Single transceiver device-free indoor localization using ultrasound body reflections and walls

Gao

et al. 2020

Applied Sciences

Currently, indoor locations based on the received signal strength (RSS) of Wi-Fi are attracting more and more attention thanks to the technology’s low cost, low power consumption and wide availability in mobile devices. However, the accuracy of Wi-Fi positioning is limited, due to the signal fluctuation and indoor multipath interference. In order to overcome this problem, this paper proposes a robust and accurate Wi-Fi fingerprint location recognition method based on a deep neural network (DNN). A stacked denoising auto-encoder (SDAE) is used to extract robust features from noisy RSS to construct a feature-weighted fingerprint database offline. We use the combination of the weights of posteriori probability and geometric relationship of fingerprint points to calculate the coordinates of unknown points online. In addition, we use constrained Kalman filtering and hidden Markov models (HMM) to smooth and optimize positioning results and overcome the influence of gross error on positioning results, combined with characteristics of user movement in buildings, both dynamic and static. The experiment shows that the DNN is feasible for position recognition, and the method proposed in this paper is more accurate and stable than the commonly used Wi-Fi positioning methods in different scenes.

Section: Online Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Gao

et al. 2020

Applied Sciences

“…Many indoor localization methods have been proposed in academia as well as in industry. Typical indoor localization methods like Wi-Fi [4][5][6][7][8][9], Bluetooth [10], ultrasound (US) [11], infrared (IR) [12], radio frequency identification (RFID) [13,14], magnetic field (MF) [15], and ultra-wideband (UWB) [16] methods, have been investigated. Indoor positioning systems estimate the location by using different types of measurements, such as angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Mobile Information Systems

Fan

et al. 2020

Along with the advancement of wireless technology, indoor localization technology based on Wi-Fi has received considerable attention from academia and industry. The fingerprint-based method is the mainstream approach for Wi-Fi indoor localization and can be easily implemented without additional hardware. However, signal fluctuations constitute a critical issue pertaining to the extraction of robust features to achieve the required localization performance. This study presents a fingerprint feature extraction method commonly referred to as the Fisher score–stacked sparse autoencoder (Fisher–SSAE) method. Some features with low Fisher scores were eliminated, and the representative features were then extracted by the SSAE. Furthermore, this study establishes a hybrid localization model constructed with the use of the global model and the submodel to avoid significant coordinate localization errors attributed to subregional localization errors. Combined with three accessible fingerprint-based positioning methods, namely, the support vector regression, random forest regression, and the multiplayer perceptron classification, the experimental results demonstrate that the proposed methods improve the localization accuracy and response time compared to other feature extraction methods and the single localization model. Compared with some state-of-the-art methods, the proposed methods have better localization performances when large number of features are used.

“…Based on specific technology, it is possible to categorize methodologies for smartphone-based indoor pedestrian self-positioning systems into two distinct groups: (1) infrastructure-based systems that use auxiliary equipment or a cooperation between nodes to realize target tracking [1,2,3,4,5,6,7,8,9], and (2) the infrastructure-free systems that realize pedestrian self-positioning using only the information provided by the smartphone carried on one’s person [9,10,11,12,13,14,15,16,17,18,19,20]. However, when using the former, the pedestrian is likely to experience difficulties in position acquisition when the cooperative information is unavailable.…”

Section: Introductionmentioning

confidence: 99%

Indoor Pedestrian Self-Positioning Based on Image Acoustic Source Impulse Using a Sensor-Rich Smartphone

Song

Qiu

et al. 2018

Sensors

The ubiquity of sensor-rich smartphones provides opportunities for a low-cost method to track indoor pedestrians. In this situation, pedestrian dead reckoning (PDR) is a widely used technology; however, its cumulative error seriously affects its accuracy. This paper presents a method of combining infrastructure-free indoor acoustic self-positioning with PDR self-positioning, which verifies the rationality of PDR results through the acoustic constraint between a sound source and its image sources. We further determine the first-order echo delay measurements, thus obtaining the mobile user position. We verify that the proposed method can achieve a continuous self-positioning median error of 0.19 m, and the error probability below 0.12 m is 54.46%, which indicates its ability to eliminate PDR error, as well as its adaptability to environmental disturbances.