A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by channel sounders at millimeter-wave frequencies. Sounders equipped...
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A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by channel sounders at millimeter-wave frequencies. Sounders equipped with an omni-directional transmitter and a receiver with a uniform planar array (UPA) are considered. An accurate mathematical model is developed for the spatial frequency response of the sounder that incorporates the non-ideal cross-polar beampatterns for the UPA elements. Due to the limited Field-of-View (FoV) of each element, the model is extended to accommodate multi-FoV measurements in distinct azimuth directions. A beamspace representation of the spatial frequency response is leveraged to develop three progressively complex algorithms aimed at solving the single-snapshot maximum likelihood estimation problem: greedy matching pursuit (CLEAN), space-alternative generalized expectation-maximization (SAGE), and RiMAX. The first two are based on purely specular MPCs whereas RiMAX also accommodates diffuse MPCs. Two approaches for performance evaluation are proposed, one with knowledge of ground truth parameters, and one based on reconstruction mean-squared error. The three algorithms are compared through a demanding channel model with hundreds of MPCs and through real measurements. The results demonstrate that CLEAN gives quite reasonable estimates which are improved by SAGE and RiMAX. Lessons learned and directions for future research are discussed.
Multi-path components (MPCs) in wireless channels generally occur in clusters, i.e., groups of MPCs that have similar delay/angle characteristics. However, when those clusters are widely separated and have significant...
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ISBN:
(数字)9781665494557
ISBN:
(纸本)9781665494557
Multi-path components (MPCs) in wireless channels generally occur in clusters, i.e., groups of MPCs that have similar delay/angle characteristics. However, when those clusters are widely separated and have significantly different power, high-resolution parameter extraction (HRPE) algorithms based on serial interference cancellation, such as CLEAN, can miss some of the weaker clusters because they concentrate the path search in the strongest cluster. This effect can occur particularly in the presence of calibration error and/or diffuse scattering. To solve this problem, we propose a heuristic modification, Regional CLEAN (R-CLEAN) that employs cluster identification in the Fourier domain and limits the number of MPCs per cluster. We first demonstrate the effect, and the effectiveness of our proposed algorithm, on synthetic channels with calibration error or diffuse scattering. We then demonstrate it with a THz Multiple-Input-Multiple-Output (MIMO) measurement at 145 - 146 GHz. The proposed optimization and algorithm can thus be an essential step towards evaluating channels with multiple clusters.
high-resolution algorithms to estimate the depth of near-surface interface using ground-penetrating radar (GPR) can provide a measurement of thin to thick top layer using synthetic data for surface-coupled antennas. I...
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high-resolution algorithms to estimate the depth of near-surface interface using ground-penetrating radar (GPR) can provide a measurement of thin to thick top layer using synthetic data for surface-coupled antennas. In practice, the air-coupled antenna configuration in the target depth estimation needs to determine the air-gap distance between the antenna module and the surface. In this study, the authors present an improved approach of estimating the antenna height for measuring thin to thick top coal layer using the high-resolution algorithms. In GPR, backscattered echoes are generally correlated in nature. In order to apply high-resolution algorithms, the authors use two improved spatial smoothing techniques for fighting against the correlation between echoes. Here, in the existence of air-coal-shale-clay and air-coal-shale-coal interface, step frequency continuous wave GPR is used to measure the thickness of the coal layer. Simulation results on the antenna height estimation using synthetic data models, full-wave model and plane wave model are shown to assess the estimation algorithm's effectiveness.
The location of a single point source in infrared imaging is typically achieved through conventional methods such as centroiding. More challenging problems with multiple point sources require alternative location-find...
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The location of a single point source in infrared imaging is typically achieved through conventional methods such as centroiding. More challenging problems with multiple point sources require alternative location-finding methods with the potential of resolving closely spaced objects. The authors introduce an algorithm predicated on least-squared-error (LSE) modeling with a Gram-Schmidt orthogonalization step. Its noise performance is compared with two other high-resolution algorithms based on the eigendecomposition of the input data. Estimates obtained through the LSE modeling approached the Cramer-Rao lower bound for high signal-to-noise ratios. However, its performance is severely degraded in the presence of non-Gaussian noise. An outlier detection scheme that may be used in conjunction with the location and amplitude estimation procedure is described. Its effectiveness is demonstrated through Monte Carlo simulations.
作者：
KloucheDjedid, AATR
Adaptive Communications Research Laboratories Kyoto-fu 619-02 Japan
high-resolution algorithms for the detection and estimation of Directions Of Arrival (DOA) such as MUSIC, lead to accurate results but require the computation of the noise-subspace through an expensive covariance matr...
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high-resolution algorithms for the detection and estimation of Directions Of Arrival (DOA) such as MUSIC, lead to accurate results but require the computation of the noise-subspace through an expensive covariance matrix eigendecomposition. Adaptive estimators of the noise-subspace can be very useful in a non-stationary environment when the convergence is possible with a few number of snapshots. Some adaptive methods are presented showing that an indirect noise-subspace estimation through a signal subspace estimation can be advantageous both in terms of convergence rate and computation complexity during each update. Some computer simulations examples showing performances are provided.
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