scholarly journals An Efficient Near-Field Localization Method of Coherently Distributed Strictly Non-circular Signals

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5176
Author(s):  
Meidong Kuang ◽  
Ling Wang ◽  
Yuexian Wang ◽  
Jian Xie
Keyword(s):  
Near Field ◽  
Integral Form ◽  
Approximate Expression ◽  
Probability Density Functions ◽  
Estimation Accuracy ◽  
Density Functions ◽  
Classical Probability ◽  
One Dimensional ◽  
Spatially Distributed ◽  
Field Localization

For the near-field localization of non-circular distributed signals with spacial probability density functions (PDF), a novel algorithm is proposed in this paper. The traditional algorithms dealing with the distributed source are only for the far-field sources, and they need two-dimensional (2D) search or omit the angular spread parameter. As a result, these algorithms are no longer inapplicable for near-filed localization. Hence the near-filed sources that obey a classical probability distribution are studied and the corresponding specific expressions are given, providing merits for the near-field signal localization. Additionally, non-circularity of the incident signal is taken into account in order to improve the estimation accuracy. For the steering vector of spatially distributed signals, we first give an approximate expression in a non-integral form, and it provides the possibility of separating the parameters to be estimated from the spatially discrete parameters of the signal. Next, based on the rank-reduced (RARE) algorithm, direction of arrival (DOA) and range can be obtained through two one-dimensional (1-D) searches separately, and thus the computational complexity of the proposed algorithm is reduced significantly, and improvements to estimation accuracy and identifiability are achieved, compared with other existing algorithms. Finally, the effectiveness of the algorithm is verified by simulation.

The Fast Diagonal-Matrix-Weight IMM Algorithm for Target Tracking

2012 ◽  
Vol 461 ◽  
pp. 132-137
Author(s):  
Yang Fu ◽  
Ming Wei ◽  
Hai Chuan Zhang ◽  
Liang Gao
Keyword(s):  
Stochastic Process ◽  
Computational Complexity ◽  
Target Tracking ◽  
Real Time ◽  
Probability Density Functions ◽  
Diagonal Matrix ◽  
Estimation Accuracy ◽  
Density Functions ◽  
Alternative Algorithm ◽  
Real Time Application

The diagonal-matrix-weight IMM (DIMM) algorithm can solve the IMM algorithm confusions of probability density functions (PDFs) and probability masses of stochastic process. Combingandfilter，the Fast-IMM algorithm has a better performance both in accuracy and reducing computational complexity. In order to improve the estimation accuracy and computational complexity，we apply Fast-IMM method to DIMM algorithm. Therefore，A new method, Fast diagonal-matrix-weight IMM (fast-DIMM) algorithm, is proposed in this paper to heighten the real-time application of DIMM algorithm. Simulations indicate that the proposed fast-DIMM algorithm is a competitive alternative algorithm to the IMM algorithm in real time application

First-passage-time densities for time-non-homogeneous diffusion processes

1997 ◽  
Vol 34 (3) ◽  
pp. 623-631 ◽  
Author(s):  
R. Gutiérrez ◽  
L. M. Ricciardi ◽  
P. Román ◽  
F. Torres
Keyword(s):  
Diffusion Processes ◽  
First Passage Time ◽  
Passage Time ◽  
Continuous Functions ◽  
Probability Density Functions ◽  
Density Functions ◽  
First Passage ◽  
One Dimensional ◽  
Dimensional Diffusion ◽  
Natural Boundaries

In this paper we study a Volterra integral equation of the second kind, including two arbitrary continuous functions, in order to determine first-passage-time probability density functions through time-dependent boundaries for time-non-homogeneous one-dimensional diffusion processes with natural boundaries. These results generalize those which were obtained for time-homogeneous diffusion processes by Giorno et al. [3], and for some particular classes of time-non-homogeneous diffusion processes by Gutiérrez et al. [4], [5].

scholarly journals Small-Scale and Mesoscale Variability of Scalars in Cloudy Boundary Layers: One-Dimensional Probability Density Functions

2001 ◽  
Vol 58 (14) ◽  
pp. 1978-1994 ◽  
Author(s):  
Vincent E. Larson ◽  
Robert Wood ◽  
Paul R. Field ◽  
Jean-Christophe Golaz ◽  
Thomas H. Vonder Haar ◽  
...  
Keyword(s):  
Probability Density ◽  
Boundary Layers ◽  
Probability Density Functions ◽  
Small Scale ◽  
Density Functions ◽  
Mesoscale Variability ◽  
One Dimensional

scholarly journals Experimental spatial rogue patterns in an optical feedback system

2010 ◽  
Vol 10 (12) ◽  
pp. 2727-2732 ◽  
Author(s):  
V. Odent ◽  
M. Taki ◽  
E. Louvergneaux
Keyword(s):  
Pattern Formation ◽  
Optical Feedback ◽  
Modulational Instability ◽  
Feedback System ◽  
Probability Density Functions ◽  
Kerr Medium ◽  
Density Functions ◽  
Long Tail ◽  
One Dimensional ◽  
Highly Nonlinear

Abstract. We study pattern formation in an optical system composed of a Kerr medium subjected to optical feedback but in a regime very far from the modulational instability threshold. In this highly nonlinear regime, the dynamics is turbulent and the associated one-dimensional patterns depict rare and intense localized optical peaks. We analyse numerically and experimentally the statistics and features of these intense optical peaks and show that their probability density functions (PDF) have a long tail indicating the occurrence of rogue events.

scholarly journals A Novel Sparse Array for Localization of Mixed Near-Field and Far-Field Sources

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yinsheng Wang ◽  
WeiJia Cui ◽  
Yuxi Du ◽  
Bin Ba ◽  
Fengtong Mei
Keyword(s):  
Degrees Of Freedom ◽  
Near Field ◽  
Parameters Estimation ◽  
Estimation Accuracy ◽  
Far Field ◽  
Computation Complexity ◽  
Field Source ◽  
One Dimensional ◽  
Range Parameter ◽  
Array Aperture

As we all know, nested array can obtain a larger array aperture and more degrees of freedom using fewer sensors. In this study, we not only designed an enhanced symmetric nested array (ESNA), which achieved more consecutive lags and more unique lags compared with a generalized nested array but also developed a special cumulant matrix, in the case of a given number of sensors, which can automatically generate the largest consecutive lags of the array. First, the direction-of-arrivals (DOAs) of mixed sources are estimated using the special cumulant matrix. Then, we can estimate the range of the near-field source in the mixed source using a one-dimensional spectral search through estimated DOAs, and in the mixed sources, the near-field and far-field sources are classified by bringing in the range parameter. The largest consecutive lags and composition method of ESNA are also given, under a given number of sensors.Our algorithm has moderate computation complexity, which provides a higher resolution and improves the parameters’ estimation accuracy. Numerical simulation results demonstrate that the proposed array showed an outstanding performance under estimation accuracy and resolution ability for both DOA and range estimation compared with existing arrays of the same physical array sensors.

First-passage-time densities for time-non-homogeneous diffusion processes

1997 ◽  
Vol 34 (03) ◽  
pp. 623-631 ◽  
Author(s):  
R. Gutiérrez ◽  
L. M. Ricciardi ◽  
P. Román ◽  
F. Torres
Keyword(s):  
Diffusion Processes ◽  
First Passage Time ◽  
Passage Time ◽  
Continuous Functions ◽  
Probability Density Functions ◽  
Density Functions ◽  
First Passage ◽  
One Dimensional ◽  
Dimensional Diffusion ◽  
Natural Boundaries

In this paper we study a Volterra integral equation of the second kind, including two arbitrary continuous functions, in order to determine first-passage-time probability density functions through time-dependent boundaries for time-non-homogeneous one-dimensional diffusion processes with natural boundaries. These results generalize those which were obtained for time-homogeneous diffusion processes by Giorno et al. [3], and for some particular classes of time-non-homogeneous diffusion processes by Gutiérrez et al. [4], [5].

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