Question

Suppose a stationary radar illuminates a complex, but
stationary, target with a series of pulses.

Which probability density function is an appropriate choice to
represent the series of echo

power measurements? Why?

Answer #1

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If the echoing process of the radar is considered to be a
stochastic process, the echo signal and the associated power
measurements can be considered a random variable. If x and y are
the in-phase and quadrature components of the echo signals, then x
and y could be assumed to satisfy the Central Limit Theorem, and
hence the joint pdf of x and y can be considered a bi-variable
Gaussian Distribution. After some complicated algebra, it can be
shown that the distribution of the power of the echo signals models
the **Hoyt, Rayleigh and the Rice distributions.**

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Suppose the power series for a function f(x) has the interval of
convergence I=(a,b]. Which of the following statement must be true
about the interval of convergence for the power series \int
f(x)dx?
I=[a,b)
I=(a,b)
I=(a,b]
I=[a,b]
Can't be determined.

1) When we fit a model to data, which is typically larger?
a) Test Error b) Training Error
2) What are reasons why test error could be LESS than training
error? (Pick all that applies)
a) By chance, the test set has easier cases than the training
set.
b) The model is highly complex, so training error systematically
overestimates test error
c) The model is not very complex, so training error
systematically overestimates test error
3) Suppose we want to...

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