A model producing behavior mimicking that of a homing desert ant while approaching the nest along a habitual route is presented. The model combines two strategies that interact with each other: local vector navigation and landmark guidance with an average landmark vector. As a multi-segment route with several waypoints is traversed, local vector navigation is mainly used when leaving a waypoint, landmark guidance is mostly used when approaching a waypoint, and a weighted interplay of the two is used in between waypoints. The model comprises a spiking neural network that is developed based on the principles of the Neural Engineering Framework. Its performance is demonstrated with a simulated robot in a virtual environment, which is shown to successfully navigate to the final waypoint in different scenes.