A hybrid adaptive array minimizing the effects of the random weight vector errors

2003 ◽  
Author(s):  
S. De Lin ◽  
M. Barkat
Keyword(s):  
Weight Vector ◽  
Adaptive Array ◽  
Random Weight

scholarly journals Bounding basic characteristics of spatial epidemics with a new percolation model

2011 ◽  
Vol 43 (2) ◽  
pp. 335-347 ◽  
Author(s):  
Ronald Meester ◽  
Pieter Trapman
Keyword(s):  
Finite Graph ◽  
Weight Vector ◽  
Percolation Model ◽  
Locally Finite ◽  
Locally Finite Graph ◽  
Percolation Probability ◽  
Special Cases ◽  
Random Weight ◽  
Dependent Percolation ◽  
Basic Characteristics

We introduce a new 1-dependent percolation model to describe and analyze the spread of an epidemic on a general directed and locally finite graph. We assign a two-dimensional random weight vector to each vertex of the graph in such a way that the weights of different vertices are independent and identically distributed, but the two entries of the vector assigned to a vertex need not be independent. The probability for an edge to be open depends on the weights of its end vertices, but, conditionally on the weights, the states of the edges are independent of each other. In an epidemiological setting, the vertices of a graph represent the individuals in a (social) network and the edges represent the connections in the network. The weights assigned to an individual denote its (random) infectivity and susceptibility, respectively. We show that one can bound the percolation probability and the expected size of the cluster of vertices that can be reached by an open path starting at a given vertex from above by the corresponding quantities for independent bond percolation with a certain density; this generalizes a result of Kuulasmaa (1982). Many models in the literature are special cases of our general model.

scholarly journals Bounding basic characteristics of spatial epidemics with a new percolation model

2011 ◽  
Vol 43 (02) ◽  
pp. 335-347 ◽  
Author(s):  
Ronald Meester ◽  
Pieter Trapman
Keyword(s):  
Finite Graph ◽  
Weight Vector ◽  
Percolation Model ◽  
Locally Finite ◽  
Locally Finite Graph ◽  
Percolation Probability ◽  
Special Cases ◽  
Random Weight ◽  
Dependent Percolation ◽  
Basic Characteristics

We introduce a new 1-dependent percolation model to describe and analyze the spread of an epidemic on a general directed and locally finite graph. We assign a two-dimensional random weight vector to each vertex of the graph in such a way that the weights of different vertices are independent and identically distributed, but the two entries of the vector assigned to a vertex need not be independent. The probability for an edge to be open depends on the weights of its end vertices, but, conditionally on the weights, the states of the edges are independent of each other. In an epidemiological setting, the vertices of a graph represent the individuals in a (social) network and the edges represent the connections in the network. The weights assigned to an individual denote its (random) infectivity and susceptibility, respectively. We show that one can bound the percolation probability and the expected size of the cluster of vertices that can be reached by an open path starting at a given vertex from above by the corresponding quantities for independent bond percolation with a certain density; this generalizes a result of Kuulasmaa (1982). Many models in the literature are special cases of our general model.

scholarly journals A Study on Radar Target Detection using Space Time Adaptive Processing Algorithm and LCMV Algorithm

2021 ◽  
Vol 12 (5) ◽  
pp. 243-248
Author(s):  
Kwan-Hyeong Lee
Keyword(s):  
Correlation Matrix ◽  
Weight Vector ◽  
Direction Of Arrival Estimation ◽  
Adaptive Array ◽  
Radar Target ◽  
Interference Signal ◽  
Music Algorithm ◽  
Processing Power ◽  
Space Time Adaptive Processing ◽  
Optimal Weight Vector

In this paper, we study the directionof arrival estimation of the desired target in adaptive array MV algorithm to update the weight, and the optimized weight removes the interference signal. The target signal is estimated using the optimized weight vector and the high resolution the direction of arrival estimation MUSIC algorithm. We calculate the inverse of the correlation matrix using the QR method to reduce the processing power consumption of the optimized weight. The optimal weight vector is applied to the proposed algorithm to estimate the desired target direction from the output power spectrum. The performance of the proposed method is compared with the existing method by simulation. The experimental method estimates three targets from the antenna received signal. The existing method did not estimate the three desired targets at [-30o,-20, -10o]. The proposed method accurately estimates the desired three targets at [-30o,-20, -10o]. In the [-10o, 0, 10o] target estimation, the existing method reduces the estimated resolution of the target, but the proposed method accurately estimates the target. We proved that the proposed method in the simulation was superior to the existing method.

scholarly journals Broadband Frost Adaptive Array Antenna with a Farrow Delay Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Tao Dong ◽  
Qiushi Wang ◽  
Yunxiao Zhao ◽  
Lixia Ji ◽  
Hao Zeng
Keyword(s):  
Design Methodology ◽  
Weight Vector ◽  
Array Antenna ◽  
Adaptive Array ◽  
Adaptive Antenna ◽  
Filter Coefficient ◽  
Digital Beamforming ◽  
Farrow Structure ◽  
Direct Form ◽  
Time Update

In a broadband adaptive array antenna with a space-time filter, a delay filter is required before digital beamforming when the Frost algorithm is used to obtain the weight vector. In this paper, we propose a Farrow structure instead of a direct form FIR structure to implement the time delay filter since it can satisfy the demand for real-time update and is very suited for the FPGA platform. Furthermore, a new off-line algorithm to calculate the Farrow filter coefficient is presented if the filter coefficient is symmetric. Finally, simulations are presented to illustrate that the design methodology for the Farrow filter is correct. The simulations also prove that the Frost broadband adaptive antenna effectively mitigates the interferences with a Farrow filter.

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