This paper aims at utilization of computational advantages of spice algorithm in the Optical domain for analyzing the optical network links with DWDM capability. Proposed methodology focuses on computing Power levels,...
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ISBN:
(纸本)9781479987924
This paper aims at utilization of computational advantages of spice algorithm in the Optical domain for analyzing the optical network links with DWDM capability. Proposed methodology focuses on computing Power levels, Dispersion and Noise values throughout a given optical link by using the Nodal Analysis technique used in spice algorithm which is an optimal computing technique in electrical domain. An example DWDM optical link is considered and detailed matrix formation and its solving method using LU factorization is explained for computing the parameters given above.
Aiming at the problem of low-altitude windshear wind speed estimation for airborne weather radar without independent identically distributed (IID) training samples, this paper proposes a low-altitude windshear wind sp...
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Aiming at the problem of low-altitude windshear wind speed estimation for airborne weather radar without independent identically distributed (IID) training samples, this paper proposes a low-altitude windshear wind speed estimation method based on knowledge-aided sparse iterative covariance-based estimation STAP (KAspice-STAP). Firstly, a clutter dictionary composed of clutter space-time steering vectors is constructed using prior knowledge of the distribution position of ground clutter echo signals in the space-time spectrum. Secondly, the spice algorithm is used to obtain the clutter covariance matrix iteratively. Finally, the STAP processor is designed to eliminate the ground clutter echo signal, and the wind speed is estimated after eliminating the ground clutter echo signal. The simulation results show that the proposed method can accurately realize a low-altitude windshear wind speed estimation without IID training samples.
This paper introduces a new class of auto-calibration algorithms, based on a generalized least-squares (GLS) cost function. This class supports both gain-phase calibration and mutual coupling calibration and it is ind...
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This paper introduces a new class of auto-calibration algorithms, based on a generalized least-squares (GLS) cost function. This class supports both gain-phase calibration and mutual coupling calibration and it is independent of array shape. The GLS cost function is minimized cyclically with respect to the direction of arrivals (DOA) and calibration parameters. These two minimization steps are repeated until convergence. In the DOA estimation step, minimization leads to the spice (SParse Iterative Covariance-based Estimation) DOA estimation algorithm. In fact, the innovation of this paper mainly lies in the calibration step. In the DOA estimation step, a generalized form of the spice algorithm should be used in the coherent sources case. Consequently, the proposed auto-calibration algorithm is able to support coherent source cases. Moreover, the proposed algorithm does not need multi-dimensional search. The necessary condition for the existence of a solution is studied as well. Using simulations, the performance of the proposed algorithm is compared with several other auto-calibration algorithms. Simulation results show that the proposed algorithm is more robust than the other algorithms and usually has better performance. (C) 2019 Elsevier GmbH. All rights reserved.
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theo...
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This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical a"" (1) minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth a"" (0) minimization, and the Sparse Iterative Covariance-Based Estimator, spice and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.
The results from L1-Endmembers display the algorithm's stability and accuracy with increasing levels of noise. The algorithm was extremely stable in the number of endmembers when compared to the spice algorithm an...
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ISBN:
(纸本)9781424495665
The results from L1-Endmembers display the algorithm's stability and accuracy with increasing levels of noise. The algorithm was extremely stable in the number of endmembers when compared to the spice algorithm and the Virtual Dimensionality methods for estimating the number of endmembers. Furthermore, the results shown for this algorithm were generated with the same parameter set for all of the data sets, from two-dimensional data to 51-dimensional real hyperspectral data. This indicates L1-Endmembers may lack of sensitivity to parameter value settings. The L1-Endmembers algorithm requires several quadratic programming steps per iteration. These can be completed directly in quadratic programming software packages such as CPLEX and take advantage of any running time reductions the software packages provide. Investigations will be conducted into whether the specific form of this algorithm, particularly with respect to the constraints on the abundance values, can be used to reduce the running time.
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