Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation

This paper considers the application of signal processing methods to passive indoor positioning with acoustics microphones. The key aspect of this problem is time-delay estimation (TDE) that is used to get the time difference of arrival of the source’s signal between the pair of distributed microphones. This paper studies the approach based on generalized phase spectrum (GPS) TDE methods. These methods use frequency-domain information about the received signals that make them different from widely applied generalized cross-correlation (GCC) methods. Despite the more challenging implementation, GPS TDE methods can be less demanding on computational resources and memory than conventional GCC ones. We propose an algorithmic implementation of a GPS estimator and study the various frequency weighting options in applications to TDE in a small room acoustic environment. The study shows that the GPS method is a reliable option for small acoustically dead rooms and could be effectively applied in presence of moderate in-band noises. However, GPS estimators are far less efficient in less acoustically dead environments, where other TDE options should be considered. The distinguishing feature of the proposed solution is the ability to get the time delay using a limited number of the adjusted bins. The solution could be useful for passively locating moving emitters of narrow-band continual noises using computationally simple frequency detection algorithms.

Section: Introductionmentioning

confidence: 99%

Study of Generalized Phase Spectrum Time Delay Estimation Method for Source Positioning in Small Room Acoustic Environment

Faerman

Avramchuk

Voevodin

et al. 2022

“…In addition to that, location in GPS-denied environments supports indoor positioning and navigation, an emerging field with a wide variety of applications for private and public areas and from casual personal use to critical emergency response [ 12 ]. Although some systems are based on the availability of an infrastructure based on IoT sensors [ 12 ] or camera-based tracking systems [ 13 ], a more general solution requires systems to calculate the indoor map based on their payload sensors, such as LiDAR [ 14 ], deep learning-aided monocular cameras [ 15 ], optical and mass flow sensors [ 16 ], or multi-sensor fusion [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Indoor Path-Planning Algorithm for UAV-Based Contact Inspection

González-deSantos

Frías

Martínez-Sánchez

et al. 2021

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.

“…They can be used for the location of the causes of emergency situations, for example, gas sources [ 25 ]. If we consider magnetic sensors, they are usually only used as a support for indoor navigation systems [ 26 ] because due to missing signals from satellite navigation systems, indoor navigation is one of the more challenging problems that need to be solved regarding the ongoing extensive research in the area of UAVs [ 27 , 28 , 29 , 30 ]. Therefore, many types of visual-based [ 31 , 32 , 33 ], visual-aided [ 34 ], acoustic [ 35 ], and ultrasonic methods [ 36 ] and the related integrated navigation, localization [ 37 , 38 ], positioning and landing systems [ 39 , 40 , 41 ], indoor path-planning [ 42 ], and mapping guidance algorithms [ 43 ] have been developed.…”

Section: Introductionmentioning

confidence: 99%

Indoor Mapping of Magnetic Fields Using UAV Equipped with Fluxgate Magnetometer

Lipovský

Draganová

Novotňák

et al. 2021

Unmanned aerial vehicles (UAVs) are used nowadays in a wide range of applications, including monitoring, mapping, or surveying tasks, involving magnetic field mapping, mainly for geological and geophysical purposes. However, thanks to the integration of ultrasound-aided navigation used for indoor UAV flight planning and development in sensorics, the acquired magnetic field images can be further used, for example, to enhance indoor UAV navigation based on the physical quantities of the image or for the identification of risk areas in manufacturing or industrial halls, where workers can be exposed to high values of electromagnetic fields. The knowledge of the spatial distribution of magnetic fields can also provide valuable information from the perspective of the technical cleanliness. This paper presents results achieved with the original fluxgate magnetometer developed and specially modified for integration on the UAV. Since the magnetometer had a wider frequency range of measurement, up to 250 Hz, the DC (Direct Current) magnetic field and low frequency industrial components could be evaluated. From the obtained data, 3D magnetic field images using spline interpolation algorithms written in the Python programming language were created. The visualization of the measured magnetic field in the 3D plots offer an innovative view of the spatial distribution of the magnetic field in the area of interest.