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Future cellular networks that utilize millimeter wave signals provide newopportunities in positioning and situational awareness. Large bandwidthscombined with large antenna arrays provide unparalleled delay and angleresolution, allowing high accuracy localization but also building up a map ofthe environment. Even the most basic filter intended for simultaneouslocalization and mapping exhibits high computational overhead since the methodsrely on sigma point or particle-based approximations. In this paper, a firstorder Taylor series based Gaussian approximation of the filtering distributionis used and it is demonstrated that the developed extended Kalman probabilityhypothesis density filter is computationally very efficient. In addition, theresults imply that efficiency does not come with the expense of estimationaccuracy since the method nearly achieves the position error bound.

mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter