4.6 Article

The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location

期刊

SENSORS
卷 22, 期 14, 页码 -

出版社

MDPI
DOI: 10.3390/s22145364

关键词

channel state information; channel estimation; compressive sensing

资金

  1. National Key Research and Development Program of China [2018YFB0505202, XJZK201802]
  2. Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN202003407]
  3. Research Project of Shanghai Polytechnic University [EDG21QD15]
  4. chongqing nation science Foundation of China [cstc2020jcyj-msxmX0753]

向作者/读者索取更多资源

In this paper, a WiFi-based indoor positioning method using CSI is proposed. The method utilizes dictionary filtering and compressive sensing to extract channel impulse response and estimate time of arrival, and employs median value filtering algorithm for target device localization. Experimental results demonstrate the superior performance of the method, enabling fast system deployment.
With the increasing demand for wireless location services, it is of great interest to reduce the deployment cost of positioning systems. For this reason, indoor positioning based on WiFi has attracted great attention. Compared with the received signal strength indicator (RSSI), channel state information (CSI) captures the radio propagation environment more accurately. However, it is necessary to take signal bandwidth, interferences, noises, and other factors into account for accurate CSI-based positioning. In this paper, we propose a novel dictionary filtering method that uses the direct weight determination method of a neural network to denoise the dictionary and uses compressive sensing (CS) to extract the channel impulse response (CIR). A high-precision time-of-arrival (TOA) is then estimated by peak search. A median value filtering algorithm is used to locate target devices based on the time-difference-of-arrival (TDOA) technique. We demonstrate the superior performance of the proposed scheme experimentally, using data collected with a WiFi positioning testbed. Compared with the fingerprint location method, the proposed location method does not require a site survey in advance and therefore enables a fast system deployment.

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