Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems
AbstractIn health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm. View Full-Text
Share & Cite This Article
Wan, L.; Han, G.; Wang, H.; Shu, L.; Feng, N.; Peng, B. Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems. Sensors 2016, 16, 368.
Wan L, Han G, Wang H, Shu L, Feng N, Peng B. Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems. Sensors. 2016; 16(3):368.Chicago/Turabian Style
Wan, Liangtian; Han, Guangjie; Wang, Hao; Shu, Lei; Feng, Nanxing; Peng, Bao. 2016. "Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems." Sensors 16, no. 3: 368.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.