This paper presents a novel biomimetic radar sensor for autonomous navigation. To accomplish this, we have drawn inspiration from the sensory mechanisms present in an echolocating mammal, the common big-eared bat ( Micronycteris microtis ). We demonstrate the correspondence in both the hardware, system model, and signal processing. To validate the performance of the sensor, we have developed a complementary control system based on subsumption architecture, which allows the system to autonomously navigate unknown environments. This architecture consist of separate behaviors with different levels of complexity, which are combined to produce the overall functionality of the system. We describe each behavior separately and examine their performance in real-world navigation experiments. For this purpose, the system is placed in two distinct office environments with the goal of achieving smooth and stable trajectories. Here, we can observe noticeable improvements when employing high-level behaviors. Furthermore, we utilize the data collected during the navigation experiments to perform simultaneous localization and mapping, using an algorithm developed in our earlier work. These results show a substantial improvement over the odometry. We attribute this to the fact that the system traverses stable and repetitive paths, which facilitates place recognition.