•We investigate angle estimation for bistatic EMVS-MIMO radar.•A propagator method is derived for angle estimation.•The proposed algorithm is much efficient than the existing algorithms.•The proposed ...method brings more accurate angle estimation than the existing matrix-based methods.
In this Communication, we revisit the parameter estimation problem in a bistatic multiple-input multiple-output (MIMO) radar system with electromagnetic vector sensors (EMVS), and a modified algorithm is suggested. Firstly, the signal subspace is obtained via propagator method. Thereafter, automatically paired spatial angles are achieved by exploiting rotation invariant property as well as vector cross-product strategy. Finally, polarization parameters are calculated based on least squares technique. The suggested algorithm is analyzed in terms of identifiability, complexity, theoretical asymptotic error as well as Cramér-Rao bound (CRB). It is computationally-friendly and it offers automatically paired direction estimation. Moreover, it provides better estimation accuracy than state-of-the-art matrix-based methods. Simulation results verify the improvement of the proposed algorithm.
Multiple-input multiple-output (MIMO) is a technical hotspot in physical layer with numerous applications in wireless communications, radars, sonars, and well beyond. In this paper, we focus on the ...multi-dimensional angle estimation problem in a bistatic electromagnetic vector sensors (EMVS) MIMO system. Namely, we need to simultaneously estimate two-dimensional (2D) direction-of-arrival (DOA), 2D direction-of-departure (DOD), 2D receive polarization angle (RPA) and 2D transmit polarization angle (TPA). To tackle this issue, a parallel factor (PARAFAC) analysis-based estimator is proposed. Firstly, a third-order PARAFAC analysis data model is established, which can efficiently exploit the tensor structure of the array measurement. After performing PARAFAC decomposition on the tensor measurement, the factor matrices are achieved. By combining the estimation method of signal parameters via rotational invariance technique (ESPRIT) with the vector cross-product method, joint estimates of 2D-DOD, 2D-DOA, 2D-TPA and 2D-RPA are obtained without further pairing calculation. Compared with the state-of-the-art ESPRIT-Like approach, the proposed method can achieve better performance by enforcing the third-order structure information, and it is suitable for arbitrary array manifolds. Theoretical analyses are given and numerical results corroborate our analysis.
In this letter, we propose a generalized nested array (GNA) with two flexible co-prime factors for enlarging the inter-element spacing of two concatenated uniform linear subarrays. It is shown that ...both the prototype nested array and generalized co-prime array can be interpreted as special cases. The closed-form expressions for the range of consecutive lags and the number of unique lags are derived for any given factors. After optimization, GNA has the same number of degrees of freedom as (super) nested array but with reduced mutual coupling. Numerical simulations prove the superiority of proposed configuration using compressed sensing algorithm.
This paper addresses the issue of two-dimensional (2-D) direction of arrival (DOA) estimation with coprime planar arrays (CPPAs) via sparse representation. Our work differs from the partial spectral ...search approach 25, which suppresses the phase ambiguity by searching the common peaks of two subarrays. We focus on the coprime property of CPPA, where the sparse array extension model with sum-difference coarray (SDCA) is derived for larger degrees of freedom (DOFs). Besides, to optimize the selection of regularization parameter, we also construct a new sparse representation algorithm by estimating the errors between the signal and noise parts. Further, an iterative scheme is presented to transform the 2-D grids searching to several times of 1-D searching, where the initial values are obtained by extracting one difference coarray from SDCA. So the proposed method can achieve aperture extension, high estimation performance, and low computational complexity. Besides, the sparse array extension model for multiple-input multiple-output radars is discussed and the Cramér-Rao bound for 2-D DOA estimation with CPPA is also derived in detail. Finally, simulation results demonstrate the effectiveness of proposed method compared to the state-of-the-art methods.
Abstract
Grid theory is rather commonly-used through out the research of integer ambiguity. In order to promote the efficiency of computation, it is of great necessity to reduce the correlations of ...the grid basis through the reduction. The classical reduction algorithm is known as the LLL (Lenstra–Lenstra–Lovász) algorithm. So as to further enhance the reduction effect, the deep-insertion LLL algorithm can be utilized as an alternative to the basis vector exchange algorithm. In practice, the deep-insertion LLL algorithm can achieve a better reduction effect, but it requires more time for reduction. The PotLLL algorithm replaces the basis vector exchange condition of deep-insertion LLL with an improving in the basis quality, and it can run in polynomial time, but with certain limitations. Therefore, this article proposes a global deep-insertion PLLL algorithm (GS-PLLL) to address the issue of integer ambiguity. GS-PLLL adopts a global strategy for deep-insertion processing, and introduces a rotation sorting method for preconditioning the grid basis. Comparative evaluations were conducted using simulation experiments and real-world measurements on the LLL, DeepLLL, PotLLL, and GS-PLLL algorithms. The experimental results indicate that the GS-PLLL algorithm achieves a better reduction effect than the PotLLL algorithm while improving the efficiency of reduction.
Broomcorn millet (Panicum miliaceum L.) has strong tolerance to abiotic stresses, and is probably one of the oldest crops, with its earliest cultivation that dated back to ca. ~10,000 years. We ...report here its genome assembly through a combination of PacBio sequencing, BioNano, and Hi-C (in vivo) mapping. The 18 super scaffolds cover ~95.6% of the estimated genome (~887.8 Mb). There are 63,671 protein-coding genes annotated in this tetraploid genome. About ~86.2% of the syntenic genes in foxtail millet have two homologous copies in broomcorn millet, indicating rare gene loss after tetraploidization in broomcorn millet. Phylogenetic analysis reveals that broomcorn millet and foxtail millet diverged around ~13.1 Million years ago (Mya), while the lineage specific tetraploidization of broomcorn millet may be happened within ~5.91 million years. The genome is not only beneficial for the genome assisted breeding of broomcorn millet, but also an important resource for other Panicum species.
Multifunctional nanoplatforms with multimodal imaging and cancer therapy capabilities have attracted attention in biomedical applications. Near-infrared persistent luminescence nanoparticles (NPLNPs) ...were considered one of the most promising candidates for constructing multifunctional nanoplatforms due to the absence of in situ excitation and high signal-to-noise ratios (SNRs). Here, we report a novel NPLNP mSiO2@Gd3Ga5O12:Cr3+, Nd3+ (mSiO2@GGO) as multifunctional nanoplatforms for multimodal imaging and cancer therapy. These NPs exhibited a persistent luminescence (745 nm) of more than 3 h in the first near-infrared window (NIR-I) after UV excitation, which can realize high SNRs and long-term in vivo imaging. Moreover, these NPs showed excellent NIR luminescence (1067 nm) in the second near-infrared window (NIR-II) under 808 nm excitation, which is more suitable for deep tissue imaging due to the lower photon scattering and deeper tissue penetration of NIR-II luminescence. Furthermore, the host Gd3Ga5O12 with high Gd3+ concentration showed a high r1 value (10.70 mM−1 s−1) and was suitable for T1 MR imaging. The mesoporous silica nanoparticles (mSiO2) served as a framework to control the mSiO2@GGO particle morphology and provide low toxicity and drug loading capacity for cancer therapy.
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Persistent luminescence nanoparticles (PLNPs) are unique optical materials that emit afterglow luminescence after ceasing excitation. They exhibit unexpected advantages for in vivo optical imaging of ...tumors, such as autofluorescence-free, high sensitivity, high penetration depth, and multiple excitation sources (UV light, LED, NIR laser, X-ray, and radiopharmaceuticals). Besides, by incorporating other functional molecules, such as photosensitizers, photothermal agents, or therapeutic drugs, PLNPs are also widely used in persistent luminescence (PersL) imaging-guided tumor therapy. In this review, we first summarize the recent developments in the synthesis and surface functionalization of PLNPs, as well as their toxicity studies. We then discuss the in vivo PersL imaging and multimodal imaging from different excitation sources. Furthermore, we highlight PLNPs-based cancer theranostics applications, such as fluorescence-guided surgery, photothermal therapy, photodynamic therapy, drug/gene delivery and combined therapy. Finally, future prospects and challenges of PLNPs in the research of translational medicine are also discussed.
Abstract In this paper, near infrared-emitting long-persistence luminescent porous Zn1.1 Ga1.8 Ge0.1 O4 :Cr3+ , Eu3+ @SiO2 nanoprobes have been prepared using mesoporous silica nanospheres both as ...morphology-controlling templates and as vessels. These nanoprobes possessed an excellent capacity for drug delivery and allowed for real-time monitoring of the delivery routes of the drug carriers in vivo . The nanoprobes demonstrated a typical mesoporous structure, a brighter NIR emission (696 nm) and a long afterglow luminescence that persisted for 15 d. Furthermore, after surface modification with folic acid (FA), a tumor-targeting group, these nanoprobes exhibited an excellent ability to target tumors with high sensitivity in vitro and in vivo . Importantly, these modified nanoprobes could accurately diagnose tumors and allow for long-term tumor monitoring via in situ and in vivo re-excitation by a red LED lamp. Furthermore, the drug release data demonstrated that the modified nanoprobes could be loaded with a large amount of doxorubicin hydrochloride (DOX) and showed sustained release behavior. Together, the results of this study indicate that these nanoprobes can accurately diagnose tumors, allow for long-term in vivo and in situ monitoring and release DOX in situ to cure tumors.
Direction-of-arrival (DOA) estimation is the preliminary stage of communication, localization, and sensing. Hence, it is a canonical task for next-generation wireless communications, namely beyond 5G ...(B5G) or 6G communication networks. Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmW) bands are emerging technologies that can be implemented to increase the spectral efficiency of an area, and a number of expectations have been placed on them for future-generation wireless communications. Meanwhile, they also create new challenges for DOA estimation, for instance, through extremely large-scale array data, the coexistence of far-field and near-field sources, mutual coupling effects, and complicated spatial-temporal signal sampling. This article discusses various open issues related to DOA estimation for B5G/6G communication networks. Moreover, some insights on current advances, including arrays, models, sampling, and algorithms, are provided. Finally, directions for future work on the development of DOA estimation are addressed.
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