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.

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

Constructing Floor Plan through Smoke Using Ultra Wideband Radar

2021 ◽  
Vol 5 (4) ◽  
pp. 1-29
Author(s):  
Weiyan Chen ◽  
Fusang Zhang ◽  
Tao Gu ◽  
Kexing Zhou ◽  
Zixuan Huo ◽  
...  
Keyword(s):  
Spatial Information ◽  
Low Cost ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Ultra Wideband ◽  
Indoor Environments ◽  
Floor Plan ◽  
Construction Methods ◽  
Median Error ◽  
Indoor Scenarios

Floor plan construction has been one of the key techniques in many important applications such as indoor navigation, location-based services, and emergency rescue. Existing floor plan construction methods require expensive dedicated hardware (e.g., Lidar or depth camera), and may not work in low-visibility environments (e.g., smoke, fog or dust). In this paper, we develop a low-cost Ultra Wideband (UWB)-based system (named UWBMap) that is mounted on a mobile robot platform to construct floor plan through smoke. UWBMap leverages on low-cost and off-the-shelf UWB radar, and it is able to construct an indoor map with an accuracy comparable to Lidar (i.e., the state-of-the-art). The underpinning technique is to take advantage of the mobility of radar to form virtual antennas and gather spatial information of a target. UWBMap also eliminates both robot motion noise and environmental noise to enhance weak reflection from small objects for the robust construction process. In addition, we overcome the limited view of single radar by combining multi-view from multiple radars. Extensive experiments in different indoor environments show that UWBMap achieves a map construction with a median error of 11 cm and a 90-percentile error of 26 cm, and it operates effectively in indoor scenarios with glass wall and dense smoke.

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 An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

2019 ◽  
Vol 11 (22) ◽  
pp. 2628 ◽  
Author(s):  
Liu ◽  
Li ◽  
Wang ◽  
Zhang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Inertial Measurement Unit ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Location ◽  
Magnetometer Data ◽  
Noise Covariance ◽  
Mems Imu ◽  
Non Line Of Sight

High precision positioning of UWB (ultra-wideband) in NLOS (non-line-of-sight) environment is one of the hot issues in the direction of indoor positioning. In this paper, a method of using a complementary Kalman filter (CKF) to fuse and filter UWB and IMU (inertial measurement unit) data and track the errors of variables such as position, speed, and direction is presented. Based on the uncertainty of magnetometer and acceleration, the noise covariance matrix of magnetometer and accelerometer is calculated dynamically, and then the weight of magnetometer data is set adaptively to correct the directional error of gyroscope. Based on the uncertainty of UWB distance observations, the covariance matrix of UWB measurement noise is calculated dynamically, and then the weight of UWB data observations is set adaptively to correct the position error. The position, velocity and direction errors are corrected by the fusion of UWB and IMU. The experimental results show that the algorithm can reduce the gyroscope deviation with magnetic noise and motion noise, so that the orientation estimates can be improved, as well as the positioning accuracy can be increased with UWB ranging noise.

scholarly journals Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1238
Author(s):  
Javier San Martín ◽  
Ainhoa Cortés ◽  
Leticia Zamora-Cadenas ◽  
Bo Joel Svensson
Keyword(s):  
Autonomous Vehicles ◽  
Research Work ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Use Case ◽  
Positioning System ◽  
Precise Positioning ◽  
Research Institutes ◽  
Simulated Environment ◽  
Non Line Of Sight

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.

scholarly journals Improving Deep Learning-Based UWB LOS/NLOS Identification with Transfer Learning: An Empirical Approach

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1714
Author(s):  
JiWoong Park ◽  
SungChan Nam ◽  
HongBeom Choi ◽  
YoungEun Ko ◽  
Young-Bae Ko
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Training Time ◽  
Identification Scheme ◽  
Office Environments ◽  
Non Line Of Sight

This paper presents an improved ultra-wideband (UWB) line of sight (LOS)/non-line of sight (NLOS) identification scheme based on a hybrid method of deep learning and transfer learning. Previous studies have limitations, in that the classification accuracy significantly decreases in an unknown place. To solve this problem, we propose a transfer learning-based NLOS identification method for classifying the NLOS conditions of the UWB signal in an unmeasured environment. Both the multilayer perceptron and convolutional neural network (CNN) are introduced as classifiers for NLOS conditions. We evaluate the proposed scheme by conducting experiments in both measured and unmeasured environments. Channel data were measured using a Decawave EVK1000 in two similar indoor office environments. In the unmeasured environment, the existing CNN method showed an accuracy of approximately 44%, but when the proposed scheme was applied to the CNN, it showed an accuracy of up to 98%. The training time of the proposed scheme was measured to be approximately 48 times faster than that of the existing CNN. When comparing the proposed scheme with learning a new CNN in an unmeasured environment, the proposed scheme demonstrated an approximately 10% higher accuracy and approximately five times faster training time.

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>

Sign in / Sign up

Export Citation Format

Share Document