| 研究実績の概要 |
Unlike the previous systems leveraging off-the-shelf WiFi devices that dominantly utilized the RSSI metric as the indicator of channel-level SNR per packet, my research is dedicated to a complete new study on WLAN device-free awareness that we explore the viability of fine-grained detection using physical layer information provided by off-the-shelf WiFi infrastructure. State-of-art studies on device-bound indoor localization have demonstrated that accuracy can significantly increase to the granularity of 1m × 1m leveraging Channel State Information measurements (CSI) compared with RSSI-based scheme. However, regarding the device-bound localization techniques, this requirement that the subjects should carry wireless devices has several inherent disadvantages. First, tracking stops whenever the device is detached from the subject either accidentally or intentionally. Second, for applications such as eldercare, we cannot assume the subjects will always agree or remember to carry the device. Recognizing these limitations, the community has started the discussion on RF-based device-free passive (DfP) localization techniques. Therefore, we believe, with the adoption of metrics of subchannel-level SNRs, precise device-free localization or detection can also achieve.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
we present an accurate device-free passive (DfP) indoor location tracking system which adopts Channel- state Information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for MIMO-OFDM PHY layer parameters are exploited to improve localization and tracking accuracy. To enable precise positioning in the presence of heavy multipath effects in cluttered indoor scenarios, we experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based technique as an accurate solution. Our scheme further boosts the localization efficiency by using principal component analysis (PCA) to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we continuously track the trajectory of a moving subject. We have evaluated the performance of our system in four indoor environments and compared it with state-of-art indoor localization schemes. Our experimental results demonstrate that this complex channel information enables more accurate localization of non-equipped individuals.
we evaluate the performance of our system and compare it against other indoor localization systems including Nuzzer, PC-DfP and Pilot and experimental results show that our system can outperform the other systems.
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| 今後の研究の推進方策 |
In the project, we would like to address the following research questions: 1)to model the relationship between human activities and the blob shape and size in RTI;2) to mitigate the noise of RTI image induced by various factors, including RSS measurements, modeling inaccuracies, etc; 3) to design efficient signal processing and classification approaches for accurately distinguishing different human activities;4) to implement a RTI-based device-free prototype of human activity recognition and experimentally testify the recognition accuracy of our system in different indoor environments.
To study the proposed problems, we present the methodological procedures and techniques as follows in terms of work packages. WP1(10-15 weeks): Literature review I will review related literatures and explore possible open directions. WP2(4 weeks): RTI network installation and raw data collection During this phase, I will install RTI networks and collect raw data from different radio links. WP3(15-20 weeks): Data analysis I will first build suitable models for human activities related to RTI image. Then I will select or create efficient signal processing and classification approaches for accurate activity detection. WP4(15-20 weeks): Prototyping and evaluation I will implement a RTI device-free prototype for activity recognition and alsoconduct experiments to demonstrate the performance of the proposed system in (different) several typical indoor environments.
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