Abstract
In 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.
Highlights
Wearable health-monitoring systems (WHMS) have emerged as an effective way of improving the performance of remote diagnoses and patients’ monitoring [1]
The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the patient’s location
Estimate the covariance matrix R ID according to Equation (25); Take the eigenvalue decomposition (EVD) of R ID ; Qs is a diagonal matrix with the entries of 3K ID ; large eigenvalues of R ID, Us ∈ C M×3K ID are the corresponding signal subspace; 3: Divide the URA into three sub-arrays; calculate the selection matrix according to Equations (A5)–(A7); construct two different rotational invariant relationships according to Equation (46); 4: Construct new matrix Ũ ID1 ; perform the EVD of Equation (31), partitioning the matrix Ux into four blocks; the eigenvalues of V1 and V2 can be obtained according to Equation (33); 5: Estimate 2D DOA for ID sources according to Equations (38) and (39)
Summary
Wearable health-monitoring systems (WHMS) have emerged as an effective way of improving the performance of remote diagnoses and patients’ monitoring [1]. For 2D DOA estimation, the sequential one-dimensional searching (SOS) method has been proposed based on uniform circular arrays (UCA) [21]. We adopt the angulation positioning method for patients’ (wearable sensors) localizations based on three BSs with the cooperation of multiple VMIMO systems. We propose a 2D DOA estimation algorithm under the coexistence of ID and CD sources. Based on first-order Taylor series expansion of the steering vector and signal subspace algorithm, we extend the ESPRIT to 2D DOA estimation. Three sub-arrays are constructed to form two rotational invariant relationships, and the ESPRIT algorithm is used for DOA estimation of ID sources. The symbol blkdiag{Z1 , Z2 } stands for a block diagonal matrix, whose diagonal entries are matrices Z1 and Z2
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