Optimization BLE Power Beacon for Indoor Locations Static Smart Device with Gaussian Filter

2021 ◽  
Vol 11 (1) ◽  
pp. 23
Author(s):  
Fanda Lyta Suzanayanti ◽  
Mudrik Alaydrus
Keyword(s):  
Power Optimization ◽  
Distance Estimation ◽  
Line Of Sight ◽  
Smart Device ◽  
Gaussian Filter ◽  
Additional Method ◽  
Transmit Power ◽  
Indoor Location ◽  
Improving Accuracy ◽  
Non Line Of Sight

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.

scholarly journals NLOS Identification and Mitigation Using Low-Cost UWB Devices

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3464 ◽  
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.

Improving Accuracy of Ray-Tracing Prediction in Non Line-of-Sight (NLOS) Urban Street Cell Environment beyond 6GHz

2017 ◽  
Vol E100.B (4) ◽  
pp. 566-574
Author(s):  
Nobutaka OMAKI ◽  
Tetsuro IMAI ◽  
Koshiro KITAO ◽  
Yukihiko OKUMURA
Keyword(s):  
Ray Tracing ◽  
Line Of Sight ◽  
Urban Street ◽  
Improving Accuracy ◽  
Non Line Of Sight

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

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>

Integrating the Non-Line of Sight Launching System (NLOS-LS) in the United States Navy

2007 ◽  
Author(s):  
Jonathon Emis ◽  
Bryan Huang ◽  
Timothy Jones ◽  
Mei Li ◽  
Don Tumbocon
Keyword(s):  
United States ◽  
United States Navy ◽  
The United States ◽  
Line Of Sight ◽  
Non Line Of Sight

scholarly journals Low-cost SPAD sensing for non-line-of-sight tracking, material classification and depth imaging

2021 ◽  
Vol 40 (4) ◽  
pp. 1-12
Author(s):  
Clara Callenberg ◽  
Zheng Shi ◽  
Felix Heide ◽  
Matthias B. Hullin
Keyword(s):  
Low Cost ◽  
Line Of Sight ◽  
Depth Imaging ◽  
Material Classification ◽  
Non Line Of Sight

scholarly journals Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 230 ◽  
Author(s):  
Slavisa Tomic ◽  
Marko Beko
Keyword(s):  
State Of The Art ◽  
Hybrid Approach ◽  
Critical Distance ◽  
Heuristic Approach ◽  
Target Localization ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Individual Measurement ◽  
New Approach ◽  
Non Line Of Sight

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.

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