11 research outputs found
Efficient Transmit Beamspace Design for Search-free Based DOA Estimation in MIMO Radar
In this paper, we address the problem of transmit beamspace design for
multiple-input multiple-output (MIMO) radar with colocated antennas in
application to direction-of-arrival (DOA) estimation. A new method for
designing the transmit beamspace matrix that enables the use of search-free DOA
estimation techniques at the receiver is introduced. The essence of the
proposed method is to design the transmit beamspace matrix based on minimizing
the difference between a desired transmit beampattern and the actual one under
the constraint of uniform power distribution across the transmit array
elements. The desired transmit beampattern can be of arbitrary shape and is
allowed to consist of one or more spatial sectors. The number of transmit
waveforms is even but otherwise arbitrary. To allow for simple search-free DOA
estimation algorithms at the receive array, the rotational invariance property
is established at the transmit array by imposing a specific structure on the
beamspace matrix. Semi-definite relaxation is used to transform the proposed
formulation into a convex problem that can be solved efficiently. We also
propose a spatial-division based design (SDD) by dividing the spatial domain
into several subsectors and assigning a subset of the transmit beams to each
subsector. The transmit beams associated with each subsector are designed
separately. Simulation results demonstrate the improvement in the DOA
estimation performance offered by using the proposed joint and SDD transmit
beamspace design methods as compared to the traditional MIMO radar technique.Comment: 32 pages, 10 figures, submitted to the IEEE Trans. Signal Processing
in May 201
Robust Design of Transmit Waveform and Receive Filter For Colocated MIMO Radar
We consider the problem of angle-robust joint transmit waveform and receive
filter design for colocated Multiple-Input Multiple-Output (MIMO) radar, in the
presence of signal-dependent interferences. The design problem is cast as a
max-min optimization problem to maximize the worst-case output
signal-to-interference-plus-noise-ratio (SINR) with respect to the unknown
angle of the target of interest. Based on rank-one relaxation and semi-definite
programming (SDP) representation of a nonnegative trigonometric polynomial, a
cyclic optimization algorithm is proposed to tackle this problem. The
effectiveness of the proposed method is illustrated via numerical examples.Comment: 6 pages, 13 figures, part of this work was submitted to IEEE Signal
Processing Letters; (short introduction; typos corrected; revised statement
in section III-B and IV; revised figure labels
Machine Learning para predecir el riesgo crediticio de un cliente en la Empresa FUTECH PERU S.A.C, 2022
En el presente trabajo de investigaci贸n se utiliz贸 los modelos de Machine
Learning para predecir el riesgo crediticio de los clientes de una empresa en
estudio, se utiliz贸 la metodolog铆a KDD, as铆 mismo herramientas como SPSS
statistic y SPSS Modeler para el uso de los modelos predictivo.
El objetivo de esta investigaci贸n es determinar en qu茅 porcentaje Machine
Learning permite predecir el riesgo crediticio de los clientes con precisi贸n,
sensibilidad y especificidad, con el fin de poder identificar a los clientes con
probabilidad de alto o bajo riesgo crediticio.
En esta investigaci贸n se utiliz贸 una poblaci贸n de 500 clientes, as铆 mismo se
us贸 la totalidad de la poblaci贸n como muestra. Por otro lado, el estudio es de
tipo aplicada, con un dise帽o de investigaci贸n experimental de tipo preexperimental de un solo grupo, ya que luego de aplicar Machine Learning se
podr谩 observar los resultados y realizar la medici贸n.
Como resultado en relaci贸n a los indicadores de precisi贸n, sensibilidad y
especificidad para los algoritmos Support Vector Machine,Random
Forest,Naibes Bayes, K Nearest Neighbor, Decision Tree, se valida que
Machine Learning permite predecir el riesgo crediticio de los clientes de la
empresa de estudio, as铆 mismo el algoritmo con mejores resultados para esta
casu铆stica fue Support Vector Machine con un valor de 99,8%