Acoustic Localization System based on Smartphone without Time Synchronization

Zhang, Peng; Wang, Mei; Qiu, Hongbing; Kan, Ruixiang

doi:10.1109/isctt51595.2020.00091

Cited by 3 publications

(8 citation statements)

References 10 publications

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“…As for each transmission path between the acoustic anchor and the reference anchor, if there is only one NLOS acoustic localization signal in this loop judging by classification result, the server will delete the only one NLOS acoustic signal and complete the localization process based on the advanced TDOA algorithm; otherwise, the process will not be carried out. From previous experimental results in [13,14], we can see that the actual positioning accuracy of indoor localization systems based on advanced TDOA using only three LOS acoustic signals is even higher than that of three LOS acoustic signals plus one acoustic NLOS acoustic signal due to its less NLOS neg-ative effect. In order to use the classification results reasonably, relevant steps also need to be designed and implemented.…”

Section: Introductionmentioning

confidence: 90%

“…Overall System Framework. Referring to our previous work in [13,14], an indoor localization system called the ZKLocPro system is designed and constructed to cope with NLOS localization in the real-world situation without a clock synchronization process. An acoustic signal acquisition section is added on the main anchor for carrying out appropriate solutions according to the collected acoustic signal.…”

Section: System Overviewmentioning

confidence: 99%

“…As for the indoor localization system based on acoustic signals in the NLOS scenario, in order to reduce its negative impact, the main process will be adjusted to improve the final positioning accuracy according to the classification result. Combined with [4,7,[11][12][13], we use six smartphones to finish our experiments: one is for the main anchor, four is used to construct acoustic anchors, the last one is used for calculation reference, and one PC is regarded as the backstage management part to set up our own system. We utilize the algorithm in [4,11,12] to complete the feature extraction of acoustic signals for our system due to its stability and expansibility.…”

Section: Introductionmentioning

confidence: 99%

“…MySQL is used to store data and information temporarily, during which the classification results interacted with other parts based on the Wi-Fi. At this time, if there are not any NLOS acoustic signals in this localization loop based on identification result, the process can be completed directly based on advanced Time Difference of Arrival (TDOA) without a time synchronization [13,14] process and figure out the final localization results. As for each transmission path between the acoustic anchor and the reference anchor, if there is only one NLOS acoustic localization signal in this loop judging by classification result, the server will delete the only one NLOS acoustic signal and complete the localization process based on the advanced TDOA algorithm; otherwise, the process will not be carried out.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the results obtained from the acoustic signal LOS and NLOS classification section will determine when to adjust the entire localization process. There is no doubt that the classification part of NLOS acoustic signal is the key, and it directly affects the final result of the indoor localization system in the NLOS scenario [4,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization

Zhou

Qiu

2022

Wireless Communications and Mobile Computing

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The demand for an indoor localization system is increasing, and related research is also becoming more universal. Previous works on indoor localization systems mainly focus on the acoustic signals in Line of Sight (LOS) scenario to obtain accurate localization information, but their effectiveness in Nonline of Sight (NLOS) scenario remains comparatively untouched. These works are usually less efficient as the acoustic signals often bring diffraction, refraction, scattering, energy decays, and so on in NLOS environments. So the system needs adjusting accordingly in a complex NLOS scenario based on NLOS identification results. Therefore, the identification of NLOS acoustic signal turns out to be significant in the indoor localization system. If the system only uses original support vector machine (SVM) to complete NLOS identification, the result turns out to be poor by our test. To address this challenge, we propose a novel indoor localization system, named ZKLocPro, which utilizes an advanced swarm intelligence method to optimize the traditional SVM classification model to deal with NLOS acoustic signal identification. Its results can help the system adjust the localization process if necessary in a complex NLOS scenario. Obviously, it is also significant to build our own NLOS data set, which is suitable for an indoor localization system’s situation. Specifically, four methods are added: (1) new LOS and NLOS acoustic localization signal sample production, rearrangement, and reselecting process; (2) advanced parameter optimization process; (3) elitist strategy; and (4) inertia weight nonlinear decrement. The experimental result shows that our system is efficient and performs better than state-of-the-art congeneric works even in a complex NLOS scenario.

show abstract

Section: Introductionmentioning

confidence: 90%

Section: System Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization

Zhou

Qiu

2022

Wireless Communications and Mobile Computing

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RAILS: 3-D Real-Time Angle of Arrival Ultrasonic Indoor Localization System

Gabbrielli

Bordoy

Xiong

et al. 2023

IEEE Trans. Instrum. Meas.

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An Advanced Artificial Fish School Algorithm to Update Decision Tree for NLOS Acoustic Localization Signal Identification with the Dual-Receiving Method

et al. 2023

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For indoor sensor systems, it is essential to implement an extra supporting area notification part. To inform the real-time coordinates, the time difference of arrival (TDOA) algorithm can be introduced. For these indoor localization systems, their main processes are often built based on the line of sight (LOS) scenario. However, obstacles make the off-the-shelf localization system unable to play its due role in the flexible non-line of sight (NLOS) scenario. So, it is necessary to adjust the signals according to the NLOS identification results. However, the NLOS identification methods before were not effective enough. To address these challenges, on the one hand, this paper proposes an adaptive strategy for a dual-receiving signal processing method. On the other hand, the system is matched with the homologous NLOS identification method based on a novel artificial fish school algorithm (AFSA) and the decision tree model. According to our experiments, our novel AFSA optimization method can obtain a better effect and take less time. The NLOS acoustic signal identification accuracy can be improved significantly in flexible scenarios compared with other methods. Based on these processes, the system will achieve more accurate localization results in flexible NLOS situations.

show abstract

Acoustic Localization System based on Smartphone without Time Synchronization

Cited by 3 publications

References 10 publications

Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization

Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization

RAILS: 3-D Real-Time Angle of Arrival Ultrasonic Indoor Localization System

An Advanced Artificial Fish School Algorithm to Update Decision Tree for NLOS Acoustic Localization Signal Identification with the Dual-Receiving Method

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