  Future cellular networks that utilize millimeter wave signals provide new
opportunities in positioning and situational awareness. Large bandwidths
combined with large antenna arrays provide unparalleled delay and angle
resolution, allowing high accuracy localization but also building up a map of
the environment. Even the most basic filter intended for simultaneous
localization and mapping exhibits high computational overhead since the methods
rely on sigma point or particle-based approximations. In this paper, a first
order Taylor series based Gaussian approximation of the filtering distribution
is used and it is demonstrated that the developed extended Kalman probability
hypothesis density filter is computationally very efficient. In addition, the
results imply that efficiency does not come with the expense of estimation
accuracy since the method nearly achieves the position error bound.

Signal Processing