Browsing by Author "Xu, Feng"

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Diacetylene Linked Anthracene Oligomers Synthesized by One-Shot Homocoupling of Trimethylsilyl on Cu(111)
(2018-08-07) Kawai, Shigeki; Krejci, Ondrej; Foster, Adam; Pawlak, Rémy; Xu, Feng; Peng, Lifen; Orita, Akihiro; Meyer, Ernst
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
On-surface chemical reaction has become a very powerful technique to conjugate small precursor molecules and several reactions have been proposed with the aim to fabricate functional nanostructures on surfaces. Here we present an unforeseen adsorption mode of 9,10-bis-((trimethylsilyl)ethynyl)anthracene on a Cu(111)surface and the resulting one-shot desilylative homocoupling of of the adsorbate by annealing at 400 K. With a combination of high-resolution atomic force microscopy and density functional theory calculations, we found that the triple bonds and silicon atoms of the monomer chemically interact with the copper surface. After the oligomerization, we discovered that the anthracene units are linked to each other via buta-1,3-diynediyl fragments while keeping the surface clean. Furthermore, the force measurement revealed the chemical nature at the center of anthracene unit.
DOA Estimation for Transmit Beamspace MIMO Radar via Tensor Decomposition with Vandermonde Factor Matrix
(2022) Xu, Feng; Morency, Mathew W.; Vorobyov, Sergiy A.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
We address the problem of tensor decomposition in application to direction-of-arrival (DOA) estimation for two-dimensional transmit beamspace (TB) multiple-input multiple-output (MIMO) radar. A general higher-order tensor model that enables computationally efficient DOA estimation is designed. Whereas other tensor decomposition-based methods treat all factor matrices as arbitrary, the essence of the proposed DOA estimation method is to fully exploit the Vandermonde structure of the factor matrices to take advantage of the shift-invariance between and within different transmit subarrays. Specifically, the received signal of TB MIMO radar is expressed as a higher-order tensor. A computationally efficient tensor decomposition method is proposed to decompose the Vandermonde factor matrices of this signal tensor. The generators of the Vandermonde factor matrices are computed to estimate the phase rotations between subarrays, which can be utilized as a look-up table for finding targets DOA. The proposed tensor signal model as well as the DOA estimation algorithm are also straightforwardly applicable for the one-dimensional TB MIMO radar case. It is further shown that our proposed approach can be used in a more general scenario where the transmit subarrays with arbitrary but identical configuration can be non-uniformly displaced. We also show that both the tensor rank of the signal tensor and the matrix rank of a particular matrix derived from the signal tensor are identical to the number of targets. Thus, the number of targets can be estimated via matrix rank determination. Simulation results illustrate the performance improvement of the proposed DOA estimation method as compared to other tensor decomposition-based techniques for TB MIMO radar.
An Iterative 2-D DOA Estimation Approach for L-shaped Nested Array
(2023-04-01) Xu, Feng; Vorobyov, Sergiy A.
A4 Artikkeli konferenssijulkaisussa
The problem of two-dimensional (2-D) direction of arrival (DOA) estimation for L-shaped nested array is considered, and an iterative approach based on tensor modeling is proposed. To develop such an approach, a high-order tensor is designed to collect the received signals of the subarrays in difference co-array domain. By exploiting the Vandermonde structure of the factor matrices, the sources azimuth and elevation angles can be estimated via tensor decomposition. The cross term of the received signal, which seriously degrades the estimation performance, can be estimated and removed by iterative procedure using also the DOA estimates obtained by tensor decomposition. Consequently, the 2-D DOA estimation performance can be improved gradually after sufficiently many iterations. Simulation results validate the proposed 2-D DOA estimation approach and demonstrate its effectiveness.
Joint DOD and DOA Estimation in Slow-Time MIMO Radar via PARAFAC Decomposition
(2020-01-01) Xu, Feng; Vorobyov, Sergiy A.; Yang, Xiaopeng
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
We develop a new tensor model for slow-time multiple-input multiple-output (MIMO) radar, and apply it for joint direction-of-departure (DOD), and direction-of-arrival (DOA) estimation. This tensor model aims to exploit the independence of phase modulation matrix, and receive array in the received signal for slow-time MIMO radar. Such tensor can be decomposed into two tensors of different ranks, one of which has identical structure to that of the conventional tensor model for MIMO radar, and the other contains all phase modulation values used in the transmit array. We then develop a modification of the alternating least squares algorithm to enable parallel factor decomposition of tensors with extra constants. The Vandermonde structure of the transmit, and receive steering matrices (if both arrays are uniform, and linear) is then utilized to obtain angle estimates from factor matrices. The multi-linear structure of the received signal is maintained to take advantage of tensor-based angle estimation algorithms, while the shortage of samples in Doppler domain for slow-time MIMO radar is mitigated. As a result, the joint DOD, and DOA estimation performance is improved as compared to existing angle estimation techniques for slow-time MIMO radar. Simulation results verify the effectiveness of the proposed method.
Tensor-Based 2D DOA Estimation for L-Shaped Nested Array
(2024-02) Xu, Feng; Zheng, Hang; Vorobyov, Sergiy A.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Among various sensor array configurations, the L-shaped nested array offers improved performance for 2-D direction-of-arrival (DOA) estimation through co-array processing. However, conventional methods overlook the multidimensional signal structure and fail to eliminate the cross term generated from the correlated co-array signal and noise components. It leads to a significant degradation in DOA estimation performance. To deal with this problem, an iterative 2-D DOA estimation algorithm based on tensor modeling is proposed. It is capable of eliminating the cross term. Specifically, the co-array signals of virtual subarrays in orthogonal directions are derived and concatenated to construct a higher order tensor, whose factor matrices have the Vandermonde structure and preserve the interconnected azimuth and elevation information. A computationally efficient tensor decomposition method is then developed to independently estimate the azimuth and elevation angles, which are effectively paired using the spatial cross-correlation matrix. Furthermore, after investigating the cross term effect, a two-step iterative algorithm is proposed to sequentially estimate and remove the cross term based on the initial estimates obtained from the high-order tensor decomposition. Consequently, the 2-D DOA estimation with enhanced estimation accuracy, resolution, and moderate computational complexity is achieved for the L-shaped nested array. Simulation results demonstrate the superiority of the proposed algorithm over competing methods.
Transmit Energy Focusing for Parameter Estimation in Slow-time Transmit Beamspace L-shaped MIMO Radar
(2024-11-06) Zhang, Tingting; Vorobyov, Sergiy A.; Xu, Feng
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
We present a novel slow-time transmit beamspace (TB) multiple-input multiple-output (MIMO) technique for L-shaped array radar with uniform linear subarrays to estimate target parameters including 2-dimensional (2-D) directions of arrival (DOA) and unambiguous velocity. Doppler division multiple access (DDMA) approach, as a type of slow-time waveform achieving waveform orthogonality across multiple pulses within a coherent processing interval, disperses the transmit energy over the entire spatial region, suffering from beam-shape loss. Moreover, Doppler spectrum division, which is necessary for transmit channel separation prior to parameter estimation, leads to the loss of crucial information for velocity disambiguation. To optimize transmit energy distribution, slow-time TB technique is proposed to focus the energy within a desired spatial region. Unlike DDMA approach, slow-time TB technique divides the entire Doppler spectrum into more subbands than the number of transmit antenna elements to narrow down the beam mainlobe intervals between adjacent beams formed by DDMA modulation vectors. As a result, more beams are incorporated into the region of interest, and slow-time TB radar can direct transmit energy to the region of interest by properly selecting the DDMA modulation vectors whose beams are directed there. To resolve velocity ambiguity, tensor signal modeling, by storing measurements in a tensor without Doppler spectrum division, is used. Parameter estimation is then addressed using canonical polyadic decomposition (CPD), and the performance of slow-time TB L-shaped MIMO radar is shown to be improved as compared to DDMA MIMO techniques. Simulations are conducted to validate the proposed method.
Transmit Energy Focusing For Parameter Estimation in Transmit Beamspace Slow-Time MIMO Radar
(2023) Zhang, Tingting; Xu, Feng; Vorobyov, Sergiy A.
A4 Artikkeli konferenssijulkaisussa
Recently, Parallel Factor-Direct (PARAFAC-Direct) method has been proposed for parameter estimation including velocity disambiguation for Doppler Division Multiple Access (DDMA) Multiple-Input Multiple-Output (MIMO) radar. However, DDMA MIMO radar spreads the overall transmit energy into the entire spatial region, and therefore, suffers from beam-shape loss that can limit the performance of PARAFAC-Direct method. To solve this problem, a Transmit Beamspace (TB) Slow-Time MIMO (ST-MIMO) approach is proposed that focuses the transmit energy within a desired spatial region. Unlike traditional DDMA MIMO radars, the Doppler spectrum is divided into more subbands than the number of transmit elements to reduce the mainlobe intervals between adjacent beams formed by DDMA modulation vectors. Then, the TB ST-MIMO beam set can be directed to the spatial region of interest via a proper selection of DDMA modulation vectors. Parameter estimation performance of TB ST-MIMO is improved as compared to conventional DDMA MIMO techniques. Simulations are conducted to validate the proposed method.