An ultrasonic sensor network for high-quality range-bearing-based indoor positioning

Sergio, Flores; Geis, Johanna; Vossiek, Martin

doi:10.1109/plans.2016.7479747

Cited by 12 publications

(2 citation statements)

References 8 publications

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“…Ultrasonic sensors are used in a wide field of applications. Although high performance sensor solutions are available in medical applications [1], automotive and robotic scenarios prefer low-cost versions [2], [3]. As the production costs are low, ultrasonic sensors are widely spread in such scenarios.…”

Section: Introductionmentioning

confidence: 99%

Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks

Popperli

Gulagundi

Yogamani

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Recently, realistic data augmentation using neural networks especially generative neural networks (GAN) has achieved outstanding results. The communities main research focus is visual image processing. However, automotive cars and robots are equipped with a large suite of sensors to achieve a high redundancy. In addition to others, ultrasonic sensors are often used due to their low-costs and reliable near field distance measuring capabilities. Hence, Pattern recognition needs to be applied to ultrasonic signals as well. Machine Learning requires extensive data sets and those measurements are time-consuming, expensive and not flexible to hardware and environmental changes. On the other hand, there exists no method to simulate those signals deterministically. We present a novel approach for synthetic ultrasonic signal simulation using conditional GANs (cGANs). For the best of our knowledge, we present the first realistic data augmentation for automotive ultrasonics. The performance of cGANs allows us to bring the realistic environment simulation to a new level. By using setup and environmental parameters as condition, the proposed approach is flexible to external influences. Due to the low complexity and time effort for data generation, we outperform other simulation algorithms, such as finite element method. We verify the outstanding accuracy and realism of our method by applying a detailed statistical analysis and comparing the generated data to an extensive amount of measured signals.

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Section: Introductionmentioning

confidence: 99%

Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks

Popperli

Gulagundi

Yogamani

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…Such solution is based on handheld consumer devices using the Android platform and is capable of real-time operation with decimeter-order accuracy. Furthermore, in [14], a positioning system that allows for measuring range and bearing has been proposed, achieving an accuracy better than 10 cm in multipath environments.…”

Section: Introductionmentioning

confidence: 99%

TDoA based positioning using ultrasound signals and wireless nodes

Angelis

Moschitta

Comuniello

2017

2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Abstract-In this paper, a positioning technique based on Time Difference of Arrival (TDoA) measurements is analyzed. The proposed approach is designed to consent range and position estimation, using ultrasound transmissions of a stream of chirp pulses, received by a set of wireless nodes. A potential source of inaccuracy introduced by lack of synchronization between transmitting node and receiving nodes is identified and characterized. An algorithm to identify and correct such inaccuracies is presented.

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