VARIATIONS OF CHARACTERISTICS OF RADIO SIGNALS AT LONGER DECAMETER WAVELENGTHS PROPAGATING ALONG QUASI-VERTICAL PATHS

2011 ◽  
Vol 2 (3) ◽  
pp. 229-245
Author(s):  
K. P. Garmash ◽  
S. G. Leus ◽  
S. V. Panasenko ◽  
L. F. Chernogor
Keyword(s):  
Radio Signals

Ultra Short Baseline (USBL) Calibration for Positioning of Underwater Objects

2019 ◽  
Vol 2 (2) ◽  
pp. 73-85
Author(s):  
Bagus Septyanto ◽  
Dian Nurdiana ◽  
Sitti Ahmiatri Saptari
Keyword(s):  
Acoustic Wave ◽  
Scale Factor ◽  
Positioning System ◽  
Radio Waves ◽  
Short Baseline ◽  
Error Angle ◽  
Satellite Navigation System ◽  
The Earth ◽  
Underwater Positioning ◽  
Radio Signals

In general, surface positioning using a global satellite navigation system (GNSS). Many satellites transmit radio signals to the surface of the earth and it was detected by receiver sensors into a function of position and time. Radio waves really bad when spreading in water. So, the underwater positioning uses acoustic wave. One type of underwater positioning is USBL. USBL is a positioning system based on measuring the distance and angle. Based on distance and angle, the position of the target in cartesian coordinates can be calculated. In practice, the effect of ship movement is one of the factors that determine the accuracy of the USBL system. Ship movements like a pitch, roll, and orientation that are not defined by the receiver could changes the position of the target in X, Y and Z coordinates. USBL calibration is performed to detect an error angle. USBL calibration is done by two methods. In USBL calibration Single Position obtained orientation correction value is 1.13 ̊ and a scale factor is 0.99025. For USBL Quadrant calibration, pitch correction values is -1.05, Roll -0.02 ̊, Orientation 6.82 ̊ and scale factor 0.9934 are obtained. The quadrant calibration results deccrease the level of error position to 0.276 - 0.289m at a depth of 89m and 0.432m - 0.644m at a depth of 76m

The Peculiarities of Using a Quasi-Continuous Sounding Radio Signals in Radioacoustical Systems

1999 ◽  
Vol 53 (7-8) ◽  
pp. 34-38
Author(s):  
V. M. Kartashov ◽  
I. B. Nagibin ◽  
V. I. Aloykhin
Keyword(s):  
Radio Signals

An Acoustooptical Method of Demodulating Radio Signals

2000 ◽  
Vol 54 (8-9) ◽  
pp. 191-198
Author(s):  
Yurii Igorevich Vasil'ev
Keyword(s):  
Radio Signals

scholarly journals Privacy-preserving Continuous Tumour Relapse Monitoring Using In-body Radio Signals

2020 ◽  
Author(s):  
Sam Hylamia ◽  
Wenqing Yan ◽  
Andre Teixeira ◽  
Noor Badariah Asan ◽  
Mauricio Perez ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Tumour Relapse ◽  
Radio Signals

Sound Recovery From Radio Signals

2021 ◽  
Author(s):  
Muhammed Zahid Ozturk ◽  
Chenshu Wu ◽  
Beibei Wang ◽  
K. J. Ray Liu
Keyword(s):  
Radio Signals

scholarly journals Research on HAR-Based Floor Positioning

2021 ◽  
Vol 10 (7) ◽  
pp. 437
Author(s):  
Hongxia Qi ◽  
Yunjia Wang ◽  
Jingxue Bi ◽  
Hongji Cao ◽  
Shenglei Xu
Keyword(s):  
Barometric Pressure ◽  
Air Pressure ◽  
Location Based Services ◽  
Sensor Data ◽  
Reference Station ◽  
Position Change ◽  
Radio Signals ◽  
Positioning Method ◽  
Pressure Changes

Floor positioning is an important aspect of indoor positioning technology, which is closely related to location-based services (LBSs). Currently, floor positioning technologies are mainly based on radio signals and barometric pressure. The former are impacted by the multipath effect, rely on infrastructure support, and are limited by different spatial structures. For the latter, the air pressure changes with the temperature and humidity, the deployment cost of the reference station is high, and different terminal models need to be calibrated in advance. In view of these issues, here, we propose a novel floor positioning method based on human activity recognition (HAR), using smartphone built-in sensor data to classify pedestrian activities. We obtain the degree of the floor change according to the activity category of every step and determine whether the pedestrian completes floor switching through condition and threshold analysis. Then, we combine the previous floor or the high-precision initial floor with the floor change degree to calculate the pedestrians’ real-time floor position. A multi-floor office building was chosen as the experimental site and verified through the process of alternating multiple types of activities. The results show that the pedestrian floor position change recognition and location accuracy of this method were as high as 100%, and that this method has good robustness and high universality. It is more stable than methods based on wireless signals. Compared with one existing HAR-based method and air pressure, the method in this paper allows pedestrians to undertake long-term static or round-trip activities during the process of going up and down the stairs. In addition, the proposed method has good fault tolerance for the misjudgment of pedestrian actions.

Sign in / Sign up

Export Citation Format

Share Document