Indoor location tracking in non-line-of-sight environments using a IEEE 802.15.4a wireless network

BLE beacon in an indoor location, battery efficiency usage must be right. Power beacons as one of the keys that need to be optimized. The decrease in power beacons will decrease the estimated distance from an indoor location based on the RSSI value. Therefore, an additional method is needed to recover the estimated distance's accuracy value due to the reduced power. In this paper, the method for recovery accuracy is by using a gaussian filter. Measurements were made at the same position on 3 BLE signals from multipower transmitters, which differed in their transmit power (TX power -1 dBm, -9 dBm and -20 dBm). The first six points are selected with the position of the line of sight as environment 1, and the second six points are chosen with the position of non-line of sight as environment 2 (obstructed). The first point of each environment is used as a reference. In environment 1, transmit power reduces the 24 dB effect to a decrease in accuracy distance estimation. The Gaussian effectiveness filter for improvement accuracy at all measurement points or 100%. In environment 2, reduce power transmit 12 dB is not followed by a decrease in accuracy distance estimation. The effectiveness of the Gaussian filter for improving accuracy is 60% of the number of measurement points. Finally, the Gaussian filter in the power optimization can provide recovery accuracy distance estimation is 80% from measurement sample for environment 1 and environment 2.

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NLOS Identification and Mitigation Using Low-Cost UWB Devices

Sensors ◽

10.3390/s19163464 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3464 ◽

Cited By ~ 17

Author(s):

Valentín Barral ◽

Carlos J. Escudero ◽

José A. García-Naya ◽

Roberto Maneiro-Catoira

Keyword(s):

Low Cost ◽

Estimation Error ◽

Ultra Wideband ◽

Line Of Sight ◽

Distance Measurements ◽

Indoor Location ◽

Indoor Scenarios ◽

Indoor Location Systems ◽

Clear Line ◽

Non Line Of Sight

Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters. In this work, machine learning (ML) techniques are employed to analyze several sets of real UWB measurements, captured in different scenarios, to try to identify the measurements facing non-line-of-sight (NLOS) propagation condition. Additionally, an ulterior process is carried out to mitigate the deviation of these measurements from the actual distance value between the devices. The results show that ML techniques are suitable to identify NLOS propagation conditions and also to mitigate the error of the estimates when there is LOS between the emitter and the receiver.

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Non-Line of Sight LoRa –Based Localization using RSSI-Kalman-Filter and Trilateration

International Journal on Information and Communication Technology (IJoICT) ◽

10.21108/ijoict.2020.00.495 ◽

2020 ◽

pp. 1

Author(s):

Thirafi Wian Anugrah ◽

Andrian Rakhmatsyah ◽

Aulia Arif Wardana

Keyword(s):

Kalman Filter ◽

Signal Strength ◽

Line Of Sight ◽

Filter Method ◽

Mountain Area ◽

Localization System ◽

Indoor Location ◽

Kalman Filter Method ◽

Close Result ◽

Non Line Of Sight

<span>The method that analyzes in this research is the combination of the Received Signal Strength Indicator (RSSI) with the Trilateration Method. This research also filtered the RSSI value using the Kalman filter method for smoothing data. The localization system traditionally based on Global Positioning System (GPS) device. However, GPS technology not working well in Non-line-of-sight (NLOS) like an indoor location or mountain area. The other way to implement the localization system is by using LoRa technology. This technology used radio frequency to communicate with each other node. The radiofrequency has a measurement value in the form of signal strength. These parameters, when combined with the trilateration method, can be used as a localization system. After implementation and testing, the system can work well compared with the GPS system for localization. RMSE is used to calculate error distance on these methods, the result from three methods used, the value from RSSI with Kalman filter have a close result to actual position, then value GPS follows with close result from Kalman filter, and the last one is RSSI without Kalman filter.</span>

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Cooperative non-line-of-sight localization technique for indoor wireless network

2011 8th International Conference on Information, Communications & Signal Processing ◽

10.1109/icics.2011.6173623 ◽

2011 ◽

Author(s):

Si Wen Chen ◽

Soon Yim Tan

Keyword(s):

Wireless Network ◽

Line Of Sight ◽

Localization Technique ◽

Indoor Wireless ◽

Non Line Of Sight

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Based on Wavelet Transform of TDOA/AOA Location Tracking Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1772 ◽

2013 ◽

Vol 427-429 ◽

pp. 1772-1775

Author(s):

Bao Quan Chen ◽

Yong Yi Mao ◽

Yang Yang ◽

Ping Xu

Keyword(s):

Wavelet Transform ◽

Mobile Station ◽

Line Of Sight ◽

Location Tracking ◽

Distance Threshold ◽

Location Algorithm ◽

Positioning Algorithm ◽

Station Location ◽

Better Than ◽

Non Line Of Sight

In cellular network mobile station location, the actual measured values non-line-of-sight error exists, makes the precision positioning algorithm based on the measured value is lower, aiming at the effects of non-line-of-sight error on the positioning performance, this paper proposes a new algorithm to reduce non line-of-sight error. Using wavelet transform to signal de-noising effect, eliminate the error in the measured value and reuse method of TDOA/AOA location algorithm to estimate mobile station location, with a certain distance threshold to mobile station location tracking. Simulation results show that the algorithm can effectively improve the positioning precision in non line-of-sight environments, positioning result was significantly better than Chan algorithm and TDOA/AOA location method algorithm.

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