Unsynchronized ultrasound system for TDOA localization

Increasing efforts toward the development of positioning techniques testify the growing interest for indoor position-based applications and services. Many applications require accurate indoor positioning or tracking of people and assets, and some market sectors are starting a rapid growth of products based on these technologies. Ultrasonic systems have already been demonstrating their effectiveness and to possess the desired positioning accuracy and refresh rates. In this work, it is shown that a typical signal used in ultrasonic positioning systems to estimate the range between the target and reference points—namely, the linear chirp—due to the effects of acoustic diffraction, in some cases, undergoes a shape aberration, depending on the shape and size of the transducer and on the angle under which the transducer is seen by the receiver. In the presence of such signal shape aberrations, even one of the most robust ranging techniques, which is based on cross-correlation, provides results affected by a much greater error than expected. Numerical simulations are carried out for a typical ultrasonic chirp, ultrasonic emitter, and range technique based on cross-correlation and for a typical office room, obtained using the academic acoustic simulation software Field II. Spatial distributions of the ranging error are provided, clearly showing the favorable low error regions. The work demonstrates that particular attention must be paid to the design of the acoustic section of the ultrasonic positioning systems, considering both the shape and size of the ultrasonic emitters and the shape of the acoustic signal used.

where c air (m/s) is the speed of sound in the air, and T (°C) is the ambient temperature. In particular, the estimate of the range estimate R* is computed as follows:

where τ MAX is the lag of the maximum peak, and R cal is a calibration constant that takes into account all the fixed delays of the considered system.…”

Section: Simulations Resultsmentioning

confidence: 99%

Simulating Signal Aberration and Ranging Error for Ultrasonic Indoor Positioning

Carotenuto

Merenda

Iero

et al. 2020

“…The mobile robot will transmit the ultrasonic signal in each 200 ms cycle as with the distance measurement. The signal is transmitted with LOS in order to overcome the multi-path case, additionally, a short ultrasonic signal pulse, just 1 ms in this paper, can reduce interference for ultrasonic multi-path effect [ 37 ], so it is ignored here. The robot moves with a very low speed in the common coverage areas, so the Doppler effect of ultrasound is ignored in this experiment.…”

Section: Methodsmentioning

confidence: 99%

A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network

Liu

2017

110

This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μs. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal.

“…They work to determine the objects’ positions based on time measurements and the known velocity of the transmitted signal. The ToA [ 31 , 32 ], TDoA [ 33 ], and RTT [ 34 ] are among the most popular techniques used for this purpose.The ToA technique works by measuring the time the signal takes to reach the receiver station (timestamp). In order to obtain accurate time estimation, a strict synchronization between the two sides is necessary [ 31 , 32 ].…”

Section: Related Workmentioning

confidence: 99%

“…The TDoA techniques work by transmitting signals from three or more stations and then measuring the difference between the signals propagation times, which are then used to estimate the user location. Again, this requires a type of time synchronization but with transmitters only, unlike the ToA technique [ 33 ]. The ToA, and TDoA techniques are considered one-way measurement techniques.…”

Section: Related Workmentioning

confidence: 99%

“…These techniques determine the distance of a mobile unit (e.g., phone) to APs using the measurement of the signal’s propagation time and the known signal’s velocity. Different approaches have been proposed for measuring the propagation time, including time of arrival (ToA) [ 31 , 32 ], time difference of arrival (TDoA) [ 33 ], and RTT [ 34 ]. The problem with ToA and TDoA is that they require a precise time synchronization of all devices.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI

Rizk

Elmogy

Yamaguchi

2022

Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called RRLoc which fuses fingerprinting and time-based techniques with a view of combining their advantages. RRLoc leverages a novel approach for fusing received signal strength indication (RSSI) and round-trip time (RTT) measurements and extracting high-level features using deep canonical correlation analysis. The extracted features are then used in training a localization model for facilitating the location estimation process. Different modules are incorporated to improve the deep model’s generalization against overtraining and noise. The experimental results obtained at two different indoor environments show that RRLoc improves localization accuracy by at least 267% and 496% compared to the state-of-the-art fingerprinting and ranging-based-multilateration techniques, respectively.