance of the RBFN+KF algorithm is evaluated using simulated RSSIs and is compared against trilateration, multilayer perceptron (MLP), and RBFN-based estimations. The simulation results reveal that the proposed RBFN+KF algorithm shows very low location estimation errors compared to the rest of the three approaches. Additionally, it is also seen that RBFN-based approach is more energy efficient than trilateration and MLP-based localization approaches.

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“…This is a time-consuming task, and thus a CNN-based localization approach may be accurate for specific system conditions but is not suitable in general. The authors in [ 23 ] proposed a RSS-based robot indoor positioning scheme based on the kernel extreme learning machine (K-ELM) algorithm. The authors took 68,500 samples of RSSI measurements for a 32 m × 16 m area using eight APs.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Regression for Mobile Target Localization in Indoor Environments

Jondhale¹,

Mohan

Sharma

et al. 2022

Sensors

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Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.

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Mobile Robot Indoor Positioning System Based on K-ELM

Cited by 14 publications

References 27 publications

Positioning by floors based on WiFi fingerprint

Positioning by floors based on WiFi fingerprint

Energy-Constrained Target Localization Scheme for Wireless Sensor Networks Using Radial Basis Function Neural Network

Support Vector Regression for Mobile Target Localization in Indoor Environments

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