R-PMD: robust passive motion detection using PHY information with MIMO

Zhu, Hai; Fu, Xiao; Sun, Lijuan; Xie, Xiaohui; Yang, Panlong; Wang, Ruchuan

doi:10.1109/pccc.2015.7447918

Cited by 7 publications

(11 citation statements)

References 15 publications

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“…To be concise, we denoted the method we used in our previous work [14] as "WMA" and the method we used in this study as "WMA+PCA". We focused on the following main metrics to evaluate our detection scheme: (1) True Positive (TP) for the probability that the human motion events are correctly detected; (2) False Positive (FP) for the fraction of cases in which the system announced a "detected" event when there was no one moving; (3) True Negative (TN) for the probability that the static environment was correctly detected; (4) False Negative (FN) for the fraction of cases in which the system failed to detect human motion.…”

Section: Experimental Methodologymentioning

confidence: 99%

“…In contrast to the direct feature extraction from each subcarrier performed in Ref. [14], in this study, we applied PCA on the primary denoised CSI sequence to extract more representative signals. Specifically, we let H i be the 30 1 dimensional vector which contains the WMA-filtered CSI values for the i -th packet and F is a K 30 dimensional matrix representing the CSI values for K consecutive packets.…”

Section: Pca-based Filteringmentioning

confidence: 99%

“…We can then obtain a single value from the equation above. With the sliding window moving m times, we obtain a 1 m matrix T. In our previous work [14] , we calculated the variances for 30 filtered CSI sequences and obtained a 30 m matrix, in which each row represented the variance of a subcarrier. Figure 9 shows the probability distribution histograms of CSI variance of the first subcarrier corresponding to Fig.…”

Section: Modeling the Csi Amplitude Featurementioning

confidence: 99%

“…7, although the variances over different subcarriers differ in absolute value, they all become dispersed under the influence of human motion. In our previous work [14] , we had therefore calculated the EMD for each subcarrier before adding the N D 30 EMDs into a single distance to obtain the overall dissimilarity, in which the size reflects the degree to which the environment is abnormal. In this paper, since we select only the second and third principal components for DfP detection, we simply aggregate the EMDs for P 2 and P 3 as the final distance , which greatly reduces the computational complexity of our system.…”

Section: Motion Detectionmentioning

confidence: 99%

“…Based on our previous work in Ref. [14], we improve the performance of DfP detection by adopting a novel Principal Component Analysis (PCA [15] )based CSI filtering technique and considering different combinations of antennas. Our main contributions are summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Robust and passive motion detection with COTS WiFi devices

Zhu

Xiao

Sun

et al. 2017

Tinshhua Sci. Technol.

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Device-free Passive (DfP) detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength (RSS), most recent studies rely on finer-grained Channel State Information (CSI). However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight (LOS) or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection (R-PMD). Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis (PCA)-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.

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Section: Experimental Methodologymentioning

confidence: 99%

Section: Pca-based Filteringmentioning

confidence: 99%

Section: Modeling the Csi Amplitude Featurementioning

confidence: 99%

Section: Motion Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust and passive motion detection with COTS WiFi devices

Zhu

Xiao

Sun

et al. 2017

Tinshhua Sci. Technol.

Self Cite

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Temporal-Frequency Attention-Based Human Activity Recognition Using Commercial WiFi Devices

et al. 2020

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Human activity recognition has been growing for decades in a variety of technological disciplines. However, in the existing WiFi-based human activity recognition systems, there are the following problems: Firstly, in the processing of channel state information (CSI) data, mainly for the removal of noise in the superimposed signal, there is no effective removal of useless multipath signals; Secondly, the data segmentation algorithm based on the empirical threshold requires manual adjustment of the threshold in different environments, resulting in poor robustness and unstable segmentation; Thirdly, simple learning classification is applied without specific design for CSI data structure and sufficiently abstracting information features. In this paper, a device-free human activity recognition system with a temporalfrequency attention mechanism is proposed, which can be deployed on commercial WiFi devices to identify human's daily activities. Firstly, the multipath signal affected by the channel change is extracted by using the difference of the propagation delay of different multipath, thereby eliminating the delay and invalid multipath signals that have undergone multiple reflections and refractions. Secondly, a neural network model based on attention mechanism is proposed, which assigns different weights to different characteristics and sequences by imitating the human brain to dedicate more attention to important information. Then, the long short-term memory (LSTM) model is used to learn the correlation features of different dimensions to realize human activity recognition. Finally, the system performance is evaluated in different environments, and the experimental results show that our syetem holds a better performance in both line-of-sight (LOS) and non-line-of-sight (NLOS) than the existing human activity recognition systems.

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Robust Device-Free Proximity Detection Using Wifi

Ozturk

Zhang

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Motion detection based on WiFi signals has gained much attention recently. However, most of the existing approaches can only detect motion in a large coverage area without knowing how far the target motion happens. In this paper, we propose two robust and responsive features in the frequency dimension, which are sensitive to the distance of motion, and establish the connection between the underlying radio propagation properties and the features. Extensive experiments in various environments demonstrate that the proposed proximity detection scheme can achieve true positive rates greater than 90% and 98% in corridor and room scenarios, respectively, while maintaining the corresponding false positive rates less than 5% and 1%. The responsiveness of the proposed scheme is verified by measured detection delays within 1.5 s for a detection distance of 2 m.

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R-PMD: robust passive motion detection using PHY information with MIMO

Cited by 7 publications

References 15 publications

Robust and passive motion detection with COTS WiFi devices

Robust and passive motion detection with COTS WiFi devices

Temporal-Frequency Attention-Based Human Activity Recognition Using Commercial WiFi Devices

Robust Device-Free Proximity Detection Using Wifi

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