Autonomous navigation of platforms in complex environments has a key role in many applications. However, the environmental conditions could negatively affect the performance of electro-optical sensors. Hence, the idea of using radar odometry has been recently developed. However, it suffers from the presence of outliers in the scene as its electro-optical counterparts. This work presents a method to classify radar echoes as inliers or outliers for two-dimensional radar odometry, based on their range rate and bearing angle. The range rate and bearing angle are in fact combined to give a classification value, different for each target. At each acquisition time, the median of all classification values is computed. Since classification values of stationary targets, i.e. the inliers, cluster around the median, while moving targets, i.e. the outliers, exhibit larger distance from the median, stationary targets and moving targets can be separated. This is also useful for Sense-and-Avoid purposes. The method has been tested in simulated scenario to show effectiveness in detecting outliers and in real case scenario to demonstrate significant improvement in reconstruction of trajectory of platform, keeping the final error around 10% of the travelled distance. Further improvement is envisaged by integrating the method in the target tracking strategy
