A low-cost centimeter-level acoustic localization system without time synchronization

Tan, Caiming; Zhu, Xinhua; Su, Yan; Wang, Yu; Wu, Zhiqiang; Gu, Dongbing

doi:10.1016/j.measurement.2015.09.037

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…In the last years, the BeepBeep strategy has become a popular choice for estimating the range between two devices having acoustic emitters and receivers [13], [14]. The main advantage of the BeepBeep technique is that it does not require node synchronization.…”

Section: A Beepbeepmentioning

confidence: 99%

“…Since the ranging accuracy depends both on the signal propagation speed and the precision of the TOA measurement, acoustic signals are usually preferred because their relative low speed [10]- [12]. In typical WASNs, TOA measurements are often performed with pairs of nodes taking timestamps of their respective local clock at the moment when the test signal is emitted or received [13], [14]. Possible sources of error include clock skew/drift between devices, misalignment between timestamps and actual signal emissions, and errors in detecting the arrival of the sound signals [3].…”

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confidence: 99%

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Simultaneous Ranging and Self-Positioning in Unsynchronized Wireless Acoustic Sensor Networks

Cobos

Pérez-Solano

Belmonte

et al. 2016

IEEE Trans. Signal Process.

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Abstract-Automatic ranging and self-positioning is a very desirable property in wireless acoustic sensor networks (WASNs) where nodes have at least one microphone and one loudspeaker. However, due to environmental noise, interference and multipath effects, audio-based ranging is a challenging task. This paper presents a fast ranging and positioning strategy that makes use of the correlation properties of pseudo-noise (PN) sequences for estimating simultaneously relative time-of-arrivals (TOAs) from multiple acoustic nodes. To this end, a proper test signal design adapted to the acoustic node transducers is proposed. In addition, a novel self-interference reduction method and a peak matching algorithm are introduced, allowing for increased accuracy in indoor environments. Synchronization issues are removed by following a BeepBeep strategy, providing range estimates that are converted to absolute node positions by means of multidimensional scaling (MDS). The proposed approach is evaluated both with simulated and real experiments under different acoustical conditions. The results using a real network of smartphones and laptops confirm the validity of the proposed approach, reaching an average ranging accuracy below 1 centimeter.

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Section: A Beepbeepmentioning

confidence: 99%

mentioning

confidence: 99%

Simultaneous Ranging and Self-Positioning in Unsynchronized Wireless Acoustic Sensor Networks

Cobos

Pérez-Solano

Belmonte

et al. 2016

IEEE Trans. Signal Process.

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“…Low-cost acoustic ranging technology is the base-stone of this kind of indoor localization and navigation systems. Most of the prototype systems are using time of arrival (TOA) estimation of an acoustic chirp signal to realize ranging based positioning [1], [7], [8], [9], [10], because TOA is apparently the most widely used ranging technique in ranging systems [11].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the similarities between acoustic ranging, radar systems and IR-UWB, a fixed-threshold based MF method is widely used to realize TOA estimation of acoustic chirp signals, based on an assumption of full prior knowledge of the source signals [1], [7], [8], [9], [10], where the TOA value is considered as the time delay of the first path component, which is the direct path in line-of-sight (LOS) scenarios. Unfortunately, this assumption of full prior knowledge of source signals is unrealistic in practical applications, especially in low-cost acoustic ranging systems due to the poor performance of electronic components.…”

Section: Introductionmentioning

confidence: 99%

TOA Estimation of Chirp Signal in Dense Multipath Environment for Low-Cost Acoustic Ranging

Zhang

Chen

Wang

et al. 2019

IEEE Trans. Instrum. Meas.

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In this paper, a novel time of arrival (TOA) estimation method is proposed based on an iterative cleaning process to extract the first path signal. The purpose is to address the challenge in dense multipath indoor environments that the power of the first path component is normally smaller than other multipath components, where the traditional matchfiltering (MF) based TOA estimator causes huge errors. Along with parameter estimation, the proposed process is trying to detect and extract the first path component by eliminating the strongest multipath component using a band-elimination filter in fractional Fourier Domain (FrFD) at each iterative procedure. To further improve the stability, a slack threshold and a strict threshold are introduced. Six simple and easily calculated termination criteria are proposed to monitor the iterative process. When the iterative 'cleaning' process is done, the outputs include the enhanced first path component and its estimated parameters. Based on these outputs, an optimal reference signal for the matchfiltering (MF) estimator can be constructed, and a more accurate TOA estimation can be conveniently obtained. The results from numerical simulations and experimental investigations verified that, for acoustic chirp signal TOA estimation, the accuracy of the proposed method is superior to those obtained by the conventional MF estimators.

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“…Among these approaches, sound-based positioning technologies have the advantages of being fully compatible with commercial off-the-shelf (COTS) smartphones, higher positioning accuracy than other technologies and low-cost infrastructure, and, thus, have attracted researchers’ attention. Quite a few systems have been designed and developed in the last decade in this area, aiming to introduce a reliable and practical technology for smartphone indoor localization [1,5,6,7,8,9]. However, from the results of Microsoft Indoor Localization Competition 2016 [10], the performance of sound localization systems is seriously impaired by indoor multipath propagation and the non-line-of-sight (NLOS) phenomenon in the real world.…”

Section: Introductionmentioning

confidence: 99%

Acoustic NLOS Identification Using Acoustic Channel Characteristics for Smartphone Indoor Localization

Zhang

Huang

Wang

et al. 2017

Sensors

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As the demand for indoor localization is increasing to support our daily life in large and complex indoor environments, sound-based localization technologies have attracted researchers’ attention because they have the advantages of being fully compatible with commercial off-the-shelf (COTS) smartphones, they have high positioning accuracy and low-cost infrastructure. However, the non-line-of-sight (NLOS) phenomenon poses a great challenge and has become the technology bottleneck for practical applications of acoustic smartphone indoor localization. Through identifying and discarding the NLOS measurements, the positioning performance can be improved by incorporating only the LOS measurements. In this paper, we focus on identifying NLOS components by characterizing the acoustic channels. Firstly, by analyzing indoor acoustic propagations, the changes of acoustic channel from the line-of-sight (LOS) condition to the NLOS condition are characterized as the difference of channel gain and channel delay between the two propagation scenarios. Then, an efficient approach to estimate relative channel gain and delay based on the cross-correlation method is proposed, which considers the mitigation of the Doppler Effect and reduction of the computational complexity. Nine novel features have been extracted, and a support vector machine (SVM) classifier with a radial-based function (RBF) kernel is used to realize NLOS identification. The experimental result with an overall 98.9% classification accuracy based on a data set with more than 10 thousand measurements shows that the proposed identification approach and features are effective in acoustic NLOS identification for acoustic indoor localization via a smartphone. In order to further evaluate the performance of the proposed SVM classifier, the performance of an SVM classifier is compared with that of traditional classifiers based on logistic regression (LR) and linear discriminant analysis (LDA). The results also show that a SVM with the RBF kernel function method outperforms others in acoustic NLOS identification.

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A low-cost centimeter-level acoustic localization system without time synchronization

Cited by 18 publications

References 17 publications

Simultaneous Ranging and Self-Positioning in Unsynchronized Wireless Acoustic Sensor Networks

Simultaneous Ranging and Self-Positioning in Unsynchronized Wireless Acoustic Sensor Networks

TOA Estimation of Chirp Signal in Dense Multipath Environment for Low-Cost Acoustic Ranging

Acoustic NLOS Identification Using Acoustic Channel Characteristics for Smartphone Indoor Localization

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