This is a univariate distribution. [itex]\Theta[/itex] is uniformly distributed in [itex][0,2\pi][/itex]; [itex]X = \cos \Theta[/itex] and [itex]Y = \sin\Theta[/itex] can then be determined from that.
[tex]\begin{align*}<br />
P(X < x) &= \begin{cases}<br />
0 & x < -1 \\<br />
P(\arccos(x) < \Theta < 2\pi - \arccos(x)) & -1 \leq x \leq 1\\<br />
1 & x > 1 <br />
\end{cases} \\<br />
P(Y < y) &= \begin{cases}<br />
0 & y < -1 \\<br />
P(\pi + \arcsin(|y|) < \Theta < 2\pi - \arcsin(|y|)), & -1 \leq y < 0 \\<br />
P(0 < \Theta < \arcsin(y)) + P(\pi - \arcsin(y) < \Theta < 2\pi), & 0 \leq y \leq 1 \\<br />
1, & y > 1<br />
\end{cases}<br />
\end{align*}[/tex]
[itex]X[/itex] and [itex]Y[/itex] are not independent, but satisfy [itex]X^2 + Y^2 = 1[/itex].