Improved adaptive localisation approach for indoor positioning by using environmental thresholds with wireless sensor nodes

Koyuncu, Hakan; Yang, Shuang

doi:10.1049/iet-wss.2013.0100

Cited by 5 publications

(7 citation statements)

References 19 publications

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“…The experimental results showed that the RSS data become smoother and less fluctuant by using the proposed averaging method. A more complex algorithm is proposed in [20] to further reduce the variation of RSS data by using a weighing method. This algorithm can greatly reduce the variation of RSS data, but the increase on computational complexity cannot be neglected.…”

Section: Rss Data Processing Methodsmentioning

confidence: 99%

“…Trigonometric techniques are utilised together with EAL algorithm to calculate the unknown target positions in [20]. In EAL algorithm, the anchor node whose transmitted signal has minimum RSS value is taken as reference point.…”

Section: Location Estimation Methodsmentioning

confidence: 99%

“…Second, the unknown node estimates its distance to each anchor based on ranging models such as log‐distance path loss model [16] and polynomial models [17, 18]. Finally, the unknown node calculates its position based on location estimation methods such as maximum‐likelihood estimation (MLE), two‐step indoor location estimation method [19] and environmentally adaptive localisation (EAL) algorithm [20]. Notwithstanding low cost, the accuracy and stability of RSS localisation is not so good, because the RSS value is susceptible to noise, multi‐path fading, interference and other environmental parameters.…”

Section: Introductionmentioning

confidence: 99%

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RSS‐based indoor localisation using MDCF

Gui

Yang

Fang

et al. 2017

IET wirel. sens. syst.

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As a low-cost distance measurement method, received signal strength (RSS) is often used for indoor wireless sensor localisation. However, RSS values can be easily influenced by multi-path fading, noise and other environmental parameters. This decreases the accuracy and stability of estimated distance. To improve localisation accuracy, this study proposes a multiplicative distance-correction factor (MDCF) to counteract the inaccuracy of estimated distance. In the same indoor environment, the product of this CF and estimated distance is regarded as a good approximation of real distance between unknown node and an anchor node. Then, two location estimated methods based on MDCF (MDCF-grid and MDCF-particle swarm optimisation) are proposed. The experimental results confirm that the proposed location estimation methods can significantly improve localisation accuracy without extra hardware in practical indoor scenarios.

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Section: Rss Data Processing Methodsmentioning

confidence: 99%

Section: Location Estimation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RSS‐based indoor localisation using MDCF

Gui

Yang

Fang

et al. 2017

IET wirel. sens. syst.

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“…Their method employs a spring model to correct the positioning error by collaboratively adjusting the estimated positions. Koyuncu and Yang (2015) proposed a localization method that utilizes a static standard deviation threshold to adapt the environmental ranging factors. Their method also uses trigonometry to calculate the unknown target positions.…”

Section: Related Workmentioning

confidence: 99%

A novel position estimation method using accelerometer based error correction

Chen

2016

Engineering Computations

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Purpose – The purpose of this paper is to present a novel position estimation method to accurately locate an object. An accelerometer-based error correction method is also developed to correct the positioning error caused by signal drift of a wireless network. Finally, the method is also utilized to locate cows in a farm for monitoring the action of standing heat. Design/methodology/approach – The proposed method adopts the received signal strength indicator (RSSI) of a wireless sensor network (WSN) to compute the position of an object. The RSSI signal can be submitted from an endpoint device. A complex environment destabilizes the RSSI value, making the position estimation inaccurate. Therefore, a three-axial accelerometer is adopted to correct the position estimation accuracy. Timer and acceleration are two major factors in computing the error correction value to adjust the position estimate. Findings – The proposed method is tested on a farm management system for positioning dairy cows accurately. Devices with WSN module and three-axial accelerometer are mounted on the cows to monitor their positions and actions. Research limitations/implications – If cows in a crowded farm are close to each other, then the position estimation method is unable to position each cow correctly because too many close objects cause interference in the wireless network. Practical implications – Experimental results demonstrate that the proposed method improves the position accuracy, and monitor the heat action of the cows effectively. Originality/value – No position estimation method has been utilized to locate cows in a farm, especially for monitoring their actions via WSN and accelerometer. The proposed method adopts an accelerometer to efficiently improve the position error caused from the signal drift of WSN.

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“…Many costeffective solutions have been developed for indoor location estimation in recent years. Indoor positioning systems using received signal strength emerged as low-cost systems achieving acceptable accuracy for many applications [2,3]. Ultra-wideband (UWB) technology [4] has also appeared for accurate and precision indoor location estimation.…”

Section: Introductionmentioning

confidence: 99%

Hybrid indoor location positioning system

Rashidzadeh

2019

IET wirel. sens. syst.

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Indoor location positioning techniques have experienced a significant growth in recent years. This work presents a hybrid indoor positioning system with fine and coarse modes. It utilises acoustic signals for fine positioning and received signal strength (RSS) for coarse location estimation. Acoustic positioning systems require a line-of-sight connection for accurate positioning which may not be available due to obstacles in indoor environments. A new solution is presented to overcome this problem using RSS as a reference to validate the line-of-sight connection. Moreover, a new digital signal processing algorithm using a matched filter is presented to enhance the system's robustness in indoor environments with low a signal-to-noise ratio. Experimental measurement results in an indoor environment show that the proposed solution can accurately determine indoor locations with <6 cm positioning error on average.

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Improved adaptive localisation approach for indoor positioning by using environmental thresholds with wireless sensor nodes

Cited by 5 publications

References 19 publications

RSS‐based indoor localisation using MDCF

RSS‐based indoor localisation using MDCF

A novel position estimation method using accelerometer based error correction

Hybrid indoor location positioning system

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