This work is concerned with the simulation of a frequency modulated continuous wave (FMCW) lidar (light detection and ranging) sensor in the context of automated vehicles. In order to save time and resources, automated driving functions are increasingly safeguarded in virtual environments, which requires respective simulation models of the vehicle’s perception sensors. In the following, the simulation of the FMCW lidar sensor is split into the three components: environment simulation, signal propagation model and signal processing model. The latter represents the main focus of this work and includes all processing steps that are executed within the sensor housing. An externally provided ray tracer realizes the signal propagation model. At the beginning, it is necessary to define the requirements for the FMCW lidar signal processing model. For this purpose, an existing approach is adapted and further developed. As a result, it is specified that the model must be able to accurately reproduce the beam pattern, the range measuring and the direct radial velocity measuring. Next up, a model development methodology is introduced which envisages an iterative step-by-step implementation of the three mentioned requirements with continuous verification and validation. Accordingly, within each iteration the current model is verified and validated after the implementation step. Subsequently, the proposed model development methodology is utilized to realize the signal processing model. The beam pattern, the radial range measuring and the radial velocity measuring are implemented one after the other. For each of the three, experimental reference measurements are conducted to obtain real sensor data. After that, the signal processing model is used within the sensor simulation to re-simulate the real measurements in a virtual environment. To enable this, additional reference sensors such as a laser range finder are employed during the real measurements to precisely determine the range to the target. For the velocity reference measurements, an Automotive Dynamic Motion Analyzer (ADMA) captures the position and velocity of the moving target vehicle. The uncertainties of these reference sensors are also taken into account by the simulation. The resulting simulated data is directly compared to the real reference data to validate the model after each implementation. At the end, a partly validated model is presented that fulfills the basic functions of an FMCW lidar sensor. The findings of this work show that the developed methodologies and approaches are suited for the development of a less complex perception sensor model. However, further improvements are necessary to enable more sophisticated and fully validated models.