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| 2. |
- Luo, Jiawei, et al.
(författare)
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Online Sparse DOA Estimation Based on Sub–Aperture Recursive LASSO for TDM–MIMO Radar
- 2022
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:9
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Tidskriftsartikel (refereegranskat)abstract
- The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple– output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high– computational complexity when dealing with massive TDM–MIMO observations, due to high– dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from O( K3) to O( K2). Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method.
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| 3. |
- Földváry Ličina, Veronika, et al.
(författare)
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Development of the ASHRAE Global Thermal Comfort Database II
- 2018
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Ingår i: Building and Environment. - : Elsevier BV. - 0360-1323. ; 142, s. 502-512
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Tidskriftsartikel (refereegranskat)abstract
- Recognizing the value of open-source research databases in advancing the art and science of HVAC, in 2014 the ASHRAE Global Thermal Comfort Database II project was launched under the leadership of University of California at Berkeley's Center for the Built Environment and The University of Sydney's Indoor Environmental Quality (IEQ) Laboratory. The exercise began with a systematic collection and harmonization of raw data from the last two decades of thermal comfort field studies around the world. The ASHRAE Global Thermal Comfort Database II (Comfort Database), now an online, open-source database, includes approximately 81,846 complete sets of objective indoor climatic observations with accompanying “right-here-right-now” subjective evaluations by the building occupants who were exposed to them. The database is intended to support diverse inquiries about thermal comfort in field settings. A simple web-based interface to the database enables filtering on multiple criteria, including building typology, occupancy type, subjects' demographic variables, subjective thermal comfort states, indoor thermal environmental criteria, calculated comfort indices, environmental control criteria and outdoor meteorological information. Furthermore, a web-based interactive thermal comfort visualization tool has been developed that allows end-users to quickly and interactively explore the data.
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| 4. |
- Zhang, Yongchao, et al.
(författare)
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High-Throughput Hyperparameter-Free Sparse Source Location for Massive TDM-MIMO Radar : Algorithm and FPGA Implementation
- 2023
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892. ; 61
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Tidskriftsartikel (refereegranskat)abstract
- The sparse iterative covariance-based estimation (SPICE) algorithm is promising for hyperparameter-free sparse source location for time-division-multiplexing multiple-input-multiple-output (TDM-MIMO) radar systems, with well-documented merits in resolution enhancement and sidelobe suppression. Regrettably, the method typically requires a large number of iterations to converge, each requiring high-dimensional matrix operations, rendering the existing batch SPICE method impractical and expensive to implement in hardware when dealing with massive TDM-MIMO observations. In order to enable real-time processing, this article presents a subaperture-recursive (SAR) SPICE method, allowing for recursively refining the location parameters for each received (RX) block observation that becomes available sequentially in time. The proposed method not only offers the same benefits as the batch SPICE method but also allows for computationally efficient online processing, without the need for high-dimensional matrix operations, notably reducing the required hardware resources as well as processing time. We further present a high-throughput architecture for the resulting method on an XCZU15EG-FFVB1156 field-programmable gate array (FPGA). In combination with simulation results, we demonstrate the effectiveness through experimental data measured by a cascaded MIMO radar system with 12 transmit (TX) and 16 receive (RX) antennas, demonstrating that the computational time of resolving closely spaced sources on 256 predefined grid points can be processed in merely 12 ms.
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| 5. |
- Zhang, Yongchao, et al.
(författare)
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Online Sparse Reconstruction for Scanning Radar Using Beam-Updating q-SPICE
- 2022
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - 1545-598X. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- The generalized sparse iterative covariance-based estimation ( $q$ -SPICE) algorithm was recently introduced for scanning radar applications, resulting in substantial improvements in the angular resolution and quality of the processed images. Regrettably, the computational complexity and storage cost are high and quickly increase with growing data size, limiting the applicability of the estimator. In this letter, we strive to alleviate this problem, deriving a beam-updating $q$ -SPICE algorithm, allowing for efficiently updating of the sparse reconstruction result for each online radar measurement along the scanned beam. The resulting method is a regularized extension of the current online $q$ -SPICE implementation, which not only offers constant computational and storage cost, independent of the data size, but also provides enhanced robustness over the current online $q$ -SPICE. Our experimental assessment, conducted using both simulated and real data, demonstrates the advantage of the beam-updating $q$ -SPICE method in the task of sparse reconstruction for scanning radar.
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| 6. |
- Zhang, Yongchao, et al.
(författare)
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Sparse source location for real aperture radar using generalized sparse covariance fitting
- 2017
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Ingår i: 2017 IEEE Radar Conference, RadarConf 2017. - 9781467388238 ; , s. 1069-1074
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Konferensbidrag (refereegranskat)abstract
- Source location for real aperture radar (RAR) has raised many concerns in the fields of ground-based monitoring for aircrafts and vessels. Notably, the resolution of RAR in azimuth is constrained by the antenna beam width, which results in low degree of location accuracy. In this paper, we exploit the inherent sparseness of the target distributions to formulate a superresolution methodology to locate the observed sources. Making use of a recently developed generalized sparse covariance fitting technique, we show that the resulting estimator enjoys improved resolution and higher location accuracy as compared with the RAR system and other recent superresolution algorithms.
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| 7. |
- Zhang, Yongchao, et al.
(författare)
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Wideband Sparse Reconstruction for Scanning Radar
- 2018
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892. ; 56:10, s. 6055-6068
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Tidskriftsartikel (refereegranskat)abstract
- Recently, the generalized sparse iterative covariance-based estimation algorithm was extended to allow for varying norm constraints in scanning radar applications. In this paper, further to this development, we introduce a wideband dictionary framework which can provide a computationally efficient estimation of sparse signals. The technique is formed by initially introducing a coarse grid dictionary constructed from integrating elements, spanning bands of the considered parameter space. After forming estimates of the initially activated bands, these are retained and refined, whereas nonactivated bands are discarded from the further optimization, resulting in a smaller and zoomed dictionary with a finer grid. Implementing this scheme allows for reliable sparse signal reconstruction, at a much lower computational cost as compared to directly forming a larger dictionary spanning the whole parameter space. Simulation and real data processing results demonstrate that the proposed wideband estimator offers significant computational savings, without noticeable loss of performance.
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| 8. |
- Alimena, Juliette, et al.
(författare)
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Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider
- 2020
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Ingår i: Journal of Physics G. - : IOP Publishing. - 0954-3899 .- 1361-6471. ; 47:9
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Tidskriftsartikel (refereegranskat)abstract
- Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton-proton collision. Such LLP signatures are distinct from those of promptly decaying particles that are targeted by the majority of searches for new physics at the LHC, often requiring customized techniques to identify, for example, significantly displaced decay vertices, tracks with atypical properties, and short track segments. Given their non-standard nature, a comprehensive overview of LLP signatures at the LHC is beneficial to ensure that possible avenues of the discovery of new physics are not overlooked. Here we report on the joint work of a community of theorists and experimentalists with the ATLAS, CMS, and LHCb experiments-as well as those working on dedicated experiments such as MoEDAL, milliQan, MATHUSLA, CODEX-b, and FASER-to survey the current state of LLP searches at the LHC, and to chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the high-luminosity LHC. The work is organized around the current and future potential capabilities of LHC experiments to generally discover new LLPs, and takes a signature-based approach to surveying classes of models that give rise to LLPs rather than emphasizing any particular theory motivation. We develop a set of simplified models; assess the coverage of current searches; document known, often unexpected backgrounds; explore the capabilities of proposed detector upgrades; provide recommendations for the presentation of search results; and look towards the newest frontiers, namely high-multiplicity 'dark showers', highlighting opportunities for expanding the LHC reach for these signals.
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| 9. |
- Zhang, Yongchao, et al.
(författare)
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Online High Resolution Stochastic Radiation Radar Imaging using Sparse Covariance Fitting
- 2019
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Ingår i: IGARSS 2019 : 2019 IEEE International Geoscience and Remote Sensing Symposium - 2019 IEEE International Geoscience and Remote Sensing Symposium. - 9781538691557 - 9781538691540 ; , s. 8562-8565
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Konferensbidrag (refereegranskat)abstract
- Stochastic radiation radar (SRR) systems allow for the forming of radar images by transmitting stochastic signals to form the stochastic radiation field and thereby increase the target observation information to achieve high resolution imaging. In this paper, we examine the use of the online SParse Iterative Covariance-based Estimation (SPICE) algorithm to suppress the noise and improve the operational efficiency. The SPICE algorithm is based on a weighted covariance fitting criterion, and has recently been generalized to allow for an improved reconstruction performance. The used online extension can take advantage of echoes non-correlation along time, allowing for updating the imaging result through successive echo sequences. The simulation results verify the superior performance of the resulting estimator as compared to other recent SRR imaging methods.
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| 10. |
- Zhang, Yongchao, et al.
(författare)
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Range-Recursive IAA for Scanning RadarAngular Super-Resolution
- 2017
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - 1545-598X. ; 14:10, s. 1675-1679
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Tidskriftsartikel (refereegranskat)abstract
- Recently, the iterative adaptive approach (IAA) was adopted to allow for the estimation of high-resolution scanning radar images. In this letter, we further develop this approach by introducing a range-recursive IAA (IAA-RR) formulation allowing for a computationally efficient updating of the resulting estimates along range. Besides exploiting the rich matrix structure to mitigate the computational complexity for each iteration, the correlation between adjacent range cells is exploited to accelerate the convergence of the IAA iterations. When an additional range measurement becomes available, further acceleration is available by exploiting the estimates already formed for the adjacent range cells. Compared with the existing fast IAA implementation, the proposed IAA-RR is shown to offer significant computational savings, without noticeable loss in performance. Numerical results illustrate the superior performance of the proposed IAA-RR algorithm.
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