RSSI based adaptive indoor location tracker

Maduskar, Deepti; Tapaswi, Shashikala

doi:10.1186/s41070-017-0015-z

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…The RSSI‐based scheme is highly appreciated because of its minimal complexity. Thus, some RSSI‐based applications have been developed to track the location of an object in the indoor area . However, this scheme required many reference points to find out the best possible match from a group of possible locations.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, some RSSI-based applications have been developed to track the location of an object in the indoor area. 5,6 However, this scheme required many reference points to find out the best possible match from a group of possible locations. The second category is based on the direction of the signal, which estimates the angle between the arrival of the signal and the array of reference antennas.…”

Section: Introductionmentioning

confidence: 99%

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A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

Musa

Nugraha

Han

et al. 2019

Int J Communication

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Summary Among existing wireless technologies, ultra‐wideband (UWB) is the most promising solution for indoor location tracking. UWB has a great multipath fading immunity; however, great multipath resolvability alone does not eliminate the effect of non‐line‐of‐sight (NLOS) and multipath propagation. NLOS and multipath propagation in indoor environments can easily produce meters of UWB ranging error. This condition gives an enormous impact on the accuracy of indoor location tracking data. To address this problem, we propose an NLOS detection method using recursive decision tree learning. Using the UWB channel quality indicators information, we develop our model with the Gini index and altered priors splitting criteria. We then validate the constructed model using the 10‐fold cross‐validation method. Our experiment shows that the constructed model has correctly detected 90% of both line‐of‐sight (LOS) and NLOS cases on the seven different indoor environments. The result of this work can be used for the UWB indoor location tracking accuracy improvement.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

Musa

Nugraha

Han

et al. 2019

Int J Communication

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“…The measured RSSI and a previously stored RSSI data set were compared for position estimation. The RMSE value of position estimation was presented as 1.47 m [10]. Li et al proposed a new path loss model instead of a fixed path loss model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this approach, RSSI levels of various stationary Wi-Fi access points are used for position estimation. Due to having minimal complexities, RSSI-based indoor location estimation is highly appreciated [10].…”

Section: Introductionmentioning

confidence: 99%

ABC-ANN Based Indoor Position Estimation Using Preprocessed RSSI

Ünlerşen¹

2022

Electronics

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The widespread use of mobile devices has popularized the idea of indoor navigation. The Wi-Fi fingerprint method is emerging as an important alternative indoor positioning method for GPS usage difficulties. This study utilizes RSSI signals with three preprocessed states (raw, preprocessed with the path loss adapted, and exponential transformed) to train and test an artificial neural network (ANN). A systematic approach to the determination of neuron numbers in the hidden layers and activation functions of ANN is provided. The ANN is trained by the artificial bee colony algorithm. Five ML methods have been employed for estimation. The best performance has been achieved with ABC-ANN by the path loss adapted database with the MAE of 1.01 m. The estimation done using processed RSSI values has better performance than raw RSSI values. In addition, 33% less error occurs with the mentioned method compared to the data set source study.

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“…Due to their ubiquity, a WiFi or Bluetooth signal is usually employed. The received power decreases as the distance the signal must travel increases, using the inverse square law [22,31,35,24]. This allows calculating the distance between the signal emitter and the beacons receiving the signal.…”

Section: Related Workmentioning

confidence: 99%

Indoor Localization Techniques Within a Home Monitoring Platform

Marin¹,

Bocicor²,

Molnar³

2020

Communications in Computer and Information Science

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This paper details a number of indoor localization techniques developed for real-time monitoring of older adults. These were developed within the framework of the i-Light research project that was funded by the European Union. The project targeted the development and initial evaluation of a configurable and cost-effective cyber-physical system for monitoring the safety of older adults who are living in their own homes. Localization hardware consists of a number of custom-developed devices that replace existing luminaires. In addition to lighting capabilities, they measure the strength of a Bluetooth Low Energy signal emitted by a wearable device on the user. Readings are recorded in real time and sent to a software server for analysis. We present a comparative evaluation of the accuracy achieved by several server-side algorithms, including Kalman filtering, a look-back heuristic as well as a neural networkbased approach. It is known that approaches based on measuring signal strength are sensitive to the placement of walls, construction materials used, the presence of doors as well as existing furniture. As such, we evaluate the proposed approaches in two separate locations having distinct building characteristics. We show that the proposed techniques improve the accuracy of localization. As the final step, we evaluate our results against comparable existing approaches.

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RSSI based adaptive indoor location tracker

Cited by 11 publications

References 8 publications

A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

A decision tree‐based NLOS detection method for the UWB indoor location tracking accuracy improvement

ABC-ANN Based Indoor Position Estimation Using Preprocessed RSSI

Indoor Localization Techniques Within a Home Monitoring Platform

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