Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments; where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance

A survey on acoustic positioning systems for location-based services

Positioning systems have become increasingly popular in the last decade for location-based services, such as navigation, and asset tracking and management. As opposed to outdoor positioning, where the global navigation satellite system became the standard technology, there is no consensus yet for indoor environments despite the availability of different technologies, such as radio frequency, magnetic field, visual light communications, or acoustics. Within these options, acoustics emerged as a promising alternative to obtain high-accuracy low-cost systems. Nevertheless, acoustic signals have to face very demanding propagation conditions, particularly in terms of multipath and Doppler effect. Therefore, even if many acoustic positioning systems have been proposed in the last decades, it remains an active and challenging topic. This article surveys the developed prototypes and commercial systems that have been presented since they first appeared around the 1980s to 2022. We classify these systems into different groups depending on the observable that they use to calculate the user position, such as the time-of-flight, the received signal strength, or the acoustic spectrum. Furthermore, we summarize the main properties of these systems in terms of accuracy, coverage area, and update rate, among others. Finally, we evaluate the limitations of these groups based on the link budget approach, which gives an overview of the system's coverage from parameters such as source and noise level, detection threshold, attenuation, and processing gain.Agencia Estatal de InvestigaciónResearch Council of Norwa

High precision hybrid RF and ultrasonic chirp-based ranging for low-power IoT nodes

Hybrid acoustic-RF systems offer excellent ranging accuracy, yet they typically come at a power consumption that is too high to meet the energy constraints of mobile IoT nodes. We combine pulse compression and synchronized wake-ups to achieve a ranging solution that limits the active time of the nodes to 1 ms. Hence, an ultra low-power consumption of 9.015 µW for a single measurement is achieved. The operation time is estimated on 8.5 years on a CR2032 coin cell battery at a 1 Hz update rate, which is over 250 times larger than state-of-the-art RF-based positioning systems. Measurements based on a proof-of-concept hardware platform show median distance error values below 10 cm. Both simulations and measurements demonstrate that the accuracy is reduced at low signal-to-noise ratios and when reflections occur. We introduce three methods that enhance the distance measurements at a low extra processing power cost. Hence, we validate in realistic environments that the centimeter accuracy can be obtained within the energy budget of mobile devices and IoT nodes. The proposed hybrid signal ranging system can be extended to perform accurate, low-power indoor positioning

Deep Room Recognition Using Inaudible Echos

Recent years have seen the increasing need of location awareness by mobile applications. This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker. Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy. However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition. This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses. Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models. Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware. Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively. Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).Comment: 29 page

Asynchronous Ultrasonic Trilateration for Indoor Positioning of Mobile Phones

Spatial awareness is fast becoming the key feature on today‟s mobile devices. While accurate outdoor navigation has been widely available for some time through Global Positioning Systems (GPS), accurate indoor positioning is still largely an unsolved problem. One major reason for this is that GPS and other Global Navigation Satellite Systems (GNSS) systems offer accuracy of a scale far different to that required for effective indoor navigation. Indoor positioning is also hindered by poor GPS signal quality, a major issue when developing dedicated indoor locationing systems. In addition, many indoor systems use specialized hardware to calculate accurate device position, as readily available wireless protocols have so far not delivered sufficient levels of accuracy. This research aims to investigate how the mobile phone‟s innate ability to produce sound (notably ultrasound) can be utilised to deliver more accurate indoor positioning than current methods. Experimental work covers limitations of mobile phone speakers in regard to generation of high frequencies, propagation patternsof ultrasound and their impact on maximum range, and asynchronous trilateration. This is followed by accuracy and reliability tests of an ultrasound positioning system prototype.This thesis proposes a new method of positioning a mobile phone indoors with accuracy substantially better than other contemporary positioning systems available on off-theshelf mobile devices. Given that smartphones can be programmed to correctly estimate direction, this research outlines a potentially significant advance towards a practical platform for indoor Location Based Services. Also a novel asynchronous trilateration algorithm is proposed that eliminates the need for synchronisation between the mobile device and the positioning infrastructure