RSSI-Based Indoor Localization Using Multi-Lateration With Zone Selection and Virtual Position-Based Compensation Methods

Booranawong, Apidet; Sengchuai, Kiattisak; Buranapanichkit, Dujdow; Jindapetch, Nattha; Saitō, Hiroshi

doi:10.1109/access.2021.3068295

Cited by 22 publications

(8 citation statements)

References 34 publications

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“…Nevertheless, if more actual anchor nodes are deployed, the hardware cost and localization system complexity will be increased [1]. In addition, the placement of four anchor nodes at the four corners of a square area has been widely implemented and tested [12], [16], [17] and it shows good estimation performance of the distance between target and anchor nodes [18]. Therefore, in this paper we only consider the case with N = 4, where four ANs are placed at the corners of the measured area, i.e., AN 1 (0, 0), AN 2 (0, 20), AN 3 (20, 20), AN 4 (20, 0).…”

Section: Simulations Resultsmentioning

confidence: 99%

RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy

et al. 2021

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Min-Max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original Min-Max algorithm only achieves coarse estimation in which the target node (TN) is regarded as the geometric centroid of the area of interest determined by measured RSS values. Although extended Min-Max (E-Min-Max) methods using weighted centroid instead of geometric centroid were recently proposed to cope with this problem, the improvement in the localization accuracy is still limited. In this paper, an improved Min-Max algorithm with area partition strategy (Min-Max-APS) is proposed to achieve better localization performance. In the proposed algorithm, the area of interest is first partitioned into four subareas, each of which contains a vertex of the original area of interest. Moreover, a minimum range difference criterion is designed to determine the target affiliated subarea whose vertex is "closest" to the target node. Then the target node's location is estimated as the weighted centroid of the target affiliated subarea. Since the target affiliated subarea is smaller than the original area of interest, the weighted centroid of the target affiliated subarea will be more accurate than that of the original area of interest. Simulation results show that the localization error (LE) of the proposed Min-Max-APS algorithm can drop below 0.16 meters, which is less than one-half of that of the E-Min-Max algorithm, and is also less than one-seventh of that of the original Min-Max algorithm. Moreover, for the proposed Min-Max-APS, 90% of the LE are smaller than 0.38 meters, while the same percentage of the LE are as high as 0.49 meters for the E-Min-Max and 1.12 meters for the original Min-Max, respectively.INDEX TERMS Min-Max algorithm, received signal strength (RSS), area partition, indoor localization, target node (TN).

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Section: Simulations Resultsmentioning

confidence: 99%

RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy

et al. 2021

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“…Experiments have been performed in the corridor of the hospital building, the 10th floor, Hat-Yai, Thailand. [33][34][35] This test field with the lower corridor area, the upper corridor area, and the empty area of the building is shown in Figure 5.…”

Section: Methodsmentioning

confidence: 99%

Development of an indoor corridor localization system based on a WCL method with corridor area selection and distance compensation

Wattananavin,

Booranawong,

Saito

2024

Int J Communication

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SummaryIn this paper, a received signal strength indicator (RSSI)‐based weighted centroid localization (WCL) method for indoor corridor scenarios is developed and tested. The contribution of the proposed system is that, to scope the area of an unknown target position, the possible corridor area where the target node should be located is automatically selected, and only reference nodes deployed in such an area are applied for position estimation. Additionally, to improve the estimation precision, distance values converted from measured RSSIs and used for the WCL are also compensated to alleviate the RSSI variation problem caused by physical environments. Therefore, our methods can save a number of reference nodes to be used, while localization accuracy can also be achieved. Experiments in the corridor environment using a 2.4 GHz IEEE 802.15.4 wireless sensor network with four reference nodes deployed in the test field dimension of 22 m × 9.3 m have been performed. Experimental results demonstrate that the proposed method, which requires only two reference nodes, outperforms the original WCL method with four reference nodes by 53.053%, as indicated by average localization errors. The results also reveal that the proposed method provides error distances lower than the WCL method for all test target positions, while all estimated positions fall within the corridor areas by 100%.

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“…These methods add some complexity to the basic path-loss RSSI multilateration models to bypass the well-known problems of the RSSI measurements instability. The main target of such systems is the profiling of the noise in an area and the application of a correction model that increases accuracy such as the division of an area into subspaces or zones and the application of a pre-established correction profile to measurements [25]. While these systems provide excellent results and are capable of functioning over existing infrastructure such as 2.4 GHz ZigBee transmitters, they are based on server-client models and are not device-agnostic.…”

Section: Related Work and Requirement Analysismentioning

confidence: 99%

Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

Kotrotsios

Fanariotis

Leligou

et al. 2022

Sensors

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In this paper, we present the results of a performance evaluation and optimization process of an indoor positioning system (IPS) designed to operate on portable as well as miniaturized embedded systems. The proposed method uses the Received Signal Strength Indicator (RSSI) values from multiple Bluetooth Low-Energy (BLE) beacons scattered around interior spaces. The beacon signals were received from the user devices and processed through an RSSI filter and a group of machine learning (ML) models, in an arrangement of one model per detected node. Finally, a multilateration problem was solved using as an input the inferred distances from the advertising nodes and returning the final position approximation. In this work, we first presented the evaluation of different ML models for inferring the distance between the devices and the installed beacons by applying different optimization algorithms. Then, we presented model reduction methods to implement the optimized algorithm on the embedded system by appropriately adapting it to its constraint resources and compared the results, demonstrating the efficiency of the proposed method.

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RSSI-Based Indoor Localization Using Multi-Lateration With Zone Selection and Virtual Position-Based Compensation Methods

Cited by 22 publications

References 34 publications

RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy

RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy

Development of an indoor corridor localization system based on a WCL method with corridor area selection and distance compensation

Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

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